# Reaction Kinetics in Food Systems

Authored by: Ricardo Villota , James G. Hawkes

# Handbook of Food Engineering

Print publication date:  January  2019
Online publication date:  December  2018

Print ISBN: 9781466563124
eBook ISBN: 9780429449734

10.1201/9780429449734-3

#### Abstract

Since the publication of the first edition of this chapter in 1992 and the second in 2007, there has been a major shift in focus within the food industry, in response to both new consumer dynamics and technological advances in processing, along with demographic and economic societal changes. In recent years, there has been a continually incr easing consumer demand for not only high-quality foods with nutritional benefits, but also products that would substantiate “clean labels” (i.e., “chemical-, hormone-, GMO-free”, “organic”, “natural”, etc.) and products that would address changes in life-style logistics (e.g., fresh and refrigerated snacking occasions and complete meals, single portions, etc.) (Sloan, 2015, 2017). In addition, advances in more efficient and versatile methods of food processing and preservation have occurred exponentially over the past few decades, such that food scientists also need to adjust their focus on new and improved models to aid the development process for new product generation. With the advances in scientific findings concerning stability and bioavailability of vitamins and various pigments/nutrients, and updated analyses, these models may be impacted. These factors all play a part in new and sometimes complex challenges in predicting the best approaches to producing high quality food. However, there will still be constants; the quality of processed foods will still depend upon the integrity of the raw materials, changes occurring during processing and subsequent storage that may result in potential losses and decreased bioavailability, and above all, microbiological safety standards.

#### 3.1  Introduction

Since the publication of the first edition of this chapter in 1992 and the second in 2007, there has been a major shift in focus within the food industry, in response to both new consumer dynamics and technological advances in processing, along with demographic and economic societal changes. In recent years, there has been a continually incr easing consumer demand for not only high-quality foods with nutritional benefits, but also products that would substantiate “clean labels” (i.e., “chemical-, hormone-, GMO-free”, “organic”, “natural”, etc.) and products that would address changes in life-style logistics (e.g., fresh and refrigerated snacking occasions and complete meals, single portions, etc.) (Sloan, 2015, 2017). In addition, advances in more efficient and versatile methods of food processing and preservation have occurred exponentially over the past few decades, such that food scientists also need to adjust their focus on new and improved models to aid the development process for new product generation. With the advances in scientific findings concerning stability and bioavailability of vitamins and various pigments/nutrients, and updated analyses, these models may be impacted. These factors all play a part in new and sometimes complex challenges in predicting the best approaches to producing high quality food. However, there will still be constants; the quality of processed foods will still depend upon the integrity of the raw materials, changes occurring during processing and subsequent storage that may result in potential losses and decreased bioavailability, and above all, microbiological safety standards.

A particular emphasis on nutraceuticals and fortified “high-energy” foods has been observed (Sloan, 1999, 2005; Molyneau and Lee, 1998; Giese, 1995; Ohr, 2017). Trends also indicate that fortification of food products has increased tremendously in the past years in multiple categories, including beverages, meals, biscuits, etc. Not only nutritional quality is important to the food processor, but also the general appearance of the food, its flavor, color and texture, factors which are highly dependent upon the target consumer. It is, therefore, of critical importance to the food industry to minimize losses of quality in food products during processing and subsequent storage. It is through the development of mathematical models to predict behavior of food components and optimization of processes for maximum product quality that continued advancement can be achieved. To obtain these goals, extensive information is needed on the rates of destruction of quality parameters and their dependence on variables such as temperature, pH, light, oxygen, and moisture content. A food engineer can then develop new processing techniques to achieve optimum product quality based on an understanding of reaction rates and mechanisms of destruction of individual quality factors combined with heat and mass transfer information. The need for this type of information is becoming critically important to the food industry with increased required nutritional labeling practices for food products (IDFA, 2016).

Chemical kinetics encompasses the study of the rates at which chemical reactions proceed. The area of kinetics in food systems has received a great deal of attention in past years, primarily due to efforts to optimize or at least maximize the quality of food products during processing and storage. Moreover, a good understanding of reaction kinetics can provide a better idea of how to formulate or fortify food products in order to preserve the existing nutrients or components in a food system or, on the other hand, minimize the appearance of undesirable breakdown products. Unfortunately, limited kinetic information is available at present for food systems or ingredients that would facilitate the development of food products with improved stability or the optimization of processing conditions. A major consideration, however, is that indirectly some of the information available may be used to predict kinetic trends and thus establish major guidelines in formulation, storage, and process conditions. Thus, it is within the scope of this chapter to (a) present a general discussion on general kinetics, outlining some of the fundamental principles, (b) provide information on a variety of food systems, indicating their reactivity and reported kinetic behavior, and (c) provide an understanding of current changes resulting from external influences, including updated analytical methodologies and technological advances. It is considered that a better understanding of kinetics in food systems will facilitate the development of a more complete and sound database.

It should be emphasized that the level of accuracy of kinetic data is dependent upon its final application. For instance, if mathematical models are developed to optimize retention of a particular attribute in a given food system, it is evident that more sophisticated techniques are required than for those where the measurement is used for routine quality control. This is an area where careful judgement needs to be exerted. In fact, many of the major drawbacks existing in current kinetic data have originated in the analytical techniques selected to compile kinetic information. Not only sensitivity but also selectivity in the assay procedure needs to be taken into account when monitoring individual compounds in highly complex food systems.

There are three main areas of concern when dealing with reaction kinetics: (1) the stoichiometry, (2) the order and rate of reaction and (3) the mechanism. For simple reactions, the stoichiometry is probably the first consideration. Once this is clarified or elucidated, the mechanisms involved in the reaction are determined. It should be mentioned that based on kinetic data, our idea of the stoichiometry may change. In highly complex reactions, as in the case of many reactions occurring in food systems, a great deal of overlap exists among the three aforementioned areas. Thus, it is of critical importance to take a close and analytical look at the overall system to be able to better characterize reaction pathways.

#### 3.2.1  Order of Reaction

The determination of the order of the reaction is of particular significance to the area of kinetics. Understanding of the mechanisms involved in the reaction is important to properly obtain and report meaningful kinetic information, select reaction conditions leading to a desired end product, and/or minimize the appearance of undesirable compounds. Unfortunately, very seldom has effort been dedicated to clearly understanding the mechanisms involved in the reaction in complex systems, as in the cases with food and biological materials. Most information available has been oversimplified. In fact, most investigators have often tried to adapt fairly simple zero- or first-order reaction kinetics to complex situations without trying to understand the actual pathways involved. Although, from a practical point of view it is clear that simplifications may be taken, applicability of the information may be restricted only to the conditions encompassed by the experimental design, and thus one may incorrectly predict trends by directly extrapolating reported information.

The reaction pathway, also called reaction mechanism, may be determined through proper experimentation. A chemical reaction may take place in a single step, as in the case of elementary reactions, or in a sequence of steps, as would be the case of most reactions occurring in food systems. Conditions such as temperature, oxygen availability, pressure, initial concentration, and the overall composition of the system may affect the mechanism of the reaction. For instance, the degradation of folic acid and ascorbic acid can be affected by the presence of oxygen, resulting in modifica tion of the reaction pathway and thus the type of breakdown products. Moreover, the rate at which these parent compounds disappear may be highly influenced by the presence and concentration of the breakdown products generated. It is true that the level of complexity involved in these reactions may be of such magnitude that a complete understanding of the mechanism of deterioration cannot always be easily determined or identified or, even more, may hinder the development of simple techniques to rapidly evaluate the stability of a given system. Nevertheless, it should be stressed that more reliable information is obtained when understanding of the reaction pathways is achieved.

A basic approach for the determination of the reaction order for a simple reaction, taking into consideration its initial rate is as follows:

3.1 $− d C d t = k C n$

which after taking the natural logarithm on both sides of the equation results in:

3.2 $ln ( − d C d t ) = ln k + n ln C$

where C is the concentration; k is the reaction rate constant; n is the order of the reaction; and t is time.

According to this approach, a plot of the ln (−dC/dt) vs. ln C will give a straight line, whose slope corresponds to the reaction order (n), as shown in Figure 3.1. Although the intercept should correspond to the reaction rate constant (k), it is normally considered that, for the sake of accuracy, this would not be the preferred approach for its estimation. Rather, once the reaction order has been determined, the rate constant can be calculated by applying the corresponding equation for that reaction order.

Figure 3.1   Graphical representation for determining reaction order (n) of a reaction.

The method of least squares can also be used to determine the order of the reaction with respect to the reactants and products involved in the reaction. For instance, for a given reaction A + BP, where A and B are the reactants and P is the product, the reaction rate (r) can be defined by an equation such that:

3.3 $− r = k [ A ] a [ B ] b$

where [A] and [B] are the respective concentrations, and a and b are the respective reaction orders of the reactants A and B. By taking the natural log on each side, the equation becomes:

3.4 $ln ( − r ) = ln k + a ln [ A ] + b ln [ B ]$

which is of the form:

3.5 $y = a 0 + a x 1 + b x 2$

where a 0, a and b are constants. Thus, by the least squares approach, the coefficients a and b, corresponding to the order of the reaction, can be determined.

For the particular case of complex reactions, such as in the case of lipid oxidation, many investigators have tried to apply simple reaction kinetics to describe their behavior. In this particular situation it is obvious that a clear understanding of the overall chain reaction including initiation, propagation, and termination stages may reach levels of complexity unwanted from a practical point of view and extremely difficult to express in simple mathematical terms. Complex reaction kinetics will be discussed in a subsequent section.

#### 3.2.2  Reaction Rate

When dealing with food systems, a common approach to report reaction rates is as the change in concentration of a reactant as a function of time. The reaction rate thus provides a measurement of the reactivity and stability of a given system. A number of variables have been observed to influence the reaction rate. Major factors include: (a) concentration of reactants, products, and catalysts; (b) environmental factors such as temperature, pressure, and oxygen availability; (c) wavelength and intensity of light; and (d) physicochemical properties such as viscosity, ionic strength, and conductivity. Depending upon the type of reaction and the components, other factors will also be influential in controlling reaction kinetics.

Although traditionally one can apply reaction kinetics to monitor chemical changes occurring in a system, other physicochemical changes may also be described using a kinetic approach. For instance, textural and color changes occurring in food systems can be described using reaction rates. It is obvious that the numbers obtained represent the final effect ca used by other complex reaction mechanisms leading to an overall result. For instance, color changes in a product containing carotenoids may be an indication of the stability of the system, and in particular, stability of the carotenoids as related to environmental conditions. Another example is the textural changes in starch-based systems as a function of time, which may be the result of starch retrogradation mechanisms as well as lipid-amylose interactions as influenced by environmental conditions.

A key issue becomes how to properly measure the changes occurring in a system as influenced by different factors. Changes may be measured by monitoring, for instance, the disappearance of a compound, the appearance of a breakdown product, or changes in the physicochemical properties of the system such as in thermal conductivity. Colored species may be monitored through their appearance and disappearance by using spectrophotometric techniques. Depending very much on the final application of the kinetic data, one may need to actually monitor the reaction rate in such a way that the reaction does not proceed to any significant extent during the analytical test. Quenching of the reaction may be accomplished in many different ways, such as by lowering the temperature of the system or by addition of the reaction mixture to a system that provides stability. For most cases in food systems, the rates of chemical reactions that proceed slowly can be easily studied through convenient methods. Since nutrient retention is of primary concern in food systems subjected to deleterious conditions, a great deal of attention has been given to the study of vitamin degradation. A number of different techniques have been developed for their analyses, a brief summary of which is presented in Table 3.1. Although this table has been updated with the focus on more reliable instrumental techniques, many of these, however, still suffer from the lack of differentiation of intermediate compounds that may form, thus resulting in erroneous conclusions. For instance, the transformation of trans-carotenoids into their cis-form is difficult to detect unless a very specific technique is utilized. Since isomerization causes losses of the provitamin activity, it is critical to be able to identify the different isomers. Most techniques utilized thus far to monitor kinetics of carotenoid degradation have not taken this factor into account. Some of the research presented in this chapter has made great progress in this area, however. A great deal of effort has recently been directed towards more conclusive methods such as high-performance liquid chromatography (HPLC) and supercritical fluid chromatography, which afford separation of the individual compounds, thus enabling the collection of proper kinetic information to elucidate mechanisms of deterioration and characterize kinetic parameters. Other methods include the use of liquid chromatography in combination with mass spectrometry (LC-MS) for the quantitative determination of 5-methyltetrahydrofolic and folic acids (Thomas et al., 2003; Pawlosky and Flanagan, 2001) and capillary zone electrophoresis for the separation of L-ascorbic and D-isoascorbic acids (Liao et al., 2000). Eitenmiller et al. (2008) have reviewed in detail many of the instrumental techniques for vitamin analysis, including extraction procedures, instrumentation, and detector systems currently in use as applied to foods and pharmaceuticals.

### Table 3.1   Methods Used for Vitamin Assays

Instrumental Analysis

Vitamin

Sample Preparation

Separation

Detection

Microbiological Assay

Other Methods

Vitamin C

Acid hydrolysis

HPLC; IEC; MECC

Fluorescence (Ex λ = 335–365/Em λ = 426–440); UV absorption (λ = 254–265 nm)

Indophenol Method (using Tillman’s Reagent – titration or photometric); monitors color changes due to 2,6-dichloroindophenol reduction;

2,4-dinitrophenylhydrazine (DNPH), monitors total ascorbic acid (AA) based on oxidation of AA to dehydroascorbic acid (DHAA) followed by coupling with DNPH to form red-colored osazones;

Microfluorometric (based on reaction of DHAA with o-phenylenediamine);

Polarographic;

Oxidation-Reduction Methods (with iodine, bromine, iron, copper, mercury, or selenious acid);

Capillary Zone Electrophoresis (separation of L- and D-isoascorbic acids).

Vitamin B1 (Thiamine)

Acid hydrolysis; enzymatic hydrolysis

IEC; GLC; HPLC

Fluorescence (Ex λ = 360–378/Em λ = 425–435), UV absorption (UV/VIS λ = 254–280 nm), FID

Lactobaccillus viridescens (12706): [intact thiamine-specific]

Fluorometric Thiochrome (alkaline oxidation of thiamine to fluorescent thiochrome);

Animal (rat, pigeon, chick).

Vitamin B2 (Riboflavin)

Acid hydrolysis

HPLC, MECC

Fluorescence (Ex λ = 370–378/Em λ = 425–565), UV absorption (UV/VIS λ = 254–280 nm)

Lactobaccilus casei subsp. rhamnsus (7469); Enterococcus fecalis (10100);

Tetrahymena pyriformis [B2-specific]; Lactobacillus casei [non-specific]

Polarographic;

Animal (rat, chick)

Niacin

Alkaline hydrolysis

HPLC, IEC, GLC, MECC

Fluorescence (Ex λ = 260–322/Em λ = 380–542); UV absorption (Ex λ = 260–322/Em λ = 380–542), FID

Lactobaccillus plantarum (8014); Luconostoc mesenteroides (9135); Lactobacillus plantarum

Radiometric Assay; Spectrophotometric: based on König reaction (niacin + cyanogen bromide pyridinium compound + aromatic amine glutaconic dialdehyde derivative [colored]); niacinimide in potassium dihydrogen phosphate + cyanogen bromide + barbituric acid purple color;

Metabolite Measurement: N1-methyl nicotinamide (NMN) and 6-pyridone of N1-methyl nicotinamide; NMN + ketones in alkali solution green fluorescent compound; NMN + ketones in alkali solution green fluorescent compound;

Animal (dog, chick, weanling rat).

Vitamin B6

Acid hydrolysis

HPLC, IEC, GLC, MECC

Fluorescence (Ex λ = 290/Em λ = 395), UV absorption (UV/VIS λ = 254–280 nm), FID

Saccharomyces carlbergensis (9080);

Saccharomyces uvarum

Animal (chick and rat growth).

Pantothenic acid

Alkaline hydrolysis; enzymatic hydrolysis

GLC

FID; UHPLC

Lactobaccillus plantarum (8014);

Saccharomyces carlsbergensis,

S. cerivisiae

Fluorometric: alkaline hydrolysis + o-phthalaldehyde + 2-mercaptoethanol in boric acid solution fluorogenic compound;

Folate

Enzymatic hydrolysis

IEC; HPLC

UV/VIS detector (λ = 270–283/550 nm); post derivitization fluorescence (Ex λ = 360–378/Em λ = 425–435)

Lactobaccilus casei subsp. Rhamnus (7469);

Enteroccus hirae (8043);

Pediococcus cerivisiae

Radiometric Assay (competitive binding radioassay using liquid or dry skim milk as binder);

Electrophoretic (polyglutamyl chain-length determination).

Biotin

Acid hydrolysis; enzymatic hydrolysis

Fluorescence detector (Ex λ = 340–490/Em λ = 395–520)

Lactobaccillus plantarum (8014)

Animal (chick, rat);

Radiometric (not for general use); Fluorometric (for non-biological materials).

Vitamin B12

Direct solvent extraction

MECC

Fluorescence (Ex λ = 250–275/Em λ = 305–312); UV/VIS absorption (λ = 254/365/546 nm)

Lactobacillus delbrueckii, subsp. lactis (4797);

Lactobacillus leichmennii

Radioassays (competitive inhibition assay based on isotope-dilution principle);

Protozoan assays (Euglena gracilis, Ochromonas malhamensis);

Stable isotope LC-MS method;

Polarographic; Animal (chick and rat).

Vitamin A

Direct solvent extraction; alkaline hydrolysis (saponification), extraction into organic solvents

UV/VIS

UV/VIS:

carotene: 445–455

retinol: 325–340

retinyl palmitate: 452

xanthophylls: 436

Supercritical Fluid Chromatography (direct measurement).

Vitamin D

Alkaline hydrolysis with extraction into organic solvents

HPLC; GLC

UV absorption (254–265 nm)

Colorimetric (Vit. D + SbCl3 in ethylene dichloride pink complex);

Saponification followed by EtOH extraction, purification, LC/MS;

Animal (rat [“line test”]; chick [bone ash]).

Vitamin E

Alkaline hydrolysis with extraction into organic solvents

HPLC

Fluorescence (Ex λ = 290–296/Em λ = 320–330); UV absorption (290 nm)

Colorimetric (based on Emmerie & Engel’s Reaction: tocopherols + bathophenanthroline + FeCl3 + orthophosphoric acid pink-colored complex);

GLC (native or trimethylsilyl derivatives);

Animal (rat, chick, duckling).

Vitamin K

Direct solvent extraction; supercritical fluid extraction; enzymatic hydrolysis

HPLC; GLC

UV absorption (247–256 nm); fluorescence (Ex λ = 243–325/Em λ = 418–430)

Reduction-Oxidation Method; Animal (chick prothrombin time determinations); Ethylcyanoacetate Method; 2,4-Dinitrophenylhydrazine Method.

FID: Flame ionization detector; Em λEmmission wavelength; Ex λExcitation wavelength; GLC: Gas–liquid chromatography; IEC: Ion exchange chromatography; HPLC: High pressure liquid chromatography; MECC: Micellar electrokinetic capillary electrophoresis; UHPLC: Ultra-high pressure liquid chromatography.

Since reactions in food systems are normally complex and a combination of several elementary steps, additional basic information may be necessary to postulate reaction rate expressions. Identification of intermediates and previous knowledge of rate equations to fit data for other systems may provide assistance in properly characterizing a given reaction. Additional factors that may affect reaction rates are the type of energy and/or conditions surrounding the process to which the food is subjected such as those associated with nontraditional techniques including high pressure, irradiation, and ohmic and pulsed electric field processing.

In the following paragraphs, a short summary of the mathematical description of concentration vs. time for single irreversible and complex reactions will be presented. The most commonly found reactions in food and biological systems are the zero-, first-, and second-order reactions.

#### 3.2.3.1  Zero-Order Reactions

In zero-order reactions, the rate is independent of the concentration. This may occur in two different situations: (a) when intrinsically the reaction rate is independent of the concentration of reactants and (b) when the concentration of the reacting compound is so large that the overall reaction rate appears to be independent of its concentration. Many catalyzed reactions fall in the category of zero-order reactions with respect to the reactants. On the other hand, the reaction rate may depend upon the catalyst concentration or other factors unrelated to the concentration of the compound under investigation.

Thus, for a zero-order reaction at constant density, the overall expression would be as follows:

3.6 $− d C d t = k 0$

where C is the concentration; t is time; k 0 is the zero-order reaction rate constant, which by integration would result in:

3.7 $C 0 − C = k 0 t$

where C is the concentration at time (t) and C 0 is the initial concentration.

According to this mathematical expression, a distinguishing feature for this type of reaction is a linear decrease in concentration as a function of time as illustrated in Figure 3.2.

Figure 3.2   Graphical representation for the determination of a zero-order rate constant (k 0).

Typical reactions that have been represented by zero-order reactions include some of the autooxidation and non-enzymatic browning reactions. It is clear that zero-order reactions do not appear to occur as frequently in food systems as other reaction orders. In most cases, it is evident that the most common situation for this type of reaction is when the concentration of the reactants is so large that the system appears to be independent of concentration.

#### 3.2.3.2  First-Order Reactions

A large number of reactions occurring in food systems appear to follow a first-order reaction. A mathematical expression for this behavior would be as follows:

3.8 $− d C d t = k 1 C$

where k 1 is the first-order reaction rate constant. By integration, this equation becomes:

3.9 $− ln ( C C 0 ) = k 1 t$

Thus, according to this mathematical expression ln C vs. time will be a linear function where the slope corresponds to –k 1 as shown in Figure 3.3. The half-life (t 1/2) is given by:

Figure 3.3   Graphical representation for the determination of a first-order rate constant (k 1).

3.10 $k 1 t 1 / 2 = − ln ( 1 / 2 )$
3.11 $t 1 / 2 = ln 2 / k 1$

The mathematical expressions above clearly indicate that the half-life and the reaction rate for a true first-order reaction are independent of the initial concentration. However, although in a number of systems this may be the case, formulated products will not necessarily follow true first-order reaction kinetics, but rather a pseudo-first-order reaction. In fact, in formulated systems the presence of breakdown products may strongly influence the order of the reaction; however, the reaction may follow apparent first-order kinetics for only a given value of initial concentration. To determine if a given reaction does indeed follow a pseudo-first-order kinetics, conditions for the kinetic study can be chosen to follow the technique of flooding. Through this approach, all but one of the concentrations are set sufficiently high that, compared to the one reagent present at lower concentration, the others are effectively constant during the time of the experiment. Since only one of the concentrations changes appreciably during the run, the effective kinetic order is reduced to the reaction order with respect to that one substance. If the order of the reaction is determined to be one, the reaction is said to follow a pseudo-first-order reaction. The degradation of ascorbic acid, for instance, has been primarily found to follow first-order kinetics in food systems. On the contrary, degradation of ascorbic acid in model systems has frequently been found to follow pseudo-first-order kinetics. It appears that the presence of breakdown products modifies the kinetics of deterioration of ascorbic acid and thus its initial concentration will influence its rate of degradation.

#### 3.2.3.3  Second-Order Reactions

Two types of second-order reaction kinetics are of importance:

Type I: A + AP

3.12 $− d C A d t = k 2 C A 2$

and Type II: A + BP

3.13 $− d C A d t = k 2 C A C B$

where C A is the concentration of reactant species (A) at time (t), C B is the concentration of reactant species (B) at time (t), and k 2 is the second-order reaction rate constant. For Type I, the integrated kinetic expression yields:

3.14 $1 C A − 1 C A 0 − k 2 t$

which in terms of the half-life becomes:

3.15 $t 1 / 2 = 1 k 2 C A 0$

For Type II, the integrated form yields:

3.16 $k 2 t = 1 C A 0 − C B 0 ln ( C B 0 C A C A 0 C B )$

where C A0 and C B0 are the respective initial concentrations and C A and C B are the respective concentrations at time (t). It should be stressed, however, that Type II reactions do not necessarily have to follow a second-order reaction. For instance, for the particular case where component A is present in large amounts as compared with component B, the reaction may follow first-order kinetics with respect to B. A typical plot of second order kinetics is presented in Figure 3.4.

Figure 3.4   Graphical representation for the determination of a second-order rate constant (k 2) for a type I reaction.

#### 3.2.4  Nonelementary Reactions

To characterize the kinetics of nonelementary reactions, one can assume a series of individual elementary reactions taking place. In these reactions, intermediates may not be observed or quantitated, either because they are present in very small amounts or because they are unstable. Such reactions would fall under three main categories: (1) consecutive or series reactions, (2) reversible or opposing reactions, which attain a finite equilibrium, and (3) parallel or competitive reactions. The types of intermediates postulated may fall in any one of the following categories, namely, (a) free radicals, (b) ions and polar substances, (c) molecules, and (d) transition complexes (chain reactions and non-chain reactions). The following are examples of the various mechanisms proposed.

#### 3.2.4.1  Types of Intermediates

Free atoms or fragments of stable molecules containing one or more unpaired electrons are called free radicals. For these compounds, a standard convention is to designate the unpaired electron by a dot (·). Some of these radicals are stable as in the case of ascorbic acid, where its hydroxyl on the C-3 readily ionizes (pK1 = 4.04 at 25°C) and may undergo degradation to dehydroascorbic acid in the presence of oxygen. Other free radicals are unstable as in the case of lipid oxidation. For instance, when a hydroperoxide decomposes to form RO radicals, such compounds can participate in other reactions, thus being capable of continuing the chain propagation process and forming several products. Hydroxy acids, keto acids, and aldehydes have been isolated from oxidizing lipid systems. The formation of protein free radicals in systems undergoing lipid oxidation is another example of reactions involving unstable radicals. Free radicals may form on the α-carbons of the proteins, while cysteil radicals may form in proteins containing cysteine or cystine (Karel, 1973).

#### 3.2.4.1.2  Ion and Polar Substances

Electrically charged atoms, molecules, or fragments of molecules, such as Na+, $NH 4 +$ , I and $NO 2 −$ , are called ions, which may serve as reactive intermediates in a variety of reactions.

#### 3.2.4.1.3  Molecules

In reactions such as: ABC, where compound B is highly reactive or its concentration in a given reaction mixture is very small, such compound B acts as an intermediate.

#### 3.2.4.1.4  Transition Complexes

Chain-Reactions:

Catalyzed reactions may fall in the category of chain reactions. In these reactions an intermediate is formed in the initiation step. Such an intermediate then interacts with the reactant to obtain a product and more intermediate to participate in the reaction. A key consideration is that the intermediate may catalyze a series of reactions before being destroyed.

A classic example for the case of chain reactions is the degradation of β-carotene, an autooxidation reaction involving three main periods: (1) induction (formation of free radicals), (2) propagation (free radical-chain reactions), and (3) termination (formation of non-radical products). According to this pathway, the reaction may be expressed as follows:

$2 RH → k i 2 R • R • + O 2 ⇄ k RO 2 • } ( 1 ) induction RO 2 • + RH → k p R • + ROOH R • + R ′ H → k ′ p R • + RH } ( 2 ) propagation R • + RO 2 • → k t products R • + R • → k ′ t products } ( 3 ) termination$

Although different approaches have been taken to mathematically describe the kinetic behavior of β-carotene, a simplified free-radical recombination has been suggested by a number of authors (Alekseev et al., 1968; Gagarina et al.; 1970; Finkel’shtein et al., 1973, 1974).

According to this approach, the rate of consumption of a hydrocarbon, i.e. carotenes, in a chain process with a second-order chain termination can be described by:

3.17 $− d C d t = a C w i$

By replacing the value corresponding to w i , the rate of formation of free radicals, the rate of consumption of the hydrocarbon can be expressed as being:

3.18 $− d C d t = a C b 0 C + b ( C 0 − C )$

where C is the carotenoid concentration at time (t); C 0 is the initial carotenoid concentration; a is the constant derived from Equation 3.19; b is the initiation rate constant of the products; b 0 is the initiation rate constant of unoxidized carotenoids; and t is time.

The initiation rate is considered to be the sum of the initiation rates of radicals formed by the unreacted carotene and by the intermediate products. The value of the constant “a” can be represented by:

3.19 $a = k p k t ⋅ k K S P O 2$

where k p , k t , and k are rate constants as previously shown; K s is the solubility coefficient of oxygen in carotenoids; and PO2 is the partial pressure of oxygen. Integrating Equation (3.19) using the dimensionless variables suggested by Gagarina et al. (1970) yields:

3.20 $ln ( 1 + 1 − C / C 0 1 − 1 − C / C 0 ) = a ( b − b 0 ) ⋅ C 0 t$

If all the constants on the right side of the previous equation are lumped together to denote the effective rate constant, namely (σ),

3.21 $σ = a ( b − b 0 ) ⋅ C 0$

Equation (3.20) can be simplified to yield:

3.22 $ln ( 1 + 1 − C / C 0 1 − 1 − C / C 0 ) = σ t$

which corresponds to the equation for a straight line. It is evident that the slope of a graph of $ln false[ false( 1 + false( 1 − C / C 0 false) false) / false( 1 − false( 1 − C / C 0 false) false) false]$ vs. t will give the value corresponding to the effective rate constant.

The aforementioned simplified models have been successfully used by various investigators to describe the reaction kinetics of carotenoids assuming a scheme of an unbranched chain as previously described. It is obvious that in systems where oxygen accessibility is limited due to the density of the material, higher rates of oxidation will take place close to the surface. Hence, different rates of degradation of the carotenoids will take place simultaneously, thus, highly complicating the analysis of the kinetics for the overall system.

Non-Chain Reactions:

Non-chain catalyzed reactions may involve the interaction of the substrate with the catalyst to form a complex, followed by its decomposition to form the product. Upon decomposition, the catalyst is then regenerated and is capable of taking part in the reaction once again. A typical example of this behavior is the acid-catalyzed hydrolysis of pyranosides according to the pathways presented in Figure 3.5. According to this diagram, the mechanism involves rapid reversible protonation of the glycosidic oxygen atom to produce a protonated oligosaccharide (2), which undergoes a slow unimolecular decomposition to a stable monosaccharide and an acyclic carbonium ion (3). It is considered that the carbonium ion is stabilized by resonance with the oxonium ion (4). Nucleophilic addition of water would yield a protonated reducing sugar (5), which through the loss of a proton would result in the hydrolytic products (6) and reappearance of the catalyst.

Figure 3.5   Illustration of a non-chain catalyzed reaction: the acid-catalyzed hydrolysis of pyranosides.

Enzyme Kinetics:

Another example of transition complex reactions is that of enzyme catalyzed reactions. It should be mentioned that most of the reactions occurring in biological systems are catalytic in nature. The basic principles for enzyme-catalyzed reactions have been presented by Michaelis-Menten, who proposed the theory of complex formation according to the following equation:

$E + S ⇄ k − 1 k 1 ES ⇄ k − 2 k 2 E + P$

where E is the enzyme; S is the substrate; ES is the enzyme-substrate complex; and P is the product. Both reactions are considered to be reversible. In this equation, k 1, k –1, k 2, and k –2 are the specific constants for the designated reactions.

Although the general principle of chemical kinetics may apply to enzymatic reactions, the phenomenon of saturation with substrate is unique to enzymatic reactions. In fact, at low substrate concentrations the reaction velocity is proportional to the substrate concentration, and thus the reaction is first-order with respect to the substrate. As the substrate conc entration increases, the reaction progressively decreases, being no longer proportional to the concentration of the substrate and deviating from any first-order kinetics. The reaction follows zero-order reaction kinetics, due to saturation with the substrate (Figure 3.6).

Figure 3.6   Illustration of reaction rate dependence on substrate concentration for the initial phase of an enzyme-catalyzed reaction, indicating maximum velocity (V max ), ½V max and Michaelis constant (K m ).

For the particular case of enzyme kinetics, the cases of competitive and noncompetitive inhibition need to be considered. In the first case, the competitive inhibitor (I) is able to interact with the enzyme to generate a complex (EI), according to the reaction:

$E + I ⇄ k − 1 k 1 EI$

In this type of reaction, the complex EI does not break down to create products. However, the reaction can be reversed by increasing the substrate concentration.

On the other hand, in the case of noncompetitive inhibition, the inhibitor may bind to the enzyme on a locus different from the active site of the enzyme, and thus it may bind to the free enzyme or to the complex according to the equations presented below:

$E + I ⇄ EI ES + I ⇄ ESI$

where the forms EI and ESI are inactive.

The most common type of competitive inhibition is that created by compounds that bind reversibly with the sulfhydryl groups of cysteine residues that are essential for the catalytic activity of some enzymes. Such groups may be located at or near the active site. In this second case, the catalytic activity may be related to stearic hindrance or the ability to maintain the three-dimensional conformation of the enzyme.

It is of critical importance to be able to identify competitive and irreversible inhibition. For the particular case of irreversible inhibition, the inhibitor binds to the enzyme irreversibly, and some may modify its molecular structure. It is obvious that for this particular case the use of the Michaelis-Menten equation is not possible since this approach assumes that the interaction between the enzyme and the inhibitor is reversible. A typical case of irreversible reactions would be the case of the trypsin inhibitors found in soybeans, namely, the Kunitz and the Bowman-Birk inhibitors. Chymotrypsin has been found to be strongly inhibited by the Bowman-Birk inhibitor, while only weakly inhibited by the Kunitz inhibitor. Both inhibitors have also been shown to be active against bovine trypsin. The activity of human trypsin has been observed to be inhibited to a significant extent by the Kunitz inhibitor.

#### 3.2.4.2  Consecutive Reactions

Consecutive reactions form another category of reactions of importance in food products. Intermediates are formed in such reactions, which may decompose or react to create other compounds. In many cases, the intermediate may have a short life, and thus simplifications may be taken to describe their kinetics. Since decomposition of the intermediates may proceed under different reaction kinetics, complex situations may arise.

For the particular case of reactions in sequence, following first-order or pseudo-first-order kinetics:

$A → k 1 B → k 2 C$

and for the particular case where the aforementioned reactions are not reversible, the rate of disappearance of A can be expressed as follows:

3.23 $− d [ A ] d t = k 1 [ A ]$

which becomes:

3.24 $[ A ] = [ A 0 ] exp ( − k 1 t )$

The concentration of the intermediate can be determined by:

3.25 $d [ B ] d t = k 1 [ A ] − k 2 [ B ]$

By substituting Equation 3.24 into Equation 3.25 and multiplying each term by exp (k 2 t), the following expression is derived:

3.26 $exp ( k 2 t ) ( d [ B ] d t ) + [ exp ( k 2 t ) ] [ B ] k 2 = k 1 [ A 0 ] [ exp ( k 2 − k 1 ) t ]$

Integration of this equation with [B] = 0 at t = 0 yields:

3.27 $[ B ] = k 1 [ A 0 ] k 2 − k 1 [ exp ( − k 1 t ) − exp ( − k 2 t ) ]$

It can be easily illustrated that the final product (C) does not form immediately as A is decomposed due to the formation of B. This period is normally termed an induction period.

It is evident that the aforementioned case is a simple case for these particular types of reactions. More complicated situations correspond to the cases for consecutive reactions with a reversible step or when the individual steps do not follow the same order reaction kinetics. If the rate of disappearance of B is fairly rapid, it is evident that if one monitors the appearance of C only based on the concentration of A, inaccuracies may be introduced in the kinetic analysis.

#### 3.2.4.3  Reversible First-Order Reactions

For the most part, we have considered reactions whose rate constant consists of a single value with an integral reaction order. However, now we will consider the reaction:

$A ⇄ k − 1 k 1 B$

in which the rate of disappearance of A is given by:

3.28 $− d [ A ] d t = k 1 [ A ] − k − 1 [ B ]$

To solve this equation, two considerations are to be taken into account: (1) from the stoichiometry:

3.29 $[ A 0 ] + [ B 0 ] = [ A ∞ ] + [ B ∞ ] = [ A ] + [ B ]$

and (2) from the condition −d[A]/dt = 0 at equilibrium:

3.30 $k 1 [ A ∞ ] = k − 1 [ B ∞ ]$

Through substitution and rearrangement:

3.31 $− d [ A ] d t = ( k 1 + k − 1 ) ( [ A ] − [ A ∞ ] )$

Integration between the corresponding limits will give:

3.32 $ln ( [ A ] − [ A ∞ ] [ A 0 ] − [ A ∞ ] ) = − ( k 1 + k − 1 ) t$

According to this equation, a plot of ln ([A]−[A]) vs. time will be a straight line, whose slope corresponds to −(k 1 + k −1). Figure 3.7 describes the concentration of reactant A as a function of time for two different situations. In the first case, the reaction will reach certain equilibrium with retention values leveling off. On the other hand, if a reactant is added to rapidly consume B, this will prevent its return to A. In this situation, only the forward reaction controls –d[A]/dt with a continuous depletion of reactant A. For both cases, the initial rate of the reaction is the same. Although this type of behavior may be possible in food systems, as in the interconversion of pyridoxamine and pyridoxal, where these compounds may undergo further degradation, most information reported in the literature will consider the rate for the overall reaction with no simultaneous information for the forward and the reverse reactions. Huang and von Elbe (1985), however, developed a kinetic reaction model accounting for the forward and reverse reactions for the degradation and regeneration of betanine in solution and its degradation products, betalamic acid, and cyclodopa-5-O-glycoside. This model could predict the amount of betanine remaining before and after regeneration of the pigment under different experimental conditions. Since in most practical situations dealing with food products the majority of investigators have not explored the mechanisms of degradation of the compound under question to minimize the amount of work involved, it becomes simple to visualize the limitations of available kinetic information if one tries to extrapolate to other systems.

Figure 3.7   Illustration of time dependence of a hypothetical reversible first-order reaction.

#### 3.2.4.4  Simultaneous Competitive Reactions

In reference to complex reactions, another case of significance corresponds to the degradation of a single compound to different products, following different pathways. The degradation of ascorbic acid is a typical example of a complex reaction where several pathways may operate simultaneously. Seldom have investigators taken the time to identify the contribution of each pathway to the degradation of this vitamin, but rather have reported an overall value. In the case of chlorophylls, Heaton et al. (1996a,b) developed a general mechanistic model for rates of chlorophyll degradation to pheophorbides via either pheophytins or chlorophyllides. Depending upon the contribution of each pathway when dealing with competitive reactions, variable levels of inaccuracy may result since each reaction will proceed at a different rate. Thus, if the reaction is expressed by the following mechanism:

$A { → k 1 P 1 → k 2 P 2 → k 3 P n$

where P1, P2,…, P n   = products; and k 1, k 2,k n   = rate constants. The rate of disappearance of A is given by:

3.33 $− d [ A ] d t = ( k 1 + k 2 + ⋯ + k n ) [ A ]$

which by integration results in:

3.34 $[ A ] = [ A 0 ] exp ( − ∑ k n t )$

where

$∑ k n = k 1 + k 2 + ⋯ + k n$

Since the products are generated with different yields, it is obvious that information on product formation is needed to evaluate the rates of the reaction. Moreover, it is evident that solely monitoring the rate of disappearance of A and assigning an overall reaction rate may be highly inaccurate, since each pathway may have a different reaction order and a different associated rate constant. This approach has been commonly taken by many investigators in determining reaction rates for the degradation of vitamins, pigments, etc. This discussion should emphasize the need for a better understanding of the reaction mechanisms to properly report kinetics. Since this is a more time-consuming approach, it is understandable why many investigators circumvented the complexity of the reaction kinetics.

#### 3.2.5  Effect of Temperature

When considering reaction rates, it is clear that these values may be influenced by a large number of parameters, including temperature and pressure. In fact, equilibrium yields, chemical reaction rates, and product distribution may be drastically influenced by temperature. Since chemical reactions are accompanied by heat effects, if these are large enough to cause a significant change in temperature of the reaction mixture, these effects also need to be taken into consideration. This would be particularly important in reactor design. The effect of temperature for an elementary process may follow, in most cases, the Arrhenius equation:

3.35 $k = k 0 e − E a / R T$

where k o is the frequency or collision factor; E a is the activation energy; R is the gas constant (1.987 cal/mol·°K); and T is the absolute temperature (°K). It is obvious that if the frequency factor and the activation energy could be evaluated from molecular properties of the reactants, it would be possible to estimate the values corresponding to the reaction rate. Unfortunately, our knowledge of kinetics is limited, particularly for complex systems, as would be the case of food systems or products.

It is, however, important to mention the collision theory as an approach to deal with kinetics. In Figure 3.8, the energy levels involved in a reaction are illustrated. According to the collision theory, upon the collision of reactive molecules, enough energy is generated to provide the necessary activation energy. Such a theory was used as the foundation for the determination of rate expressions based on the frequency of molecular collision required to generate a minimum energy.

Figure 3.8   Representation of potential energy levels during the process of a given endothermic reaction.

Another theory, the activated-complex or transition-state theory, has also been suggested. According to this approach, which still relies on reactions occurring due to collision between reactive molecules, an activated complex is formed from the reactants, which eventually decomposes to generate products. The activated complex is in thermodynamic equilibrium with the reactants. Complex decomposition is, then, the limiting step. Regardless of the theory considered, these approaches do not provide the means to rapidly and easily calculate activation energies from simple thermodynamic information. Thus, in practical terms, one has to obtain basic kinetic information to be able to determine the effect of temperature as affecting reaction kinetics. Based on the Arrhenius equation it is clear that if one plots the ln k vs. 1/T, the slope would correspond to the activation energy divided by the gas constant. Moreover, this value will not provide by itself any idea of the reactivity of a given system, only information on temperature dependence of the reaction.

Although the Arrhenius equation is commonly used to describe temperature dependence of the reaction rate in most food systems, deviations may occur as reported by several authors including Labuza and Riboh (1982) and Taoukis et al. (1997). In fact, a large number of factors may contribute to deviations. Changes in reaction mechanisms may occur for a large temperature range. For instance, it is highly possible that mechanisms of deterioration may change at conditions below the freezing point due to a concentration effect. On the other hand, at high temperatures, changes in the physical state of some compounds including fats and sugars may occur. Lipids may change from a solid to a liquid state, while sugars may change from an amorphous to a crystalline or to a liquid state. Because of the high complexity of food systems, it is also possible that when various mechanisms of deterioration operate simultaneously, the effect of temperature may alter the rates of one, thus causing inhibition or catalysis in the other mechanisms. Finally, irreversible changes such as starch hydrolysis or protein denaturation may occur due to temperature, thus modifying the reactivity of the system. In fact, although enzyme catalyzed reactions will have an increasing reaction rate upon an increase in temperature, a decrease will be observed beyond a certain temperature due to enzyme inactivation. Typical values for activation energies for a number of reactions are summarized in Table 3.2.

### Table 3.2   Activation Energies for Selected Reactions in Food Related Systems

Activation Energy (Kcal/mole)

Reaction

MIN

MAX

AVG

MED

CT

Vitamins: a

Vitamin C

3

46

16

16

66

Thiamine

8

31

25

28

24

Riboflavin

7

50

18

15

16

B6

14

30

22

24

6

Folates

8

23

14

18

19

PA

20

38

27

25

8

Carotene

1

29

16

17

30

Vitamin E

3

13

7

9

22

Tocopherols HighTemp

3

14

8

8

12

Trienols

0.5

27

13

12

10

Pigments:a

Total chlorophyll (as measured)

5

62

15

11

6

Chlorophyll a

5

27

17

17

15

Chlorophyll b

7

35

17

16

14

Pheophytin a

21

25

23

Pheophytin b

16

17

16

Anthocyanins

5

30

18

20

76

Carotenoids (pigments)

1

23

10

10

15

Betanines

5

29

15

15

53

Phycocyanins

10

136

32

23

20

Browning

7

58

23

13

93

Miscellaneous:

Enzyme reactionsb

9

111

45

36

30

Hydrolysis of disaccharides

10

15

12.5

Lipid oxidation

10

25

17.5

Protein denaturationb

6

135

40

26

40

Moisture diffusivityc (dehydration)

3

26

10

8

30

Microbial inactivation: b

6

198

76

75

240

Vegetative cell destructiond

50

150

100

Spore destructiond

60

80

70

Note: MIN = minimum; MAX = maximum; AVG = average; MED = median values; CT = count total calculated from the following sources: (a) current chapter tables; (b) Okos, M.R., 1986; (c) Heldman, D.R. and Lund, D.B., 1992; (d) Fennema, O.R., 1975.

#### 3.2.6  Effect of Pressure

Traditionally, processing of foods has involved thermal treatments with the specific goal of making the foods microbiologically safe. With greater concerns over the nutritional benefits of the foods as well as qualitative aspects such as texture and color, investigation into advancing food processing has resulted in the emergence of various new technologies within the food industry. One area of recent interest is the use of ultra-high pressure (UHP) processing of foods, also referred to as high hydrostatic pressure (HHP) and high pressure processing (HPP). According to the Le Chatelier principle, any reaction, conformational change, or phase change that is accompanied by a decrease in volume will be favored at high pressure, while reactions involving an increase in volume will be inhibited (Williams, 1994).

The kinetics of reactions as influenced by pressure may be best approached from the Eyring (activated complex or absolute theory) where reaction rates are based on the formation of an unstable intermediate complex, which is in quasi-equilibrium with the reactants. For instance, in a bimolecular reaction, reactants A and B form an intermediary complex (AB)*, with an equilibrium rate constant (k 1), which may further decompose at a rate constant (k 2) to form product(s).

$A + B ⇄ k 1 ( AB ) * → k 2 P$

The overall reaction rate is therefore controlled by the rate of formation of the activated complex which is a function of the change in “Gibbs free energy” (ΔG) going from the normal to the activated state, similar to the previous discussion (Section 2.3.4.1.). In the case with the effect of changing temperature, the relationship was given by the Arrhenius equation, assuming pressure was held constant. The influence of pressure on reaction rate, however, may be described by the basic thermodynamic relationship, as shown in Equation (3.36) as applied to the Eyring Equation (3.37):

3.36 $( d Δ G o d P ) T = Δ V o$

where ΔG o is the standard free energy associated with the formation of one mole of substance at 25°C and 1 atmosphere (molal free energy); P is pressure; and ΔV o is the associated volume at constant temperature (T). The Eyring equation, where ΔG * = ΔH *– ΔS * T:

3.37 $k = k B T h exp ( Δ S * R ) e x p ( − Δ H * R T ) = k B T h exp ( − Δ G * R T )$

where ΔG * is the change in free energy; ΔS * is the entropy; ΔH * is the enthalpy; k B is the Boltzman constant; h is Plank’s constant; and R is the gas constant. Combining these equations results in the following expression at constant temperature:

3.38 $( d ln k d P ) T = − Δ V * R T$

By integration this gives the expression for the rate constant, k:

3.39 $ln k = ln k 0 − Δ V * R T P$

where k o is a constant dependent on the s ystem; ΔV * is the volume of activation; and T is temperature (°K). The activation volume relates the change in volume between that of the reactants and that of the activated complex. Basically, this expression indicates that the rate constant increases with increasing pressure if ΔV * is negative. In other words, the molar volume of the activated complex is smaller than that of the reactants together. From a practical point of view, the activation volume can be determined from the slope (−ΔV */RT) of the plot of ln k versus P at constant temperature. This is similar to the method of determination of the activation energy (E a ) from the slope (−E a /R) from a plot of ln k versus 1/T at constant pressure. It is also important for the rate constant (k) to be measured above the “critical” or “threshold” pressure in order for activation volume constants to be meaningful.

It should be pointed out that in the process of pressure treating foods, there is an increase in temperature due to the work of compression. It is therefore, critical to maintain a constant temperature during the pressure treatment in order to obtain meaningful kinetic data. Farkas and Hoover (2000) pointed out several critical process factors to take into account when conducting pressure related studies. These factors include maintaining constant composition, pH, water activity, come-up-times and pressure release times, change in temperature due to compression, and in the case of microorganism testing, the type, age, culturing, and growth conditions should all be kept the same for comparison.

Limited work on the effect of pressure on kinetics of vitamin and pigment degradation has been reported in the literature. Thus far, most of the emphasis has been placed on the microbiological aspects, as would be expected. A further discussion on effects of pressure will be addressed in a later section of this chapter.

In the following section, a brief discussion of the mechanisms of deterioration of food components including vitamins and pigments, and some of the most relevant kinetic information will be presented.

#### 3.3  Kinetics of Food Components

Over the past years, many reviews on the stability of various nutrients and pigments have been published (Harris and von Loesecke, 1960; Harris and Karmas, 1975; DeRitter, 1976; Archer and Tannenbaum, 1979; Thompson, 1982; Villota and Hawkes, 1986; Clydesdale et al., 1991; Delgado-Vargas et al., 2000; Dionísio et al, 2009; Rickman et al., 2007; Riaz et al., 2009; Nayak et al., 2015); they discuss the concerns over nutrient and general quality losses during different types of processing and storage ­conditions. With regard to mathematical modelling of quality changes in foods, additional reviews have been published (Van Boekel, 2008; Ling et al., 2015); they discuss the potential for new and improved mathematical models, facilitated through the advancements in computer technology, where more complex models may be required to more accurately identify and quantify factors to predict quality. However, despite the large numbers of kinetic studies on the stability of nutrients, pigments, textural properties, and potential new predictive modelling techniques, etc., it is still evident that systematic studies geared to elucidate mechanisms of reactions to provide information on developing comprehensive kinetic models remain scarce. In addition, a great deal of research over the past decade has resulted in many new findings for vitamins and their derivatives, as well as other nutraceuticals, elucidating their reaction mechanisms relevant to metabolic pathways, and thus reestablishing the importance of fortification in foods and further emphasizing the significance of understanding their stability during processing and storage. Moreover, it is also clear that although work carried out in model systems contribute to our understanding of reactions, the actual kinetic information obtained may not be readily applicable to highly complex systems such as foods. Thus, information compiled for this review, considers primarily the biochemistry and stability of bioactive ingredients in real food products, with limited emphasis given to model systems.

#### 3.3.1.1  Vitamin C (Ascorbic Acid)

Vitamin C (ascorbic acid) is chemically known as L-3-keto-threo-hexuronic acid lactone, is naturally found as the L-isomer in various citrus fruits, hip berries, and fresh tea leaves. It also exists in a stereoisomeric form referred to as D-isoascorbic acid (also called D-araboascorbic or erythorbic acid) and has only a twentieth of the bioavailability of L-ascorbic acid. L-ascorbic acid can reversibly convert to dehydroascorbic acid in the presence of mild oxidants and may subsequently and irreversibly convert to 2,3-diketogulonic acid, which has no bioavailability. This makes it important for proper differentiation during its analysis for nutritional purposes. Originally discovered for its ability to cure scurvy, ascorbic acid is currently well known as a biological cofactor that plays an essential role in a broad array of physiological pathways, fundamental to cellular functions. Its biochemical actions as an antioxidant and universal reducing agent in vivo have been well documented and reviewed (Eitenmiller et al., 2008; Johnston et al., 2014). Considerable amounts of ascorbic acid may be lost during processing and storage of food products. In fact, ascorbic acid is readily destroyed by heating and oxidation, and its protection is particularly difficult to achieve. Other factors influencing the degradation of this vitamin include water activity or moisture content, pH, and metal traces, especially copper and iron. In general, it has been observed for a wide number of products containing ascorbic acid that the reaction appears to follow first-order kinetics. It should be mentioned, however, that different pathways exist for the degradation of ascorbic acid. Such pathways give origin to different breakdown products, and, therefore, affect the overall rates of vitamin degradation. In fact, the reaction may proceed under aerobic or anaerobic conditions, or through catalyzed or uncatalyzed aerobic pathways (Figure 3.9). Understanding of the mechanisms involved facilitates the handling of kinetic data. However, because of the fact that many parameters will influence the kinetics of ascorbic acid decomposition, it is difficult to establish a precursor–product relationship, except for the earlier part of the reactions. For instance, in stored canned products, the reaction may occur at the beginning through catalyzed or uncatalyzed aerobic mechanisms. Upon storage after the disappearance of the free oxygen, subsequent losses may be due to anaerobic decomposition of the compound. It is also possible that various mechanisms of deterioration can operate simultaneously, thus highly complicating the treatment of the kinetic data. For instance, in the presence of oxygen, it is possible that the anaerobic pathway will take place, although its contribution appears to be less significant than even the pathway for uncatalyzed degradation. Eison-Perchonok and Downes (1982) used second-order kinetics to describe the degradation of ascorbic acid under limiting oxygen concentrations. Finholt et al. (1963) indicated a maximum rate for anaerobic degradation of ascorbic acid at pH 4 and attributed this behavior to a 1:1 complex of ascorbic acid molecules and hydrogen ascorbate ions which would be present at the highest concentration at a pH near 4.0. Since at normal temperatures the anaerobic degradation proceeds at low rates, normally its contribution can be considered insignificant in the presence of excess oxygen.

Figure 3.9   Degradation pathways of ascorbic acid (adapted from Bauernfeind and Pinkert, 1970 and Tannenbaum et al., 1985).

With regard to metal-catalyzed reactions of ascorbic acid, it has been found that the reaction can be catalyzed by transition metals such as copper, iron, and vanadium, both free and bound in complex compounds (Khan and Martell, 1967a,b, 1968). The reaction may also be catalyzed by the copper containing metal enzyme, ascorbate oxidase. Two different mechanisms have been proposed for the non-enzyme catalyzed degradation of ascorbic acid by free and complex ions. Differences in their reaction order, rate limiting step, and products of oxygen reduction were observed. Schwertnerová et al. (1976) reported that the autooxidation of ascorbic acid, catalyzed by copper ions, followed the Michaelis-Menten law in the presence of an inhibitor. Pekkarinen (1974) observed that the autooxidation of ascorbic acid catalyzed by iron salts in citric acid solutions had a clear induction period, particularly at lower concentrations. Although the addition of copper salt decreased the induction period, it was also observed that the rate of the reaction slowed down at a later stage. It is possible, according to the author, that the copper salt may destroy radicals produced by the reaction catalyzed by the iron salts. Spanyár and Kevei (1963) had previously reported that copper was very destructive to ascorbic acid in air but had an insignificant effect in nitrogen. The authors also indicated that although iron had a prooxidant activity, iron and copper combined accelerated the degradation of ascorbic acid at a lower rate than either of the two acting alone.

In most situations it has been observed that an increase in temperature also increases the destruction rates of ascorbic acid. On the other hand, contradicting results have been suggested for conditions at sub-freezing temperatures. Grant and Alburn ( 1965a,b) studied the degradation of ascorbic acid in frozen and unfrozen solutions containing 10−4 M ascorbic acid in a 0.02 M acetate buffer at pH values of 5.0 and 5.5. At both pH values, the rates of ascorbic acid oxidation were observed to be significantly higher at −11°C as compared to the system at 1°C, and more pronounced for the system at pH 5.5. A number of factors have been suggested to be responsible for the observed trends. An increase in concentration of the reactants occurs upon freezing of the system (Pincock and Kiovsky, 1966). Other factors, such as a catalytic effect exerted by the ice crystals, favorable orientation of the reactants in the partially frozen system, and a decrease in dielectric constant or an increase in proton mobility, have also been given as possible reasons for the enhanced rates of ascorbic acid degradation at sub-freezing temperatures. It is also possible that oxygen availability is another factor to be taken into consideration. Results reported by Thompson and Fennema (1971) indicated that, in fact, an increase in reactant concentration, pH, and oxygen availability could explain higher rates of degradation or a smalle r than expected decrease in rate in partially frozen systems. Changes of pH as a result of freezing should also be taken into consideration. In fact, Van den Berg and Rose (1959) reported significant changes in pH during the freezing of well-buffered phosphate solutions upon freezing from 0 to −10°C.

In general, taking into consideration the possible reaction pathways, as summarized by Bauernfeind and Pinkert (1970), the specific kinetic parameters may be dependent on more factors than normally accounted for. In fact, it is a common assumption to measure the rate of degradation of ascorbic acid in food products, natural or formulated, for only a given initial concentration. Work done by various investigators, indicates that for the case of this particular vitamin the degradation may follow pseudo-first-order reaction kinetics. In fact, the presence of breakdown products will alter the kinetic rates and, possibly, the mechanisms of degradation (Villota, 1979). In fact, depending on the composition of the system and considering that some of the steps involved in the degradation of ascorbic acid are reversible, it is clear that although the reaction may still follow first-order reaction kinetics the rates of degradation will vary depending on the equilibrium created in the system and the levels of breakdown products. Lavelli and Giovanelli (2003) further exemplified the effect of initial concentration in tomato products. They found higher rates of degradation of ascorbic acid at lower initial concentration levels (Table 3.3).

### Table 3.3   Kinetic Parameters for Vitamin Degradation During Thermal Processing and/or Storage

Commodity

Process/Conditions

r2

kT value (min−1)

Ea (kcal/mol)

Reaction Order

r2

Temp. Range (°C)

t1/2

(min)

References

Water Soluble Vitamins:

Ascorbic acid (Vitamin C)

Apricots

Canned

0.94

k26.7 = 53.236 × 10−8

1.30 × 106

Cameron et al. (1955)

0.82

k18.3 = 9.079 × 10−8

23.7

1

0.91

10–26.7

7.64 × 106

0.84

k10 = 5.043 × 10−8

13.75 × 106

Asparagus

Canned

0.75

k26.7 = 12.967 × 10−8

5.35 × 106

Cameron et al. (1955)

0.72

k18.3 = 7.769 × 10−8

8.1

1

0.97

10–26.7

8.92 × 106

0.85

k10 = 5.808 × 10−8

11.95 × 106

Raw, whole in crushed ice; room temperature

0.91

k0 = 0.0001065

1

~0

6.51 × 103

0.92

k20 = 0.0002394

1

~20

2.90 × 103

Ascorbic acid

Asparagus

Bud segment

Blanching

0.986

k100 = 30.4 × 10−2

2.28

Zheng et al. (2011)

Dimensions:

0.986

k95 = 15.9 × 10−2

4.35

Base: 0.8–1.0 cm

0.986

k90 = 7.34 × 10−2

9.44

Length: 20 cm

0.986

k85 = 4.60 × 10−2

15.10

0.986

k80 = 2.67 × 10−2

24.24

1

0.965

60–100

25.96

0.986

k75 = 1.39 × 10−2

49.87

4 sampling time intervals, based on temp:

0.986

k70 = 1.12 × 10−2

61.89

0.986

k65 = 0.86 × 10−2

80.60

0.986

k60 = 0.59 × 10−2

121.60

Upper segment

100°C: 2–8 min

0.986

k100 = 27.5 × 10−2

2.52

95°C: 3–12 min

0.986

k95 = 15.1 × 10−2

4.59

90°C: 5–20 min

0.986

k90 = 7.63 × 10−2

9.08

85°C: 7–28 min

0.986

k85 = 4.68 × 10−2

14.81

80°C: 10–40 min

0.986

k80 = 2.81 × 10−2

24.68

1

0.986

60–100

24.67

75°C: 15–60 min

0.986

k75 = 1.63 × 10−2

42.52

70°C: 20–80 min

0.986

k70 = 1.09 × 10−2

63.59

65°C: 25–100 min

0.986

k65 = 0.81 × 10−2

85.57

60°C: 30–120 min

0.986

k60 = 0.46 × 10−2

150.68

Middle segment

0.986

k100 = 25.8 × 10−2

2.69

Zheng et al. (2011)

0.986

k95 = 15.0 × 10−2

4.63

Assay:

0.986

k90 = 6.09 × 10−2

11.38

Indophenol method

0.986

k85 = 3.62 × 10−2

19.15

0.986

k80 = 2.16 × 10−2

28.70

1

0.988

60–100

32.09

0.986

k75 = 1.02 × 10−2

67.96

0.986

k70 = 0.74 × 10−2

93.67

0.986

k65 = 0.39 × 10−2

177.73

0.986

k60 = 0.25 × 10−2

277.26

Middle segment

0.986

k100 = 24.5 × 10−2

2.83

0.986

k95 = 13.0 × 10−2

5.34

0.986

k90 = 5.46 × 10−2

12.70

0.986

k85 = 3.45 × 10−2

20.09

0.986

k80 = 2.17 × 10−2

27.56

1

0.983

60–100

31.94

0.986

k75 = 1.06 × 10−2

65.39

0.986

k70 = 0.61 × 10−2

113.63

0.986

k65 = 0.42 × 10−2

165.04

0.986

k60 = 0.29 × 10−2

239.02

Ascorbic acid

Beef

Laing et al. (1978)

Model system (40% soy flour, 25% beef, 20% sucrose, 10% propylene glycol)

aw = 0.69

3.3

0

61–100

aw = 0.80

4.1

0

61–100

aw = 0.90

3.8

0

61–100

Ascorbic acid

Breakfast cereal

Packaging:

Kirk et al. (1977)

Model system (soy protein/fat/carbohydrate/salt/sugar)

208 × 006

TDT cans

aw = 0.10

k37 = 0.681 × 10−5

10.18 × 104

Total ascorbic acid

k30 = 0.632 × 10−5

8.1

1

0.956

10–37

10.97 × 104

k20 = 0.313 × 10−5

22.15 × 104

k10 = 0.215 × 10−5

32.24 × 104

aw = 0.24

k37 =0.479 × 10−5

1.99 × 104

Kirk et al. (1977)

k30 = 1.236 × 10−5

15.9

1

0.971

10–37

5.61 × 104

k20 = 0.660 × 10−5

10.50 × 104

k10 =0.257 × 10−5

26.97 × 104

aw = 0.40

k37 = 4.882 × 10−5

1.42 × 104

k30 = 2.174 × 10−5

17.6

1

0.997

10–37

3.19 × 104

k20 = 0.889 × 10−5

7.80 × 104

k10 = 0.292 × 10−5

23.74 × 104

aw = 0.50

k37 = 6.417 × 10−5

1.08 × 104

k30 = 2.771 × 10−5

19.2

1

0.986

10–37

2.50 × 104

k20 =0.778 × 10−5

8.91 × 104

k10 = 0.340 × 10−5

20.39 × 104

aw = 0.65

k37 = 10.931 × 10−5

0.634 × 104

k30 = 3.313 × 10−5

19.2

1

0.984

10–37

2.09 × 104

k20 = 1.000 × 10−5

6.93 × 104

k10 = 0.347 × 10−5

19.98 × 104

Reduced ascorbic acid

aw = 0.10

k37 = 0.854 × 10−5

8.12 × 104

k30 = 0.771 × 10−5

7.1

1

0.979

10–37

8.99 × 104

k20 = 0.451 × 10−5

15.37 × 104

k10 = 0.299 × 10−5

23.18 × 104

aw = 0.24

k37 = 3.083 × 10−5

2.25 × 104

k30 = 1.597 × 10−5

14.2

1

0.985

10–37

4.34 × 104

k20 = 0.931 × 10−5

7.45 × 104

k10 = 0.312 × 10−5

22.22 × 104

aw = 0.40

k37 = 5.465 × 10−5

1.27 × 104

k30 = 2.667 × 10−5

17.8

1

0.994

10–37

2.60 × 104

k20 = 1.174 × 10−5

5.90 × 104

k10 = 0.326 × 10−5

21.26 × 104

Reduced ascorbic acid

aw = 0.50

k37 = 6.181 × 10−5

1.12 × 104

Kirk et al. (1977)

k30 = 3.251 × 10−5

18.1

1

0.989

10–37

2.16 × 104

k20 = 0.903 × 10−5

7.68 × 104

k10 = 0.403 × 10−5

17.20 × 104

aw = 0.65

k37 = 11.701 × 10−5

0.592 × 104

k30 = 3.674 × 10−5

21.1

1

0.989

10–37

1.89 × 104

k20 = 1.340 × 10−5

5.17 × 104

k10 = 0.382 × 10−5

18.154 × 104

Packaging: cardboard boxes/w/wax paper liners

Total ascorbic acid

aw = 0.10

k30 = 1.701 × 10−5

1

30

4.07 × 104

aw = 0.40

k30 = 2.521 × 10−5

1

30

2.75 × 104

aw = 0.85

k30 = 4.868 × 10−5

1

30

1.45 × 104

Reduced ascorbic acid

aw = 0.10

k30 = 1.847 × 10−5

1

30

3.75 × 104

aw = 0.40

k30 = 2.618 × 10−5

1

30

2.65 × 104

aw = 0.85

k30 = 4.903 × 10−5

1

30

1.41 × 104

Total ascorbic acid

aw = 0.24

k30 = 2.507 × 10−5

1

30

2.76 × 104

aw = 0.40

k30 = 5.944 × 10−5

1

30

1.17 × 104

Reduced ascorbic acid

aw = 0.24

k30 = 2.625 × 10−5

1

30

2.64 × 104

aw = 0.40

k30 = 5.313 × 10−5

1

30

1.30 × 104

Breakfast cereal model system [similar to Kirk et al. (1977)]

Dennison and Kirk (1978)

Packaging: 303 cans

Total ascorbic acid

aw = 0.10

k37 = 1.229 × 10−5

5.64 × 104

k30 = 0.944 × 10−5

10.7

1

0.963

10–37

7.34 × 104

k20 = 0.597 × 10−5

11.61 × 104

k10 = 0.229 × 10−5

30.27 × 104

aw = 0.24

k20 = 0.764 × 10−5

1

20

9 .07 × 104

aw = 0.40

k37 = 5.035 × 10−5

1.38 × 104

Dennison and Kirk (1978)

k30 = 2.542 × 10−5

16.0

1

0.998

10–37

2.73 × 104

k20 = 1.014 × 10−5

6.84 × 104

k10 = 0.417 × 10−5

16.62 × 104

aw = 0.65

k37 = 8.382 × 10−5

0.827 × 104

k30 = 3.854 × 10−5

18.3

1

0.999

10–37

1.80 × 104

k20 = 1.410 × 10−5

4.92 × 104

k10 = 0.479 × 10−5

14.47 × 104

Reduced ascorbic acid

aw = 0.10

k37 = 1.129 × 10−5

5.36 × 104

k30 = 0.896 × 10−5

10.7

1

0.977

10–37

7.74 × 104

k20 = 0.583 × 10−5

11.89 × 104

k10 = 0.236 × 10−5

29.377 × 104

aw = 0.24

k20 = 0.715 × 10−5

1

20

9.69 × 104

aw = 0.40

k37 = 5.271 × 10−5

1.32 × 104

k30 = 2.167 × 10−5

15.5

1

0.984

10–37

3.20 × 104

k20 = 1.021 × 10−5

6.79 × 104

k10 = 0.438 × 10−5

15.83 × 104

aw = 0.65

k37 = 8.104 × 10−5

0.885 × 104

k30 = 3.549 × 10−5

16.9

1

0.994

10–37

1.95 × 104

k20 = 1.417 × 10−5

4.89 × 104

k10 = 0.563 × 10−5

12.31 × 104

Corn (yellow)

Canned

0.92

k26.7 = 18.768 × 10−8

3.693 × 106

Cameron et al. (1955)

0.74

k18.3 = 10.867 × 10−8

9.7

1

0.99

10–26.7

6.378 × 106

0.76

k10 = 7.183 × 10−8

9.650 × 106

Ascorbic acid

Fruit juice

% O2 Concentration:

k1

Van Bree et al. (2012)

Juice Model

0.03

0.94

k22 = 1.22 × 10−5

5.70 × 104

(25 g glucose, 25 g fructose, 40 g sucrose, 9.2 g citric acid, 0.45 g ascorbic acid, 1.27 g L-asparagine, 0.7 g L-arginine/liter)

0.63

0.94

k22 = 2.08 × 10−5

3.34 × 104

1.17

0.96

k22 = 3.04 × 10−5

2.28 × 104

2.78

0.99

k22 = 5.36 × 10−5

1

22

1.29 × 104

4.84

0.98

k22 = 8.19 × 10−5

0.846 × 104

10.02

0.90

k22 = 18.1 × 10−5

0.382 × 104

20.90

0.99

k22 = 27.1 × 10−5

0.256 × 104

k1

Commercial orange

0.03

0.79

k22 = 0.701 × 10−5

9.88 × 104

0.98

0.91

k22 = 1.37 × 10−5

1

22

5.07 × 104

2.91

0.99

k22 = 2.15 × 10−5

3.23 × 104

10.8

0.96

k22 = 7.01 × 10−5

0.988 × 104

k1

Juice model

0.03

0.98

k22 = 1.21 × 10−5

k1

5.74 × 104

Van Bree et al. (2012)

0.63

0.97

k22 = 2.06 × 10−5

consecutive irreversible

3.37 × 104

1.17

0.99

k22 = 2.83 × 10−5

2.45 × 104

2.78

0.99

k22 = 5.03 × 10−5

1

22

1.38 × 104

4.84

0.98

k22 = 7.78 × 10−5

0.891 × 104

10.02

0.92

k22 = 18.2 × 10−5

0.380 × 104

20.90

0.98

k22 = 21.7 × 10−5

0.319 × 104

k1

Commercial orange

0.03

0.99

k22 = 0.694 × 10−5

k1

9.98 × 104

0.98

0.99

k22 = 1.35 × 10−5

consecutive irreversible

5.15 × 104

2.91

0.99

k22 = 2.07 × 10−5

1

22

3.35 × 104

10.8

0.96

k22 = 6. 83 × 10−5

1.01 × 104

k3

Juice model

0.03

0.98

k22 = 15.3 × 10−5

k3

0.452 × 104

0.63

0.97

k22 = 19.1 × 10−5

consecutive irreversible

0.363 × 104

1.17

0.99

k22 = 20.6 × 10−5

0.337 × 104

2.78

0.99

k22 = 21.7 × 10−5

1

22

0.320 × 104

4.84

0.98

k22 = 23.3 × 10−5

0.298 × 104

10.02

0.92

k22 = 24.8 × 10−5

0.280 × 104

20.90

0.98

k22 = 29.3 × 10−5

0.237 × 104

k3

Van Bree et al. (2012)

Commercial orange

0.03

0.99

k22 = 1.03 × 10−5

k3

0.700 × 104

0.98

0.99

k22 = 1.45 × 10−5

consecutive irreversible

0.478 × 104

2.91

0.99

k22 = 1.76 × 10−5

1

22

0.393 × 104

10.8

0.96

k22 = 2.56 × 10−5

0.271 × 104

k1

Juice model

0.03

0.98

k22 = 1.31 × 10−5

k1

5.28 × 104

0.63

0.97

k22 = 2.06 × 10−5

consecutive reversible

3.37 × 104

1.17

0.99

k22 = 3.06 × 10−5

2.26 × 104

2.78

0.99

k22 = 5.03 × 10−5

1

22

1.38 × 104

4.84

0.98

k22 = 7.78 × 10−5

0.891 × 104

10.02

0.92

k22 = 18.3 × 10−5

0.380 × 104

20.90

0.98

k22 = 21.7 × 10−5

0.319 × 104

k1

Commercial orange

0.03

0.99

k22 = 0.757 × 10−5

k1

9.16 × 104

0.98

0.99

k22 = 1.50 × 10−5

consecutive irreversible

4.62 × 104

2.91

0.99

k22 = 2.07 × 10−5

1

22

3.35 × 104

10.8

0.96

k22 = 6.83 × 10−5

1.01 × 104

k2

Juice model

0.03

0.98

k22 = 1.31 × 10−5

k2

5.28 × 104

0.63

0.97

k22 = 0

1.17

0.99

k22 = 2.03 × 10−5

consecutive reversible

3.42 × 104

2.78

0.99

k22 = 0

1

22

4.84

0.98

k22 = 0

10.02

0.92

k22 = 0

20.90

0.98

k22 = 0

k2

Commercial orange

0.03

0.99

k22 = 0.757 × 10−5

k2

9.16 × 104

0.98

0.99

k22 = 1.50 × 10−5

consecutive irreversible

4.62 × 104

2.91

0.99

k22 = 0

1

22

10.8

0.96

k22 = 0

Juice model

0.03

0.98

k22 = 15.1 × 10−5

k3

0.460 × 104

Van Bree et al. (2012)

0.63

0.97

k22 = 19.1 × 10−5

consecutive reversible

0.363 × 104

1.17

0.99

k22 = 20.6 × 10−5

0.336 × 104

2.78

0.99

k22 = 21.7 × 10−5

1

22

0.320 × 104

4.84

0.98

k22 = 23.3 × 10−5

0.298 × 104

10.02

0.92

k22 = 24.8 × 10−5

0.280 × 104

20.90

0.98

k22 = 29.3 × 10−5

0.237 × 104

k3

Commercial orange

0.03

0.99

k22 = 10.3 × 10−5

k3

0.670 × 104

0.98

0.99

k22 = 14.5 × 10−5

consecutive irreversible

0.478 × 104

2.91

0.99

k22 = 17.6 × 10−5

1

22

0.393 × 104

10.8

0.96

k22 = 25.6 × 10−5

0.271 × 104

Grapefruit segments

Canned

0.99

k26.7 = 76.141 × 10−8

0.910 × 106

Cameron et al. (1955)

0.90

k18.3 = 22.558 × 10−8

20.7

1

0.99

10–26.7

3.073 × 106

0.73

k10 = 9.810 × 10−8

7.066 × 106

Grapefruit juice

Thermal concentration

Solids

11.2 oBx

0.937

k96 = 0.002642

262

Saguy et al. (1978b)

0.996

k95 = 0.002503

4.98

1

0.998

61–96

277

0.966

k80 = 0.001899

(apparent)

365

0.937

k61 = 0.001276

543

31.2 oBx

0.978

k91 = 0.002701

257

0.956

k82 = 0.002165

5.33

1

0.997

60–91

320

0.974

k75 = 0.001874

370

0.925

k60 = 0.001349

514

47.1 oBx

0.935

k96 = 0.003777

184

0.964

k90 = 0.003121

6.69

1

0.998

61–96

222

0.976

k80 = 0.00246

282

0.962

k61 = 0.00143

485

55.0 oBx

0.988

k91 = 0.004712

147

Saguy et al. (1978b)

0.972

k81 = 0.003348

8.60

1

0.999

61–91

207

0.970

k75 = 0.002715

255

0.951

k61 = 0.001618

428

62.5 oBx

0.924

k96 = 0.01068

69

0.945

k81 = 0.005365

11.30

1

0.998

68–96

129

0.929

k76 = 0.004561

163

0.947

k68 = 0.003022

229

Green beans

Canned

0.89

k26.7 = 25.832 × 10−8

2.683 × 106

Cameron et al. (1955)

0.86

k18.3 = 17.819 × 10−8

7.5

1

0.99

10–26.7

3.890 × 106

0.81

k10 = 12.317 × 10−8

5.628 × 106

Raw, whole, sieved by mesh size

No. 5

0.96

k20 = 0.0002454

1

20

2,825

No. 4

0.95

k20 = 0.0002337

1

20

2,966

Nos. 2 and 3

0.85

k20 = 0.0003789

1

20

1,829

Stored at room temp.

(18.9–21.1°C)

Green beans

Frozen

k−5 = 22.920 × 10−6

3.024 × 104

Giannakourou et al. (2003)

Blanch: 90°C/2 min

k−10 = 9.627 × 10−6

24.3

1

0.967

−5 to −20

7.200 × 104

IQF: −22°C/2 min

k−15 = 3.946 × 10−6

17.568 × 104

k−20 = 1.548 × 10−6

44.784 × 104

Ascorbic acid

Green Bell Peppers

Rahman et al. (2015)

Fresh Capsicum

7–10 time intervals per temp.

0.73

k20 = 6.88 × 10−5

1.01 × 104

(MC = 94 g/100 g)

0.92

k5 = 4.93 × 10−5

1.37

1

0.819

−40–20

1.41 × 104

0.84

k−20 = 5.14 × 10−5

1.35 × 104

0.89

k−40 = 3.33 × 10−5

2.08 × 104

Freeze dried capsicum

(freeze-dried and powdered)

0.93

k60 = 10.3 × 10−5

0.674 × 104

(MC = 15 g/100 g)

0.99

k45 = 5.35 × 10−5

4.91

1

0.925

5–60

1.30 × 104

0.98

k20 = 2.64 × 10−5

2.63 × 104

Assay:

0.90

k5 = 2.36 × 10−5

2.94 × 104

Freeze dried capsicum

Indophenol

0.96

k5 = 0.319 × 10−5

21.7 × 104

(MC = 5 g/100 g)

0.96

k−20 = 0.299 × 10−5

0.47

1

0.986

−40–5

23.2 × 104

0.93

k−40 = 0.271 × 10−5

25.6 × 104

Vitamin C

Infant food (fruit-based)

Bosch et al. (2013)

22% pear puree

Process:

22% tangerine juice

Hot fill > 85°C

0.973

k50 = 18.52 × 10−6

3.742 × 104

16.5% banana puree

130 g pkg. (PP/EVOH)

0.883

k37 = 5.459 × 10−6

20.1

1

0.998

25–50

12.70 × 104

10% carrot puree

Pasteurized

0.960

k25 = 1.345 × 10−6

51.53 × 104

grape juice conc.

Stored up to 32 wks

k4 no loss

4

starch, vitamin C

Assay: Indophenol

Infant formula

[15% protein (milk-based); 24% lipids; 57% carbohydrates; 4% vitamins, minerals, water]

With ferrous sulfate

0.920

k45 = 4.24 × 10−6

1.63 × 105

Galdi et al. (1989)

0.916

k37 = 2.80 × 10−6

9.0

1

0.998

20–45

2.48 × 105

0.954

k20 = 1.25 × 10−6

5.55 × 105

With ferric glycinate

0.959

k45 = 3.24 × 10−6

2.14 × 105

0.932

k37 = 2.04 × 10−6

8.7

1

0.992

20–45

3.40 × 105

0.926

k20 = 0.972 × 10−6

7.13 × 105

Ascorbic acid

Mandarin slices

Deff (m2/s)*

Akdaş and Başlar (2014)

Ascorbic acid

1.37 × 10−7

0.932

k75 = 12.2 × 10−4

569.6

Oven drying

0.758 × 10−7

0.932

k65 = 6.61 × 10−4

10.12

1

0.951

55–75

1048.6

0.353 × 10−7

0.948

k55 = 4.97 × 10−4

1394.7

Ascorbic acid

2.76 × 10−7

0.888

k75 = 8.69 × 10−4

797.6

Vacuum drying

1.61 × 10−7

0.925

k65 = 4.44 × 10−4

12.34

1

0.978

55–75

1561.1

0.952 × 10−7

0.971

k55 = 2.92 × 10−4

2373.8

DPPH**

Assay: Indophenol

0.940

k75 = 5.06 × 10−4

1369.9

(antioxidant capacity)

0.966

k65 = 2.91 × 10−4

8.42

1

0.919

55–75

2381.9

Oven drying

0.957

k55 = 2.40 × 10−4

2888.1

DPPH**

0.907

k75 = 7.37 × 10−4

940.5

Akdaş and Başlar (2014)

(antioxidant capacity)

0.912

k65 = 6.21 × 10−4

13.15

1

0.870

55–75

1116.2

Vacuum drying

0.911

k55 = 2.33 × 10−4

2974.9

*effective moisture diffusivity

**di(phenyl)-(2,4,6-trinitrophenyl) iminoazanium

L-Ascorbic acid

Model system – pH 4.5

0.50 mmol/10 ml buffer

0.951

k150 = 37.9 × 10−3

18.3

Li et al., (2016)

0.992

k140 = 21.4 × 10−3

32.4

0.999

k130 = 11.0 × 10−3

18.59

1

0.99 0

110–150

63.0

15 ml sealed vials

0.999

k120 = 7.80 × 10−3

88.9

Heat:

0.999

k110 = 3.50 × 10−3

198.0

Model system – pH 5.8

Oil bath (110–150°C)

0.999

k150 = 30.7 × 10−3

22.6

10–150 min

0.996

k140 = 16.0 × 10−3

43.3

0.998

k130 = 9.20 × 10−3

18.01

1

0.995

110–150

75.3

0.991

k120 = 5.80 × 10−3

119.5

0.997

k110 = 3.10 × 10−3

223.6

Model system – pH 5.8

Buffers:

0.998

k150 = 12.8 × 10−3

54.2

0.020 M Na2HPO4

0.998

k140 = 9.70 × 10−3

71.5

0.020 M Na2H2PO4

0.995

k130 = 6.70 × 10−3

11.20

1

0.999

110–150

103.5

0.020 M Na2HPO4-Na2H2PO4

0.991

k120 = 4.80 × 10−3

144.4

0.998

k110 = 3.20 × 10−3

216.6

Model system – pH 8.0

0.997

k150 = 22.0 × 10−3

31.5

0.969

k140 = 10.5 × 10−3

66.0

Assay:

0.995

k130 = 6.70 × 10−3

20.31

1

0.995419113

110–150

103.5

HPLC – A243

0.981

k120 = 3.20 × 10−3

216.6

0.992

k110 = 1.70 × 10−3

407.7

Model system – pH 9.5

0.999

k150 = 20.3 × 10−3

34.1

Li et al., (2016)

0.996

k140 = 12.9 × 10−3

53.7

0.997

k130 = 5.50 × 10−3

22.31

1

0.984128498

110–150

126.0

1st-order best fit of 0, 1, 2-order calc.

0.997

k120 = 2.30 × 10−3

301.4

0.992

k110 = 1.50 × 10−3

462.1

Okra

Frozen

k−5 = 12.034 × 10−6

5.760 × 104

Giannakourou et al., (2003)

Blanch: 90°C/2 m

k−10 = 4.912 × 10−6

25.2

1

0.868

−5 to −20

14.112 × 104

IQF: −22°C/2 m

k−15 = 1.933 × 10−6

35.856 × 104

k−20 = 0.729 × 10−6

95.040 × 104

Oranges (fresh squeezed/filtered)

Van den Broeck, et al. (1998)

Thermal treatment:

pH 3.5

0.99

k150 = 0.0967

7.2

In capillary tubes (1.15 × 150 mm) – vac – 0–150 min

0.98

k140 = 0.048

28.1

1

0.998

120–150

14.4

0.98

k130 = 0.0205

33.8

0.99

k120 = 0.0076

91.2

Thermal/pressure treatment:

0.98

k80 = 0.010289

67.4

8500 bar

0.98

k70 = 0.004338

20.1

1

0.999

65–80

159.8

0.3 ml/N2 flush

0.99

k65 = 0.0028967

239.3

Orange juice

Canned

0.98

k26.7 = 68.467 × 10−8

1.012 × 106

Cameron et al. (1955)

0.98

k18.3 = 22.738 × 10−8

24.3

1

0.99

10–26.7

3.048 × 106

0.81

k10 = 6.171 × 10−8

11.232 × 106

Ascorbic acid

Orange juice

Micro Wet Milled

0.97

k37 = 1.18 × 10−5

1.18 × 104

Islam et al. 2017

OJ:MD 30:70

(with pulp)

0.98

k30 = 0.903 × 10−5

4.2

1

0.996

4–37

7.68 × 104

0.99

k4 = 0.639 × 10−5

10.8 × 104

Orange juice

Blended/w/different maltodextrin (MD) ratios

0.98

k37 = 1.60 × 10−5

4.34 × 104

OJ:MD 40:60

0.98

k30 = 1.04 × 10−5

4.3

1

0.903

4–37

6.65 × 104

0.99

k4 = 0.833 × 10−5

8.32 × 104

Orange juice

Vacuum spray-dried

0.98

k37 = 2.01 × 10−5

3.44 × 104

OJ:MD 50:50

0.98

k30 = 1.04 × 10−5

5.8

1

0.837

4–37

6.65 × 104

Analysis: HPLC

0.99

k4 = 0.833 × 10−5

8.32 × 104

Orange juice

UV (A254)

0.95

k37 = 2.29 × 10−5

3.02 × 104

OJ:MD 60:40

0.97

k30 = 1.11 × 10−5

6.6

1

0.861

4–37

6.24 × 104

0.98

k4 = 0.833 × 10−5

8.32 × 104

Ascorbic acid*

Blood orange juice

Pasteurize:

0.982

k37 = 40.8 × 10−5

1.70 × 103

Remini et al. 2015

(Not fortified control)

Oil bath (80°C/2 min)

0.974

k30 = 17.3 × 10−5

12.2

1

0.959

4–37

4.01 × 103

30 ml glass bottles

0.943

k20 = 8.47 × 10−5

8.18 × 103

0.737

k4 = 3.47 × 10−5

20.0 × 103

AA-fortified juice

rapid cool

0.982

k37 = 32.2 × 10−5

2.15 × 103

(100 mg AA/L)

0.960

k30 = 14.3 × 10−5

14.7

1

0.993

4–37

4.84 × 103

Stored dark up to 25 days

0.945

k20 = 7.38 × 10−5

9.39 × 103

0.815

k4 = 1.70 × 10−5

40.7 × 103

AA-fortified juice

Assay: HPLC

0.998

k37 = 30.6 × 10−5

2.27 × 103

(200 mg AA/L)

UV (A254)

0.988

k30 = 14.7 × 10−5

13.3

1

0.986

4–37

4.72 × 103

0.894

k20 = 6.60 × 10−5

10.5 × 103

0.917

k4 = 2.18 × 10−5

31.8 × 103

(Not fortified deaerated)

0.842

k30 = 7.94 × 10−5

-

1

20–30

8.72 × 103

0.777

k20 = 1.27 × 10−5

54.5 × 103

*Similar trends found for color intensity measured at 515 nm

Ascorbic acid

Peaches

Canned

0.97

k26.7 = 56.939 × 10−8

1

26.7

1.217 × 106

Cameron et al. 1955

Peas (sweet)

Canned

0.92

k26.7 = 17.954 × 10−8

3.860 × 106

Cameron et al. 1955

0.98

k18.3 = 10.794 × 10−8

9.0

1

0.99

10–26.7

6.422 × 106

0.82

k10 = 7.386 × 10−8

9.385 × 106

Peas (green)

Frozen

k−5 = 20.060 × 10−6

3.456 × 104

Giannakourou et al. 2003

Blanch: 90°C/2 m

k−10 = 8.596 × 10−6

23.4

1

0.958

−5 to −20

8.064 × 104

IQF: −22°C/2 m

k−15 = 3.647 × 10−6

19.008 × 104

k−20 = 1.481 × 10−6

46.800 × 104

Predicted

Teff = -3.8°C

keff = 24.24 × 10−6

1

−5 to −20

2.86 × 104

Temp. Cycle:

−3°C/72 h

−5°C/24 h

kexp = 21.88 × 10−6

1

0.981

−3 to −8°C

3.17 × 104

−8°C/12 h

Ascorbic acid

Peas (sweet)

Dehydro-frozen (50% H2O)

Neumann et al. 1965 (from Labuza 1972)

Storage losses (in air)

aw = 0.90

k−7 = 3.19 × 10−5

2.17 × 104

aw = 0.90

k−15 = 0.214 × 10−5

46.0

1

−15 to −7

32.39 × 104

Peas (sweet)

Canned

k132.2 = 0.009

77

Lathrop and Leung 1980

k126.7 = 0.0043

161

k121.1 = 0.0025

39.3

1

0.984

110–132

277

k115.6 = 0.0014

495

k110.0 = 0.00046

1,507

Pineapple slices

Canned

0.98

k26.7 = 67.577 × 10−8

1.026 × 106

Cameron et al. 1955

0.87

k18.3 = 26.975 × 10−8

11.5

1

0.88

10–26.7

2.570 × 106

0.70

k10 = 21.456 × 10−8

3.231 × 106

Spinach

Canned

0.81

k26.7 = 17.475 × 10−8

3.967 × 106

Cameron et al. 1955

0.80

k18.3 = 11.574 × 10−8

6.3

1

0.96

10–26.7

5.989 × 106

0.95

k10 = 9.347 × 10−8

7.4165 × 106

Spinach

Frozen

k−5 = 60.169 × 10−6

1.152 × 104

Giannakourou et al. 2003

Blanch: 90°C/2 m

k−10 = 24.068 × 10−6

26.8

1

0.992

−5 to −20

2.880 × 104

IQF: −22°C/2 m

k−15 = 8.752 × 10−6

7.920 × 104

k−20 = 3.146 × 10−6

22.032 × 104

Predicted

Teff = –1.7°C

keff = 119.9 × 10−6

1

−5 to −20

0.578 × 104

Temp. Cycle:

−1°C/72 h

−4°C/24 h

kexp = 113.9 × 1 0−6

1

0.917

−1 to −7°C

0.609 × 104

−7°C/12 h

Sweet potato (flour)

Freeze-dried/ground/rehumidified

Haralampu and Karel 1983

aw = 0.020

0.629

k40 = 1.58 × 10−5

1

40

43.87 × 103

aw = 0.058

0.843

k40 = 2.53 × 10−5

1

40

27.40 × 103

aw = 0.112

0.973

k40 = 2.67 × 10−5

1

40

25.96 × 103

aw = 0.316

0.999

k40 = 6.57 × 10−5

1

40

10.55 × 103

aw = 0.484

0.986

k40 = 10.77 × 10−5

1

40

6.44 × 103

aw = 0.555

0.960

k40 = 16.48 × 10−5

1

40

4.21 × 103

aw = 0.661

0.985

k40 = 27.67 × 10−5

1

40

2.51 × 103

aw = 0.747

1.000

k40 = 31.83 × 10−5

1

40

2.18 × 103

Tomatoes

Canned

0.94

k26.7 = 32.380 × 10−8

2.141 × 106

Cameron et al. 1955

0.70

k18.3 = 9.865 × 10−8

13.3

1

0.82

10–26.7

7.026 × 106

0.70

k10 = 8.578 × 10−8

8.081 × 106

Tomatoes (fresh squeezed/filtered)

Van den Broeck, et al. 1998

Thermal treatment:

pH 4.5

0.99

k150 = 0.0487

14.2

Variety A

In capillary tubes (1.15 × 150 mm) – vac – 0–150 min

0.98

k140 = 0.0245

25.2

1

0.999

120–150

60.3

0.98

k130 = 0.0115

28.3

0.99

k120 = 0.0049

141.5

Thermal/pressure treatment:

0.99

k80 = 0.005744

120.7

8500 bar

0.98

k70 = 0.0032353

17.8

1

0.965

65–80

214.3

0.3 ml/N2 flush

0.98

k65 = 0.001789

387.4

Thermal treatment:

Variety B

0.99

k150 = 0.0864

8

0.99

k140 = 0.0411

27.5

1

0.998

120–150

16.9

0.99

k130 = 0.0183

37.9

0.98

k120 = 0.0071

97.6

Thermal treatment:

Van den Broeck, et al. 1998

Phosphate buffer

pH 4.0

k140 = 0.01302

1

140

53.2

pH 7.0

k140 = 0.0060626

1

140

114.4

pH 8.0

k140 = 0.0022658

1

140

305.4

(0–360 min)

Ascorbic acid

Tomato juice

Heat processed, stored in 8 oz cans + citrate buffer

Lee et al. 1977

pH 3.53

k37.8 = 0.128 × 10−5

4.5

1

10–37.8

54.15 × 104

pH 3.78

k37.8 = 0.158 × 10−5

4.0

1

10–37.8

43.87 × 104

pH 4.06

k37.8 = 0.172 × 10−5

40.30 × 104

k29.4 = 0.147 × 10−5

3.3

1

0.999

10–37.8

47.15 × 104

k18.3 = 0.121 × 10−5

57.28 × 104

k10 = 0.101 × 10−5

68.63 × 104

pH 4.36

k37.8 = 0.158 × 10−5

3.8

1

37.8

43.87 × 104

Tomato juice

Freeze-dried/rehydrated

Riemer and Karel 1977

aw = 0

0.99

k51 = 2.083 × 10−5

18.8

3.32 × 104

0.99

k37 = 0.340 × 10−5

(24.6)a

1

0.971

20–51

20.37 × 104

0.74

k20 = 0.090 × 10−5

77.02 × 104

aw = 0.11

0.99

k51 = 6.944 × 10−5

17.0

0.981 × 104

0.98

k37 = 1.389 × 10−5

(22.3)a

1

0.974

20–51

4.99 × 104

0.95

k20 = 0.410 × 10−5

16.91 × 104

aw = 0.32

0.99

k51 = 19.444 × 10−5

20.3

0.357 × 104

0.99

k37 = 4.861 × 10−5

(20.2)a

1

0.999

20–51

1.43 × 104

0.99

k20 = 0.694 × 10−5

9.99 × 104

aw = 0.57

0.99

k51 = 48.611 × 10−5

16.0

0.143 × 104

0.99

k37 = 13.889 × 10−5

(18.1)a

1

0.997

20–51

0.499 × 104

0.99

k20 = 3.472 × 10−5

2.00 × 104

aw = 0.75

0.98

k51 = 81.944 × 10−5

14.6

0.0846 × 104

0.98

k37 = 38.194 × 10−5

(16.2)a

1

0.986

20–51

0.181 × 104

0.99

k20 = 7.639 × 10−5

0.907 × 104

Tomato paste

Lavelli and Giovanelli 2003

Total ascorbic acid

Storage: 90 d

0.94

k50 = 9.51 × 10−6

7.29 × 104

130 g Al-tubes

0.94

k40 = 6.67 × 10−6

5.84

1

0.985

30–50

10.40 × 104

0.69

k30 = 5.21 × 10−6

(5.64)a

13.31 × 104

Tomato pulp

Storage: 90 d

0.92

k50 = 14.93 × 10−6

4.64 × 104

450 g cans

0.84

k40 = 3.47 × 10−6

25.90

1

0.995

30–50

19.96 × 104

0.77

k30 = 1.04 × 10−6

(25.1)a

66.54 × 104

Tomato pulp/w/XS heat

0.94

k40 = 11.11 × 10−6

1

40

6.24 × 104

Tomato puree

0.94

k40 = 7.43 × 10−6

1

40

9.33 × 104

B-Vitamins

Vitamin B1 (thiamine)

Breakfast cereal model system (Vit. B1)

Packaged in:

TDT cans

aw = 0.10

k45 = 0.00066

1

45

1,050

Dennison, et al. 1977

aw = 0.25

k45 = 0.00091

1

45

762

aw = 0.40

k45 = 0.000675

1

45

103

aw = 0.50

k45 = 0.01101

1

45

63

aw = 0.65

k45 = 0.00867

1

45

80

Breakfast cereal model system

Dennison, et al. 1977

(Vit. B1 + Vit. C + Vit. A)

aw = 0.10

k45 = 0.00014

1

45

4951

aw = 0.25

k45 = 0.00065

1

45

1066

aw = 0.40

k45 = 0.00648

1

45

107

aw = 0.50

k45 = 0.00927

1

45

75

aw = 0.65

k45 = 0.00948

1

45

73

Carrots (puree)

ak149 = 0.1669

4.2

Feliciotti and Esselen, 1957

k139 = 0.0711

9.7

k129 = 0.0285

28.3

1

0.999

109–149

24

k119 = 0.012

58

k109 = 0.0049

141

Vitamin B1

Green beans (puree)

k149 = 0.1744

4.0

Feliciotti and Esselen, 1957

k139 = 0.0717

9.7

k129 = 0.0311

28.6

1

0.999

109–149

22

k119 = 0.0122

57

k109 = 0.0049

141

Meats

Feliciotti and Esselen, 1957

Beef heart (puree)

k149 = 0.2171

3.2

k139 = 0.0161

22.1

1

0.687

109–149

43

k129 = 0.0392

27.9

1

0.999

109–149

18

k119 = 0.0157

(−k139)

44

k109 = 0.0068

102

Beef liver (puree)

k149 = 0.2326

3.0

Feliciotti and Esselen, 1957

k139 = 0.0892

7.8

k129 = 0.0364

28.5

1

0.996

109–149

19

k119 = 0.0147

47

k109 = 0.0067

103

Beef (puree)

k137.8 = 0.03673

19

Mulley, et al. 1975a

k132.2 = 0.02509

28

k126.7 = 0.01436

27.5

1

0.996

121–138

48

k121.1 = 0.00906

77

Lamb (puree)

k149 = 0.1935

3.6

Feliciotti and Esselen, 1957

k139  = 0.0814

8.5

k129 = 0.0377

27.7

1

0.998

109–149

18

k119 = 0.0138

50

k109 = 0.0062

112

Vitamin B1

Pork (puree)

k149 = 0.1693

4.1

Feliciotti and Esselen, 1957

k139 = 0.0717

9.7

k129 = 0.0288

27.4

1

0.998

109–149

24

k119 = 0.0129

54

k109 = 0.0055

126

Vitamin B1

Pork (puree)

ak137.8 = 0.084

8.3

Lenz and Lund, 1977b

k126.7 = 0.0385

25.6

1

0.997

115–138

18

k115.6 = 0.014

50

Pork (puree)

18.4a

1

99–126.5

Greenwood et al. 1944)

Meat loaf

ak98 = 0.002511

276

Skjöldebrand, et al. (1983

(ground meat, potato starch and bread crumbs)

baked in convection oven

k85.5 = 0.001244

27.1

1

0.955

70.5–98

557

k70.5 = 0.000139

4,987

Milk

infant formula [15% protein, (milk-based); 24% lipids; 57% carbohydrates; 4% vitamins, minerals, water]

Galdi et al. (1989)

With ferrous sulfate

0.992

k45 = 2.18 × 10−6

3.18 × 105

0.915

k37 = 1.27 × 10−6

7.6

1

0.950

20–45

5.46 × 105

0.984

k20 = 0.741 × 10−6

9.35 × 105

With ferric glycinate

0.920

k45 = 1.74 × 10−6

3.98 × 105

Galdi et al. (1989)

0.967

k37 = 0.794 × 10−6

10.6

1

0.933

20–45

9.07 × 105

0.920

k20 = 0.370 × 10−6

18.73 × 105

Milk

Pasteurized/UHT stored in 0.5-L glass bottles/dark

k85 = 13.68 × 10−4

507

Fink and Kessler (1985)

(3.5% fat)

k72 = 2.69 × 10−4

30.5

2

0.970

35–85

2,580

k50 = 0.248 × 10−4

27,950

k35 = 0.0396 × 10−4

175,000

Vitamin B1

Kamman et al. (1981)

Pasta (enriched)

conditions

aw = 0.44

0.992

k55 = 1.087 × 10−5

0.638 × 105

0.982

k45 = 0.154 × 10−5

27.7

1

0.976

25–55

4.50 × 105

0.931

k35 = 0.0451 × 10−5

15.37 × 105

0.875

k25 = 0.0138 × 10−5

50.23 × 105

aw = 0.54

0.995

k55 = 1.406 × 10−5

0.493 × 105

0.994

k45 = 0.267 × 10−5

29.0

1

0.996

25–55

2.60 × 105

0.979

k35 = 0.066 × 10−5

10.50 × 105

0.828

k25 = 0.0153 × 10−5

45.30 × 105

aw = 0.65

0.995

k55 = 1.809 × 10−5

0.383 × 105

Kamman et al. (1981)

0.997

k45 = 0.435 × 10−5

24.2

1

0.992

25–55

1.59 × 105

0.983

k35 = 0.127 × 10−5

5.46 × 105

0.941

k25 = 0.0424 × 10−5

16.35 × 105

Square-wave fluctuations

aw = 0.44

25–55°C

0.930

k = 0.463 × 10−5

1

25–55

1.50 × 105

25–45°C

0.973

k = 0.0807 × 10−5

1

25–45

8.59 × 105

aw = 0.54

25–55°C

0.954

k = 0.589 × 10−5

1

25–55

1.18 × 105

25–45°C

0.960

k = 0.132 × 10−5

1

25–45

5.25 × 105

aw = 0.65

25–55°C

0.989

k = 0.194 × 10−5

1

25–55

3.57 × 105

25–45°C

0.989

k = 0.882 × 10−5

1

25–45

0.786 × 105

Peas

Bendix et al. (1951)

Brine packed

ak132.2 = 0.0351

20

(whole)

k126.7 = 0.0286

20.5

1

0.976

104–132

24

k118.3 = 0.0122

57

k104.4 = 0.0058

120

Vacuum packed

k132.2 = 0.0351

20

Bendix et al. (1951)

(whole)

k126.7 = 0.0226

21.9

1

0.996

104–132

31

k118.3 = 0.0142

49

k104.4 = 0.0046

151

Vitamin B1

Peas (puree)

k149 = 0.1659

4.2

Feliciotti and Esselen, (1957)

k139 = 0.0708

9.8

k129 = 0.0276

28.1

1

0.998

109–149

25

k119 = 0.0114

61

k109 = 0.0051

136

Peas (puree)

ak137.8 = 0.0435

16

Lenz and Lund (1977b)

k126.7 = 0.021

23.2

1

0.998

115–138

33

k115.6 = 0.0086

81

Peas (puree)

k137.8 = 0.03757

18

Mulley et al. (1975a)

k132.2 = 0.02206

27.8

1

0.964

121–138

31

k126.7 = 0.01164

60

k121.1 = 0.009328

74

Peas (puree, in brine)

k137.8 = 0.03897

18

k132.2 = 0.03026

27.1

1

0.977

121–138

23

k126.7 = 0.01584

44

k121.1 = 0.01016

68

Spinach (puree)

k149 = 0.228

3.0

Feliciotti and Esselen, (1957)

k139 = 0.0825

8.4

k129 = 0.0336

28.2

1

0.993

109–149

21

k119 = 0.0143

48

k109 = 0.0067

103

Vitamin B2 (Riboflavin)

Packaged in TDT cans

Dennison, et al. (1977)

aw = 0.10

a,bk37 = 0.00023

1

37

3,010

aw = 0.25

k37 = 0.00188

1

37

369

aw = 0.40

k37 = 0.00263

1

37

264

aw = 0.50

k37 = 0.00411

1

37

169

aw = 0.65

k37 = 0.00503

1

37

138

Breakfast cereal

Packaged in paperboard boxes

Dennison, et al. (1977)

aw = 0.10

k30 = 0.0044

1

30

158

aw = 0.40

k30 = 0.0043

1

30

161

aw = 0.85

k30 = 0.0043

1

30

161

Vitamin B2

Macaroni

Woodcock et al. (1982)

Light exposure (lumens/m2)

First phase

27.87

aw = 0.32

a,bk55 = 1.38

0.502

k25 = 1.26

0.550

27.87

aw = 0.44

k55 = 1.32

0.525

k35 = 1.52

0.546

k25 = 1.56

0.444

18.58

aw = 0.44

k55 = 1.72

0.403

k35 = 1.27

0.6–2.0a

1

25–55

0.546

k25 = 1.0

0.533

9.29

aw = 0.44

k55 = 1.32

0.525

k35 = 1.53

0.453

k25 = 1.37

0.506

Second phase

27.87

aw = 0.32

k55 = 0.55

1.260

k25 = 0.78

0.889

27.87

aw = 0.44

k55 = 0.87

0.797

k35 = 0.55

1.260

k25 = 0.57

1.2 20

18.58

aw = 0.44

k55 = 1.0

0.630

k35 = 0.85

1.9–4.3a

1

25–55

0.815

k25 = 0.84

0.825

9.29

aw = 0.44

k55 = 1.33

0.521

k35 = 0.0

0.866

k25 = 0.0

0.990

Vitamin B2

Macaroni

Light exposure = 150 ft-c at 4°C

Furuya et al. (1984)

Whole

First phase

0.83

k4 = 0.326 × 10−3

1

4

2.13 × 103

Second phase

0.80

k4 = 0.764 × 10−5

1

4

90.73 × 103

Particulate

First phase

0.88

k4 = 0.386 × 10−3

1

4

1.80 × 103

Second phase

0.75

k4 = 1.181 × 10−5

1

4

58.69 × 103

Milk

Galdi et al. (1989)

Infant formula

Flexible plastic containers

[15% protein (milk-based); 24% lipids; 57% carbohydrates; 4% vitamins, minerals, water]

With ferrous sulfate

0.952

k45 = 3.06 × 10−6

2.27 × 105

0.939

k37 = 2.50 × 10−6

8.0

1

0.986

20–45

2.77 × 105

0.930

k20 = 1.07 × 10−6

6.48 × 105

With ferric glycinate

0.968

k45 = 2.186 × 10−6

3.18 × 105

0.796

k37 = 0.949 × 10−6

27.0

1

0.994

20–45

7.30 × 105

0.907

k20 = 0.058 × 10−6

120 × 105

Milk (whole)

Container type:

Singh et al. (1975)

(1-gal samples)

Glass

Light intensity:

150 ft-c

k10 = 1.83 × 10−5

37.88 × 103

k4.4 = 1.46 × 10−5

6.6

1

0.098

1.7–10

47.48 × 103

k1.7 = 1.28 × 10−5

54.15 × 103

300 ft-c

k10 = 5.37 × 10−5

12.91 × 103

k4.4 = 4.04 × 10−5

8.1

1

0.999

1.7–10

17.16 × 103

k1.7 = 3.48 × 10−5

19.92 × 103

450 ft-c

k10 = 5.98 × 10−5

20.8

1

4.4–10

11.52 × 103

k4.4 = 5.55 × 10−5

18.02 × 103

Vitamin B2

Blow-molded poly-ethylene (BMP)

Singh et al. (1975)

Light intensity:

150 ft-c

k10 = 1.76 × 10−5

3.94 × 104

k4.4 = 0.998 × 10−5

16.2

1

0.999

1.7–10

6.95 × 104

k1.7 = 0.735 × 10−5

9.43 × 104

300 ft-c

k10 = 5.19 × 10−5

1.34 × 104

k4.4 = 3.79 × 10−5

11.4

1

0.962

1.7–10

1.83 × 104

k1.7 = 2.75 × 10−5

2.52 × 104

450 ft-c

k10 = 8.75 × 10−5

11.9

1

4.4–10

0.792 × 104

k4.4 = 5.70 × 10−5

1.22 × 104

Gold-pigmented

BMP

Light intensity:

150 ft-c

k10 = 0.655 × 10−5

10.58 × 104

k4.4 = 0.570 × 10−5

11.6

1

0.749

1.7–10

12.16 × 104

k1.7 = 0.327 × 10−5

21.20 × 104

300 ft-c

k10 = 1.53 × 10−5

4.53 × 104

k4.4 = 1.16 × 10−5

20.5

1

0.773

1.7–10

5.98 × 104

k1.7 = 0.453 × 10−5

15.30 × 104

450 ft-c

k10 = 0.920 × 10−5

15.5

1

4.4–10

7.53 × 104

k4.4 = 0.527 × 10−5

13.15 × 104

Paperboard

Light intensity:

150 ft-c

k10 = 0.453 × 10−5

15.30 × 104

k4.4 = 0.648 × 10−5

14.2

1

0.313

1.7–10

10.70 × 104

k1.7 = 0.170 × 10−5

40.77 × 104

300 ft-c

k10 = 1.75 × 10−5

3.96 × 104

k4.4 = 1.547 × 10−5

49.8

1

0.916

1.7–10

12.67 × 104

k1.7 = 0.103 × 10−5

67.30 × 104

450 ft-c

k10 = 0.302 × 10−5

23.0

1

4.4–10

22.95 × 104

k4.4 = 0.690 × 10−5

10.05 × 104

Vitamin B2

Milk (skim)

Light exposure = 150 ft-c at 4°C

Furuya et al. (1984)

Liquid

First phase

0.97

k4 = 0.834 × 10−3

1

4

831

Milk (nonfat dry milk powder)

First phase

0.90

k4 = 0.190 × 10−3

1

4

3,648

Second phase

0.68

k4 = 2.50 × 10−3

1

4

27,730

Vitamin B3/Niacin

Nisha et al. (2009)

Potatoes

10 g potato cubes (1 cm3)/30 g water in 100 ml beaker

0.995

k120 = 3.10 × 10−3

224

0.987

k110 = 2.70 × 10−3

257

Heat:

0.992

k100 = 2.50 × 10−3

277

waterbath 50–100°C

0.988

k90 = 2.00 × 10−3

4.2

1

0.992

50–120

347

autoclave 110–120°C

0.989

k80 = 1.60 × 10−3

433

Time: 0–60 min

0.989

k70 = 1.40 × 10−3

495

0.994

k60 = 1.20 × 10−3

578

0.991

k50 = 1.00 × 10−3

693

Niacin solutions

1 ml niacin soln. (900 mg/100 ml water)

0.988

k120 = 3.40 × 10−3

204

0.985

k110 = 3.10 × 10−3

224

0.991

k100 = 2.70 × 10−3

257

0.994

k90 = 2.20 × 10−3

4.3

1

0.994

50–120

315

0.986

k80 = 1.80 × 10−3

385

0.990

k70 = 1.50 × 10−3

462

0.990

k60 = 1.30 × 10−3

533

0.987

k50 = 1.10 × 10−3

630

Nicotinic acid

Averrhoa bilimbi fruit

Muhamed et al. (2015)

Process:

extract

k120 = 7.22 × 10−2

9.6

extract

k100 = 2.57 × 10−2

8.8

1

0.995

90–120

27.0

extract

k90 = 3.11 × 10−2

22.3

Assay: UHPLC-QTOF

pure solution

k90 = 2.50 × 10−2

90

27.7

Vitamin B6

Breakfast cereal model system

Toasted

ak200 = 0.4895

1.4

Evans et al. (1981)

k185 = 0.1688

30.0

1

0.999

155–200

4.1

Pyridoxine

k170 = 0.0522

13

k155 = 0.0174

40

Vitamin B6

Casein-based liquid model system

Gregory and Hiner (1983)

Pyridoxine

0.98

k133 = 0.0083

84

0.98

k118 = 0.0025

28.6

1

0.995

105–133

277

0.98

k105 = 0.0006

1155

Pyridoxamine

0.99

k133 = 0.0187

37

Gregory and Hiner (1983)

0.98

k118 = 0.0064

23.8

1

0.999

105–133

108

0.98

k105 = 0.0021

330

Pyridoxal

0.94

k133 = 0.0266

26

0.98

k118 = 0.0092

20.7

1

0.998

105–133

75

0.96

k105 = 0.004

173

Cauliflower (puree)

ak137.7 = 0.01145

61

Navankasattusas and Lund (1982)

Overall B6

k125.6 = 0.00652

13.8

1

0.990

105.9–137.7

106

k114.6 = 0.00453

(27 + 2)a

pseudo-1a

153

k105.9 = 0.00265

262

Vitamin B12

Milk (cow)

Watanabe et al. (1998)

Conventional boil 30 min/CUT = ~7.5 min

0.990

k100 = 0.018279

1

100

38

Microwave 6 min/CUT = ~ 2 min

0.999

k100 = 0.091545

1

100

7.6

Folates

Folic acid

Apple juice

pH 3.4

ak140 = 0.0137

51

Mnkeni and Beveridge (1982)

Method: L. casei

k130 = 0.00792

88

k121 = 0.00468

19.9

1

0.998

100–140

148

k110 = 0.00202

343

k100 = 0.00105

660

Folic acid

Tomato juice

pH 4.3

k140 = 0.01205

58

Mnkeni and Beveridge (1982)

k130 = 0.006567

106

k121 = 0.003417

19.9

1

0.996

100–140

203

k110 = 0.001733

400

k100 = 0.0009333

743

Citrate buffer

pH 3.0

k140 = 0.03933

18

k130 = 0.02767

25

k121 = 0.0124

22.4

1

0.989

100–140

56

k110 = 0.004883

142

k100 = 0.002417

287

pH 4.0

k140 = 0.014

50

k130 = 0.009667

72

k121 = 0.0039

19.4

1

0.982

100–140

178

k110 = 0.002133

325

k100 = 0.001233

562

Folic acid

pH 5.0

k140 = 0.00345

201

Mnkeni and Beveridge (1982)

k130 = 0.001867

371

k121 = 0.0008833

17.8

1

0.975

100–140

785

k110 = 0.0005167

1,341

k100 = 0.00035

1,980

Folic acid

Phosphate buffer

Nguyen et al. (2003)

pH 7.0

Capillary tubes:

k160 = 0.00473

147

HPLC

1.5 × 150 mm

k140 = 0.00126

a12.35

1

0.8

120–160

550

k120 = 0.00104

666

Folic acid

Model system (solid)

Hawkes and Villota (1989a)

Avicel/glycerol

Heat in 6 × 50 mm glass tubes

(60:40)

Initial folate conc. = 0.2 mg/g solids

MC = g H2O/100 g solids

6.8

0.997

k80 = 0.0005109

1,357

0.994

k70 = 0.0003386

9.47

1

0.998

50–80

2,047

0.961

k60 = 0.0002298

3,016

0.984

k50 = 0.0001436

4,827

11.0

0.986

k80 = 0.000521

1,330

0.977

k70 = 0.0003386

9.39

1

0.998

50–80

2,047

Cold extraction – pH 8.0 phos. Buffer

0.998

k60 = 0.0002365

2,931

0.952

k50 = 0.0001475

4,699

Assay: HPLC

18.0

0.995

k80 = 0.0006502

1,066

Detector: UV/VIS

0.990

k70 = 0.0004199

8.65

1

0.995

50–80

1,651

A280

0.990

k60 = 0.0003033

2,285

0.997

k50 = 0.0002025

3,423

27.0

0.993

k80 = 0.0007266

954

0.993

k70 = 0.0005189

8.82

1

0.998

50–80

1,336

0.984

k60 = 0.0003357

2,065

0.999

k50 = 0.0002294

3,022

40.0

0.998

k80 = 0.0008392

826

0.998

k70 = 0.0005628

9.66

1

0.996

50–80

1,232

0.992

k60 = 0.0003463

2,002

0.998

k50 = 0.0002379

2,914

Folic acid

Strawberries (fresh/whole – Zephyr)

Strålsjö et al. (2003)

Total folate

Stored: 0–9 days

0.998

k4 = 0.000025744

1

4

26,925

Open air

0.996

k20 = 0.00010381

1

20

6,677

Swiss chard

Storage conditions:

Gami and Chen (1985)

Plastic bag

0.998

k21 = 0.000135

1

21

5,134

Moist conditions

0.996

k21 = 0.000149

1

21

4,652

Open air

k40 = 0.001645

421

k35 = 0.001042

22.9

1

0.996

4–40

665

k21 = 0.000205

3,381

k4 = 0.00001433

48,370

5-Methyltetrahydrofolate

Apple juice

pH 3.4

0.97

k70 = 0.249

2.8

Mnkeni and Beveridge (1983)

Method: L. casei

(unlimited O2)

0.95

k60 = 0.193

7.9

1

0.99

50–70

3.6

0.96

k50 = 0.123

5.6

pH 3.4

0.89

k70 = 0.00

3.5

(limited O2, at 5.3 ppm)

0.92

k60 = 0.126

9.5

1

0.98

50–70

5.5

0.99

k50 = 0.089

7.8

Tomato juice

pH 4.3

0.93

k130 = 1.065

0.65

(unlimited O2)

0.92

k121 = 0.792

10.6

1

0.99

100–130

0.88

0.95

k110 = 0.508

1.4

0.99

k100 = 0.374

1.9

pH 4.3

0.98

k130 = 0.488

1.4

(limited O2, at 5.3 ppm)

0.95

k121 = 0.353

10.8

1

0.99

100–130

2.0

0.97

k110 = 0.259

2.7

0.98

k100 = 0.16

4.3

5-Methyltetrahydrofolate

Model system (aqueous solutions)

Hawkes and Villota (1989a)

Glycerol/water

Heat in 6  × 50 mm glass tubes

Initial folate conc. = 0.1 mg/g solids

g glycerol/g water

HPLC

0.996

k85 = 0.00645

107.5

0

0.998

k80 = 0.005

10.8

1

0.989

75–85

138.6

0.993

k75 = 0.00417

166.2

0.991

k85 = 0.00552

125.6

0.0526

0.998

k80 = 0.00378

17.2

1

0.996

183.4

0.995

k75 = 0.00276

251.1

g glycerol/g water

0.991

k85 = 0.00483

143.5

0.1100

0.999

k80 = 0.00338

17.0

1

0.999

205.1

0.989

k75 = 0.00243

285.2

0.989

k85 = 0.0041

169.1

0.2500

0.998

k80 = 0.0028

17.1

1

0.996

247.6

0.989

k75 = 0.00205

338.1

5-Methyltetrahydrofolate

Phosphate buffer

Micro-scale

Viberg et al. (1997)

UHT simulation

pH 7.0

anaerobic

O2 at 0.3 ppm

k150 = 1.63

0.425

k140 = 1.12

14.9

1

0.989

110–150

0.619

k120 = 0.488

1.420

k110 = 0.242

2.864

aerobic

O2 at 6.8 ppm

k150 = 3.18

0.218

k140 = 1.65

18.3

1

0.994

110–150

0.132

k120 = 0.638

(25.5)

(2)

0.998

0.207

k110 = 0.3

0.690

Folates – L-5-Methyltetrahydrofolic acid

Phosphate buffer

Capillary tubes:

Nguyen et al. (2003)

1.5 × 150 mm

k90 = 0.06831

10.2

pH 7.0

k80 = 0.02814

19.1

1

0.993

65–90

24.6

k70 = 0.01306

53.1

k65 = 0.00973

71.2

L-5-Methyltetrahydrofolic acid

Model system – pH 4.0

Liu et al. (2012)

50 μg/ml in 0.1M acetate buffer

Heat: Waterbath (21–75°C)

k75 = 22.5 × 10−3

30.8

k50 = 8.20 × 10−3

22.94

1

0.911

21–75

84.5

k37 = 1.20 × 10−3

577.6

k21 = 0.050 × 10−3

13862.9

Model system – pH 6.9

Analysis: HPLC

k75 = 210.5 × 10−3

3.3

50 μg/ml in 0.1M phosphate buffer

k50 = 30.9 × 10−3

17.37

1

0.972

21–75

22.4

k37 = 5.46 × 10−3

127.0

Fluorescence (Ex: A290/Em: A365)

k21 = 2.50 × 10−3

277.3

NaAsc*:L-5-CH3THF

0:1

Heat: Waterbath (50°C)

k50 = 33.7 × 10−3

20.5

Liu et al. (2012)

100:1

k50 = 9.80 × 10−3

70.7

500:1

k50 = 4.80 × 10−3

1

50

144.4

2000:1

k50 = 2.10 × 10−3

330.1

5000:1

k50 = 0.20 × 10−3

3465.7

* Sodium ascorbate

Pantothenic acid

Averrhoa bilimbi fruit

Muhamed et al. (2015)

Process:

extract

k120 = 8.37 × 10−2

8.3

extract

k100 = 2.31 × 10−2

11.2

1

0.994

90–120

30.0

extract

k90 = 2.88 × 10−2

24.1

Assay: UHPLC-QTOF

pure solution

k90 = 2.03 × 10−2

90

34.1

Catechin

Averrhoa bilimbi fruit

Muhamed et al. (2015)

Process:

extract

k120 = 8.69 × 10−2

8.0

extract

k100 = 7.07 × 10−2

1.3

1

0.999

90–120

9.8

Assay: UHPLC-QTOF

extract

k90 = 7.79 × 10−2

8.9

pure solution

k90 = 4.92 × 10−2

90

14.1

Pantothenic acid (PA)

Meat puree (beef)

Hamm and Lund (1978)

Free PA

pH 5.4

a20

1

118–143

Total PA

pH 5.4

25

1

118–143

Pea puree

Free PA

pH 7.0

38

1

118–143

Total PA

pH 7.0

36

1

118–143

Buffered PA

pH 4.0

20

1

118–143

pH 5.0

22

1

118–143

pH 6.0

27

1

118–143

Fat Soluble Vitamins:

Vitamin A

Beef liver puree

Trans-retinol

k126.7 = 0.09738

26.9

7.1

Wilkinson et al. (1981)

k122.1 = 0.05766

12

k118.3 = 0.0408

1

0.995

102.9–126.7

17

k111.0 = 0.02316

30

k102.9 = 0.01074

65

(% fat/protein/moisture/ash + carbohydrate)

Sample 1:

10.9/21.6/63.2/4.3

a0.992

k122 = 0.006162

26.4

112

0.993

k112 = 0.002694

1

0.999

102–122

257

0.986

k102 = 0.001026

676

Sample 2:

27.9/11.7/56.0/4.4

0.993

k122 = 0.00348

199

0.984

k112 = 0.001392

24.2

1

0.993

102–122

498

0.990

k102 = 0.000672

1,031

Sample 3:

10.0/13.0/72.2/4.8

0.996

k122 = 0.007704

23.9

90

Wilkinson et al. (1982)

0.997

k112 = 0.003696

1

0.998

102–122

188

0.999

k102 = 0.001524

455

Sample 4:

26.1/20.2/51.7/2.0

0.979

k122 = 0.002718

27.0

255

0.925

k112 = 0.00078

1

0.953

102–122

889

0.998

k102 = 0.000432

1,605

(15% protein/17–30% fat)

ppm Cu + 2/pH/% moisture

Sample 1:

6/5.6/55

a0.999

k122 = 0.00273

a21.8

1

102–122

254

0.974

k102 = 0.000666

1,041

Sample 2:

30/5.6/55

0.994

k122 = 0.001213

8.6

1

102–122

571

0.994

k102 = 0.000774

896

Sample 3:

6/7.0/55

0.975

k122 = 0.002781

28.0

1

102–122

255

0.980

k102 = 0.000498

1,392

Sample 4:

30/7.0/55

0.971

k122 = 0.001368

8.6

1

102–122

507

0.998

k102 = 0.000666

1,041

Sample 5:

6/5.6/68

0.998

k122 = 0.01102

28.2

1

102–122

63

0.996

k102 = 0.00165

420

Sample 6:

30/5.6/68

0.999

k122 = 0.00921

23.2

1

102–122

75

0.988

k102 = 0.00227

305

Sample 7:

6/7.0/68

0.998

k122 = 0.01069

28.9

1

102–122

65

0.996

k102 = 0.001542

450

Sample 8:

30/7.0/68

0.993

k122 = 0.005028

17.2

1

102–122

138

0.999

k102 = 0.001458

475

Vitamin A

Carotene (crystalline)

Heated dry in 2-ml glass vials (50–150°C/10–30 min)

Chen et al. (1994)

All-trans-α-Carotene

⇆ 13-cis-α-carotene

Forward reaction (k1)

HPLC analysis dissolved in hexane for analysis

0.994

ak150 = 0.0323→

1

150

Reverse reaction (k−1)

k150 = 0.0998←

1

150

⇆ 9-cis-α-carotene

Forward reaction (k1)

0.980

k150 = 0.0038→

1

150

Reverse reaction (k−1)

k150 = 0.0449←

1

150

⇆ 15-cis-α-carotene

Forward reaction (k1)

0.990

k150 = 0.0067→

1

150

Reverse reaction (k−1)

k150 = 0.0407←

1

150

All-trans-β-Carotene

Chen et al. (1994)

⇆ 13-cis-β-carotene

Forward reaction (k1)

0.996

k150 = 0.0125→

1

150

Reverse reaction (k−1)

dissolved in MeOH: CHCl3 (45:55) for analysis

k150 = 0.0339←

1

150

⇆ 9-cis-β-carotene

Forward reaction (k1)

0.990

k150 = 0.0043→

1

150

Reverse reaction (k−1)

k150 = 0.0184←

1

150

Vitamin A

Corn flakes (fortified)

Storage:

Kim et al. (2000)

Vitamin A palmitate

23–45°C, up to 16 wks

0.960

ak45 = 1.2639 × 105

apparent-2

23–45

5.484 × 104

(15% RDI)

0.954

k23 = 1.0208 × 105

apparent-2

23–45

6.790 × 104

Vitamin A palmitate

Packaging: cardboard box with plastic liner

0.960

k45 = 17.5694 × 105

apparent-1

23–45

0.395 × 104

(plus B1, B6, B12, C, D2 & D3 – 15% RDI each)

0.793

k23 = 4.5139 × 105

apparent-1

23–45

1.536 × 104

Carotenoids

β-Cryptoxanthin

Solutions:

0.9997

k80 = 14.4 × 10−3

48.0

Mitra et al. (2016)

(extracted from

50:50 EtOH:Water

0.9970

k70 = 11.1 × 10−3

62.4

Kocuria marina)

Time: 9 hrs

0.9961

k60 = 9.49 × 10−3

73.1

0.9933

k50 = 8.60 × 10−3

3.2

1

0.952

25–80

80.6

Fluorescent light

0.9958

k40 = 7.43 × 10−3

93.3

Analysis: UV-VIS

0.9996

k37 = 6.50 × 10−3

106.6

(A445)

0.9991

k25 = 6.22 × 10−3

111.5

β-Cryptoxanthin

Effect of pH:

1 ml sample/3 ml buffer

0.9988

k25 = 4.15 × 10−4

1670.2

0.9985

k25 = 2.75 × 10−4

2520.5

0.9999

k25 = 2.45 × 10−4

1

25

2829.2

0.9997

k25 = 2.52 × 10−4

2754.2

0.9996

k25 = 1.37 × 10−4

5071.8

0.9992

k25 = 1.03 × 10−4

6707.9

Vitamin A

Liu et al. (2015)

Emulsions (O/W/w/β-Carotene)

Control

Composition:

k65 = 34.2 × 10−5

0.203 × 10−4

(no antioxidants)

Gum arabic

k45 = 6.68 × 10−5

5.1

1

0.679

4–65

1.04 × 10−4

Carotene

k25 = 6.63 × 10−5

1.05 × 10−4

Antioxidant:

Antioxidant

k4 = 4.92 × 10−5

1.41 × 10−4

0.01% Ascorbyl Palmitate

k65 = 15.5 × 10−5

0.446 × 10−4

k45 = 3.67 × 10−5

7.8

1

0.874

4–65

1.89 × 10−4

k25 = 1.51 × 10−5

4.60 × 10−4

k4 = 1.15 × 10−5

6.04 × 10−4

0.05% Ascorbyl Palmitate

k65 = 15.2 × 10−5

0.455 × 10−4

k45 = 2.47 × 10−5

10.9

1

0.954

4–65

2.81 × 10−4

k25 = 1.26 × 10−5

5.52 × 10−4

k4 = 0.347 × 10−5

20.0 × 10−4

0.10% Ascorbyl Palmitate

k65 = 15.9 × 10−5

0.436 × 10−4

Liu et al. (2015)

k45 = 3.60 × 10−5

12.2

1

0.988

4–65

1.93 × 10−4

k25 = 0.965 × 10−5

7.18 × 10−4

k4 = 0.278 × 10−5

24.9 × 10−4

0.01% α-Tocopherol

k65 = 13.2 × 10−5

0.524 × 10−4

k45 = 2.35 × 10−5

9.6

1

0.937

4–65

2.95 × 10−4

k25 = 1.38 × 10−5

5.02 × 10−4

k4 = 0.458 × 10−5

15.1 × 10−4

0.05% α-Tocopherol

k65 = 9.07 × 10−5

0.765 × 10−4

k45 = 1.70 × 10−5

10.1

1

0.955

4–65

4.08 × 10−4

k25 = 0.993 × 10−5

6.98 × 10−4

k4 = 0.263 × 10−5

26.3 × 10−4

0.10% α-Tocopherol

k65=9.53 × 10−5

0.727 × 10−4

k45 = 2.45 × 10−5

12.3

1

0.996

4–65

2.83 × 10−4

k25 = 0.847 × 10−5

8.19 × 10−4

k4 = 0.153 × 10−5

45.4 × 10−4

0.01% TBHQ

k65 = 23.3 × 10−5

0.297 × 10−4

k45 = 3.82 × 10−5

1

4–65

1.82 × 10−4

k25 = 3.45 × 10−5

2.01 × 10−4

k4 = 2.00 × 10−5

3.47 × 10−4

Antioxidant:

0.05% TBHQ

k65 = 18.7 × 10−5

0.371 × 10−4

k45 = 2.45 × 10−5

1

4–65

2.83 × 10−4

k25 = 1.61 × 10−5

4.32 × 10−4

k4 = 0.355 × 10−5

19.5 × 10−4

0.10% TBHQ

k65 = 13.4 × 10−5

0.519 × 10−4

k45 = 2.33 × 10−5

1

4–65

2.97 × 10−4

k25 = 1.21 × 10−5

5.74 × 10−4

k4 = 0.257 × 10−5

27.0 × 10−4

Vitamin A

Enteral feeding formula (5.5% protein, 3.6% lipid, 11.4% CHO)

Frias and Vidal-Valverde (2001)

Reconstitution; presterilization (UHT: 136°C/3–4 s); homogenize; add vitamins; pack in glass; sterilize (118°C/9 min); store 4–30°C/0–9 mo.

All-trans-retinol

0.780

k30 = 5.628 × 10−6

1.23 × 105

0.840

k20 = 5.859 × 10−6

1.06

1

0.708

4–30

1.18 × 105

0.829

k4 = 4.849 × 10−6

1.43 × 105

13-cis-retinol

0.945

k30 = 3.853 × 10−6

1.80 × 105

0.881

k20 = 2.766 × 10−6

2.47

1

0.784

4–30

2.51 × 105

0.809

k4 = 2.538 × 10−6

2.73 × 105

Vitamin A activity

0.831

k30 = 11.21 × 10−6

0.619 × 105

Frias and Vidal-Valverde (2001)

0.844

k20 = 5.581 × 10−6

5.19

1

0.783

4–30

1.24 × 105

0.830

k4 = 4.664 × 10−6

1.49 × 105

Milk/infant (stored 12 months)

Liquid: 115°C/15 min sealed in brown glass jars

k37 = 7.87 × 10−7

1

37

8.81 × 105

Powdered: 40% conc./60°C; 70–72°C/15 sec; spray dry 80°C; agglom.

k37 = 9.44 × 10−7

1

37

7.34 × 105

Vitamin A

Galdi et al. (1989)

Milk

infant formula [15% protein, (milk-based); 24% lipids; 57% carbohydrates; 4% vitamins, minerals, water]

With ferrous sulfate

0.978

k45 = 2.92 × 10−6

2.37 × 105

0.993

k37 = 2.36 × 10−6

7.6

1

0.991

20–45

2.94 × 105

0.935

k20 = 1.07 × 10−6

6.48 × 105

With ferric glycinate

0.991

k45 = 2.39 × 10−6

2.90 × 105

0.970

k37 = 1.18 × 10−6

8.5

1

0.909

20–45

5.87 × 105

0.929

k20 = 0.694 × 10−6

9.99 × 105

Butternut squash

Carotene

ak80 = 0.0011

11.8

630

Stefanovich and Karel (1982)

k70 = 0.0007

(13.5)a

1

0.998

60–80

990

k60 = 0.0004

1,733

Yellow corn

Carotene

k80 = 0.00135

20.5

513

k70 = 0.00023

(4.8)a

1

0.735

60–80

3,014

k60 = 0.00023

3,014

Model system

Avicel-PH102/

k80 = 0.0239

21.8

29

β-carotene

k70 = 0.0119

(21.7)a

pseudo-1

0.984

60–80

58

k60 = 0.0037

187

Sweet potato

k80 = 0.00116

10.5

598

k70 = 0.00061

(10.6)a

1

0.936

60–80

1,136

k60 = 0.00047

1,475

Sweet potato

Freeze-dried/ground/rehumidified

Haralampu and Karel (1983)

β-carotene: test 1

g H2O/g solids

aw = 0.020

0.008

0.978

k40 = 1.01 × 10−4

pseudo-1

40

6.88 × 103

aw = 0.058

0.026

0.999

k40 = 0.638 × 10−4

pseudo-1

40

10.86 × 103

aw = 0.112

0.034

0.993

k40 = 0.570 × 10−4

pseudo-1

40

12.16 × 103

aw = 0.316

0.068

0.993

k40 = 0.423 × 10−4

pseudo-1

40

16.39 × 103

aw = 0.484

0.085

0.992

k40 = 0.382 × 10−4

pseudo-1

40

18.15 × 103

aw = 0.555

0.101

0.996

k40 = 0.313 × 10−4

pseudo-1

40

22.15 × 103

aw = 0.661

0.116

0.981

k40 = 0.347 × 10−4

pseudo-1

40

19.98 × 103

aw = 0.747

0.204

0.995

k40 = 0.320 × 10−4

pseudo-1

40

21.66 × 103

β-carotene: test 2

g H2O/g solids

aw = 0.020

0.030

0.994

k40 = 2.15 × 10−4

pseudo-1

40

3.22 × 103

aw = 0.058

0.058

0.996

k40 = 1.16 × 10−4

pseudo-1

40

5.98 × 103

aw = 0.112

0.064

0.998

k40 = 0.789 × 10−4

pseudo-1

40

8.79 × 103

aw = 0.316

0.094

0.997

k40 = 0.653 × 10−4

pseudo-1

40

10.61 × 103

aw = 0.484

0.110

0.994

k40 = 0.577 × 10−4

pseudo-1

40

12.01 × 103

aw = 0.555

0.133

0.972

k40 = 0.417 × 10−4

pseudo-1

40

16.62 × 103

aw = 0.661

0.147

0.999

k40 = 0.368 × 10−4

pseudo-1

40

18.84 × 103

aw = 0.747

0.190

0.995

k40 = 0.423 × 10−4

pseudo-1

40

16.39 × 103

Vitamin A

Tomato paste

Lavelli and Giovanelli (2003)

(reported as reduction in total antioxidant activity – β-carotene and lycopene)

Storage 0–90 days

0.98

k50 = 7.43 × 10−6

1.35 × 105

0.91

k40 = 5.07 × 10−6

4.97

1

0.917

30–50

1.37 × 105

0.89

k30 = 4.44 × 10−6

(4.83)a

1.56 × 105

Tomato pulp

0.96

k50 = 5.28 × 10−6

1.31 × 105

0.89

k40 = 3.13 × 10−6

5.49

1

0.795

30–50

2.22 × 105

0.94

k30 = 2.99 × 10−6

(5.31)a

2.32 × 105

Tomato pulp/w/XS heat

0.97

k40 = 3.33 × 10−6

1

40

2.08 × 105

Tomato puree

0.980

k40 = 2.71 × 10−6

1

40

2.56 × 105

Carotenoids

Tomato (Lycopersicum esculentum)

Demiray et al. (2013)

β-Carotene

Process:

Tomatoes quartered

k100 = 6.35 × 10−3

109.2

50 kg/batch

k90 = 5.15 × 10−3

134.7

Tray drier

k80 = 4.78 × 10−3

9.6

1

0.922

60–100

144.9

A445

Final MC ~15 g/100 g

k70 = 2.26 × 10−3

307.4

Airflow: 0.2 m/s

k60 = 1.40 × 10−3

495.7

~2% humidity

Lycopene

Organic solvent extraction

k100 = 7.47 × 10−3

92.9

Demiray et al. (2013)

A470

k90 = 6.29 × 10−3

110.1

Analysis: UV-VIS

k80 = 5.44 × 10−3

11.2

1

0.921

60–100

127.5

k70 = 2.30 × 10−3

301.4

k60 = 1.30 × 10−3

533.2

Ascorbic acid

k100 = 7.87 × 10−3

88.1

A254

k90 = 6.87 × 10−3

100.9

k80 = 5.01 × 10−3

11.2

1

0.933

60–100

138.4

k70 = 2.92 × 10−3

237.7

k60 = 1.27 × 10−3

547.9

Vitamin D

Vitamin D2 (ergocalciferol)

Li and Min (1998)

Model system (12% water/88% acetone)

Air-tight serum bottles/w/light

Phase 1:

Initial concentration:

0 ppm B2

0.99

k25 = 1.12 × 10−4

1

25–60

6.21 × 103

1000 ppm D2

0.91

k60 = 3.24 × 10−4

1

25–60

2.14 × 103

15 ppm B2

0.90

k25 = 5.48 × 10−4

1

25–60

1.27 × 103

0.94

k60 = 6.96 × 10−4

1

25–60

0.996 × 103

Phase 2:

0 ppm B2

0.86

k25 = 0.217 × 10−4

1

25–60

31.9 × 103

0.96

k60 = 0.200 × 10−4

1

25–60

34.7 × 103

15 ppm B2

0.87

k25 = 0.583 × 10−4

1

25–60

11.9 × 103

0.83

k60 = 0.250 × 10−4

1

25–60

27.7 × 103

Vitamins D, A, B2

Skim milk

Vitamin D3

20 g powdered skim/100 ml water

Control

k21 = 11.8 × 10−4

16.0

1

8–21

586

Montenegro et al. (2007)

k8 = 3.33 × 10−4

2079

MIC: (Spray dried Lycopene/Gum Arabic)

w/MIC (6 g/L)

k8 = 1.83 × 10−4

3781

Vitamin A

Control

k21 = 5.33 × 10−3

4.0

1

8–21

130

(230 μg lycopene/100 g)

k8 = 4.00 × 10−3

173

w/MIC (6 g/L)

k8 = 2.17 × 10−3

320

Vitamins D, A, B2

Montenegro et al. (2007)

Skim milk cont. -

Assays: HPLC

Riboflavin (Rf)*

*Rf: triplet excited state B2

Control

k21 = 9.00 × 10−1

0.77

w/MIC (6 g/L)

k21 = 4.17 × 10−1

1.66

Vitamins D, A, B2

UHT Milk (fortified)

Vitamin D3

Saffert et al. (2009)

1.5% fat

15% RDA Vit. A, D3

PET Clear

0.997

k23 = 8.63 × 10−6

1

23

0.803 × 10−5

Preheat 65°C/10–15 m

PET low white

0.983

k23 = 8.93 × 10−6

1

23

0.776 × 10−5

Heat: 140°C/4 s

PET high white

0.933

k23 = 3.27 × 10−6

1

23

2.12 × 10−5

PET white & yellow

0.925

k23 = 3.48 × 10−6

1

23

1.99 × 10−5

Control

0.513

k23 = 0.280 × 10−6

1

23

24.7 × 10−5

Vitamin A

Test Samples:

PET Clear

0.980

k23 = 20.6 × 10−6

1

23

3.36 × 10−4

Storage: 23°C

PET low white

0.976

k23 = 1.92 × 10−6

1

23

3.61 × 10−4

Light: 700 lux

PET high white

0.974

k23 = 1.22 × 10−6

1

23

5.67 × 10−4

12 wks

PET white & yellow

0.946

k23 = 0.977 × 10−6

1

23

7.09 × 10−4

Control

0.946

k23 = 0.139 × 10−6

1

23

49.7 × 10−4

Controls:

Vitamin B2

Storage: 23°C

PET Clear

0.998

k23 = 43.5 × 10−6

1

23

1.59 × 10−4

Dark

PET low white

0.994

k23 = 18.5 × 10−6

1

23

3.76 × 10−4

PET high white

0.974

k23 = 11.2 × 10−6

1

23

6.21 × 10−4

PET white & yellow

0.998

k23 = 7.38 × 10−6

1

23

9.38 × 10−4

Control

0.559

k23 = 1.070 × 10−6

1

23

64.7 × 10−4

Vitamin E

Enteral feeding formula (5.5% protein, 3.6% lipid, 11.4% CHO)

Frias and Vidal-Valverde (2001)

Reconstitution; presterilization (UHT: 136°C/3–4 s); homogenize; add vitamins; pack in glass; sterilize (118°C/9 min); store 4–30°C/0–9 mo.

α-Tocopherol

0.874

k30 = 1.835 × 10−6

3.78 × 105

0.608

k20 = 1.598 × 10−6

2.46

1

0.999

4–30

4.34 × 105

0.611

k4 = 1.250 × 10−6

5.55 × 105

γ-Tocopherol

0.874

k30 = 2.300 × 10−6

3.01 × 105

Frias and Vidal-Valverde (2001)

0.608

k20 = 1.799 × 10−6

4.11

1

0.999

4–30

3.85 × 105

0.611

k4 = 1.210 × 10−6

5.73 × 105

δ-Tocopherol

0.874

k30 = 1.943 × 10−6

3.57 × 105

0.608

k20 = 1.572 × 10−6

2.82

1

0.985

4–30

4.41 × 105

0.611

k4 = 1.242 × 10−6

5.58 × 105

Vitamin E

Vitamin E activity

0.939

k30 = 1.844 × 10−6

3.76 × 105

Frias and Vidal-Valverde (2001)

0.936

k20 = 1.603 × 10−6

2.50

1

0.999

4–30

4.32 × 105

0.915

k4 = 1.249 × 10−6

5.55 × 105

Enteral feeding formula (3.7% protein, 3.9% lipid, 12.5% CHO)

α-Tocopherol

0.952

k30 = 2.126 × 10−6

3.26 × 105

0.946

k20 = 1.774 × 10−6

2.51

1

0.989

4–30

3.91 × 105

0.938

k4 = 1.427 × 10−6

4.86 × 105

γ-Tocopherol

0.873

k30 = 2.471 × 10−6

2.81 × 105

0.775

k20 = 1.879 × 10−6

3.21

1

0.964

4–30

3.69 × 105

0.772

k4 = 1.474 × 10−6

4.70 × 105

δ-Tocopherol

0.920

k30 = 2.473 × 10−6

2.80 × 105

0.885

k20 = 2.057 × 10−6

2.68

1

0.993

4–30

3.37 × 105

0.891

k4 = 1.620 × 10−6

4.28 × 105

Vitamin E activity

0.952

k30 = 2.134 × 10−6

3.25 × 105

0.944

k20 = 1.777 × 10−6

2.54

1

0.989

4–30

3.90 × 105

0.936

k4 = 1.428 × 10−6

4.85 × 105

Vitamin E

Frying oil

Mba et al. (2017)

Virgin palm oil

Deep-fat frying:

0.98

k190 = 7.09 × 10−4

978

α-Tocopherol

0.99

k180 = 5.81 × 10−4

10.5

1.6

0.999

← p-R2*

1192a

Potatoes peeled, sliced, washed, lightly dried

0.99

k170 = 3.95 × 10−4

11.9

1

0.971

170–190

1756

0.99

k190 = 8.02 × 10−4

865

ϒ-Tocopherol

0.98

k180 = 7.52 × 10−4

3.1

1.4

0.999

922a

0.98

k170 = 6.73 × 10−4

3.6

1

0.981

170–190

1030

Preheat oil 2 hr before frying

0.93

k190 = 2.65 × 10−4

2614

δ-Tocopherol

0.99

k180 = 2.44 × 10−4

11.0

1.5

0.999

2845a

0.98

k170 = 1.35 × 10−4

13.8

1

0.852

170–190

5134

30 min intervals:

0.99

k190 = 2.83 × 10−4

2448

α-Tocotrienol

100 g sliced potatoes fried 10 min in 4.5 L hot oil

0.98

k180 = 1.90 × 10−4

12.4

1.8

0.999

3644a

0.96

k170 = 1.16 × 10−4

18.1

1

0.998

170–190

5955

0.99

k190 = 7.75 × 10−4

894

ϒ-Tocotrienol

0.99

k180 = 6.64 × 10−4

4.8

1.5

0.999

1045a

0.99

k170 = 6.19 × 10−4

4.6

1

0.949

170–190

1121

Aliquots of oil were removed each time interval/stored frozen until analysis

0.98

k190 = 0.834 × 10−4

8311

δ-Tocotrienol

0.99

k180 = 0.828 × 10−4

24.6

1.3

0.987

8371a

0.99

k170 = 0.816 × 10−4

0.4

1

0.968

170–190

8494

0.98

k190 = 0.696 × 10−4

9959

Total carotenoids

Total heating and fry time = 20 hr

0.99

k180 = 0.564 × 10−4

17.0

1.5

0.999

12290a

0.99

k170 = 0.426 × 10−4

10.0

1

0.995

170–190

16271

Refined canola oil (w/BHA, BHT, DMPS)

0.98

k190 = 5.70 × 10−4

1216

α-Tocopherol

0.99

k180 = 4.43 × 10−4

10.5

1.6

0.999

1563a

0.99

k170 = 3.46 × 10−4

10.2

1

0.999

170–190

2006

0.96

k190 = 7.80 × 10−4

889

ϒ-Tocopherol

Tocopherol analysis: Normal Phase HPLC

0.98

k180 = 7.28 × 10−4

3.6

1.4

0.996

952a

0.99

k170 = 6.73 × 10−4

3.0

1

0.999

170–190

1030

Frying oil

0.98

k190 = 0.804 × 10−4

8621

Mba et al. (2017)

Total carotenoids

Carotenoid analysis: UV-VIS/A445

0.99

k180 = 0.756 × 10−4

0.4

1.5

0.999

9169a

0.99

k170 = 0.708 × 10−4

2.6

1

0.999

170–190

9790

Palm oil: canola oil blend

0.99

k190 = 5.03 × 10−4

1377

α-Tocopherol

0.99

k180 = 4.03 × 10−4

11.5

1.6

0.993

1719a

0.99

k170 = 3.26 × 10−4

8.8

1

0.999

170–190

2124

0.99

k190 = 5.85 × 10−4

1185

ϒ-Tocopherol

0.99

k180 = 4.95 × 10−4

4.8

1.6

0.998

1400a

0.99

k170 = 4.05 × 10−4

7.5

1

0.998

170–190

1711

δ-Tocopherol

0.97

k170 = 1.15 × 10−4

170

6017

0.99

k190 = 2.20 × 10−4

3148

α-Tocotrienol

0.95

k180 = 1.30 × 10−4

17.7

1.8

0.995

5324a

0.96

k170 = 0.600 × 10−4

26.5

1

0.991

170–190

11552

0.99

k190 = 5.65 × 10−4

1226

ϒ-Tocotrienol

0.98

k180 = 4.55 × 10−4

11.2

1.6

0.999

1524a

0.99

k170 = 3.95 × 10−4

7.3

1

0.982

170–190

1756

0.99

k190 = 0.738 × 10−4

9392

Total Carotenoids

0.99

k180 = 0.624 × 10−4

15.5

1.5

0.999

11108a

* p-R2 = pseudo coefficient of determination

0.99

k170 = 0.528 × 10−4

6.8

1

0.999

170–190

13128

Vitamin E

Milk

Galdi et al. (1989)

Infant formula

0.988

k45 = 6.25 × 10−6

1.11 × 105

0.900

k37 = 2.59 × 10−6

9.4

1

0.998

20–45

2.68 × 105

[15% protein (milk-based); 24% lipids; 57% carbohydrates; 4% vitamin, minerals, water]

With ferrous sulfate

0.861

k20 = 1.78 × 10−6

3.89 × 105

0.958

k45 = 3.82 × 10−6

1.81 × 105

With ferric glycinate

0.932

k37 = 2.50 × 10−6

9.6

1

0.999

20–45

2.77 × 105

0.926

k20 = 1.04 × 10−6

6.66 × 105

Vitamin E

Freeze-dried/rehumidified

Widicus et al. (1980)

Model system:

α -Tocopherol

Packaging: 303 × 406 cans (XS headspace)

(48% starch, 34% corn syrup solids, 10.2% soy isolate, 5.1% sucrose, 2.0% NaCl)

aw = 0.10

k37 = 0.557 × 10−5

1.24 × 105

k30 = 0.345 × 10−5

9.5

1

0.977

20–37

2.01 × 105

k20 = 0.225 × 10−5

3.08 × 105

aw = 0.24

k37 = 0.892 × 10−5

0.777 × 105

k30 = 0.432 × 10−5

10.8

1

0.894

20–37

1.60 × 105

k20 = 0.310 × 10−5

2.24 × 105

aw = 0.40

k37 = 1.031 × 10−5

0.672 × 105

k30 = 0.436 × 10−5

10.4

1

0.788

20–37

1.59 × 105

k20 = 0.363 × 10−5

1.91 × 105

aw = 0.65

k37 = 1.107 × 10−5

0.626 × 105

Widicus et al. (1980)

k30 = 0.494 × 10−5

11.4

1

0.871

20–37

1.40 × 105

k20 = 0.360 × 10−5

1.93 × 105

Packaging:

208 × 006 TDT (no headspace)

aw = 0.10

k37 = 0.599 × 10−5

1.16 × 105

k30 = 0.326 × 10−5

10.1

1

0.936

20–37

2.12 × 105

k20 = 0.224 × 10−5

3.09 × 105

aw = 0.24

k37 = 0.790 × 10−5

0.877 × 105

k30 = 0.410 × 10−5

13.1

1

0.978

20–37

1.69 × 105

k20 = 0.226 × 10−5

3.07 × 105

aw = 0.40

k37 = 0.913 × 10−5

0.795 × 105

k30 = 0.419 × 10−5

10.8

1

0.861

20–37

1.65 × 105

k20 = 0.315 × 10−5

2.20 × 105

aw = 0.65

k37 = 0.931 × 10−5

0.745 × 105

k30 = 0.451 × 10−5

8.8

1

0.791

20–37

1.54 × 105

k20 = 0.385 × 10−5

1.80 × 105

Vitamin E

Seaweed (Ascophyllum nodosum)

Jenson (1969)

α -Tocopherol

Air-dried/ground

Moisture content:

10%

0.954

k25 = 0.441 × 10−5

1.57 × 105

0.883

k15 = 0.296 × 10−5

10.1

1

0.880

4–25

2.34 × 105

0.881

k10 = 0.256 × 10−5

2.71 × 105

0.987

k4 = 0.111 × 10−5

6.24 × 105

15%

0.923

k25 = 0.538 × 10−5

1.29 × 105

0.980

k15 = 0.354 × 10−5

12.5

1

0.893

4–25

1.96 × 105

0.926

k10 = 0.261 × 10−5

2.66 × 105

0.965

k4 = 0.0999 × 10−5

6.94 × 105

25%

0.983

k25 = 0.833 × 10−5

0.832 × 105

a Values reported by authors.

0.902

k15 = 0.561 × 10−5

6.3

1

0.998

4–25

1.24 × 105

0.885

k10 = 0.464 × 10−5

1.49 × 105

0.902

k4 = 0.373 × 10−5

1.86 × 105

Multiple Vitamins

Fish feed

Extrusion:

With Crystalline vitamin

Marchetti et al. 1999

Wenger X/185

Ascorbic acid

0.970

kRT = 6.30 × 10−6

1

RT

1.10 × 10−5

Moisture: 16%

Biotin

0.880

kRT = 0.316 × 10−6

1

RT

22.0 × 10−5

Outlet Temp = 96°C

Cyanocobalamin

0.994

kRT = 1.73 × 10−6

1

RT

4.01 × 10−5

Dry: 95°C/20 min

Folic acid

0.817

kRT = 1.44 × 10−6

1

RT

4.81 × 10−5

0.998

kRT = 3.47 × 10−6

1

RT

1.20 × 10−5

Nicotinamide

0.945

kRT = 0.110 × 10−6

1

RT

63.0 × 10−5

Storage: RT/

Pantothenic acid

0.886

kRT = 1.13 × 10−6

1

RT

6.14 × 10−5

Paper bags

Pyridoxine

0.999

kRT = 1.86 × 10−6

1

RT

3.73 × 10−5

Riboflavin

0.979

kRT = 0.715 × 10−6

1

RT

9.70 × 10−5

Thiamin

0.937

kRT = 0.955 × 10−6

1

RT

7.26 × 10−5

Extrusion:

With Fat coated vitamin

Marchetti et al. (1999)

Assay: NR

Ascorbic acid

0.964

kRT = 0.871 × 10−6

1

RT

7.96 × 10−5

Assay: mo

Biotin

0.719

kRT = 0.276 × 10−6

1

RT

25.1 × 10−5

Assay: mo

Cyanocobalamin

0.956

kRT = 1.11 × 10−6

1

RT

6.23 × 10−5

Assay: mo

Folic acid

0.928

kRT = 0.523 × 10−6

1

RT

13.3 × 10−5

Assay: HPLC

0.966

kRT = 1.03 × 10−6

1

RT

6.76 × 10−5

Assay: mo

Nicotinamide

0.859

kRT = 0.0993 × 10−6

1

RT

69.8 × 10−5

Assay: mo

Pantothenic acid

0.883

kRT = 0.266 × 10−6

1

RT

26.1 × 10−5

Assay: mo

Pyridoxine

0.997

kRT = 0.307 × 10−6

1

RT

22.6 × 10−5

Assay: fluorometric

Riboflavin

0.991

kRT = 0.416 × 10−6

1

RT

16.7 × 10−5

Assay: fluorometric

Thiamine

0.913

kRT = 0.266 × 10−6

1

RT

26.0 × 10−5

Pelleting:

With Crystalline vitamin

Moisture: 5%

Ascorbic acid

0.997

kRT = 5.79 × 10−6

1

RT

1.20 × 10−5

CPM 7000

Biotin

0.986

kRT = 0.448 × 10−6

1

RT

15.5 × 10−5

Outlet Temp = 85C

Cyanocobalamin

0.960

kRT = 1.05 × 10−6

1

RT

6.59 × 10−5

Folic acid

0.990

kRT = 0.720 × 10−6

1

RT

9.63 × 10−5

Storage: RT/

0.993

kRT = 3.29 × 10−6

1

RT

2.11 × 10−5

Paper bags

Nicotinamide

0.994

kRT = 0.306 × 10−6

1

RT

22.7 × 10−5

Pantothenic acid

0.999

kRT = 1.20 × 10−6

1

RT

5.76 × 10−5

Pyridoxine

0.971

kRT = 1.34 × 10−6

1

RT

5.17 × 10−5

Riboflavin

0.966

kRT = 0.423 × 10−6

1

RT

16.4 × 10−5

Thiamine

0.993

kRT = 0.471 × 10−6

1

RT

14.7 × 10−5

Pelleting:

With Fat coated vitamin

Assay: NR

Ascorbic acid

0.960

kRT = 1.09 × 10−6

1

RT

6.35 × 10−5

Assay: mo

Biotin

0.976

kRT = 0.289 × 10−6

1

RT

24.0 × 10−5

Assay: mo

Cyanocobalamin

0.924

kRT = 0.727 × 10−6

1

RT

9.54 × 10−5

Assay: mo

Folic acid

0.973

kRT = 0.253 × 10−6

1

RT

29.5 × 10−5

Assay: HPLC

0.961

kRT = 0.814 × 10−6

1

RT

8.52 × 10−5

Assay: mo

Nicotinamide

0.961

kRT = 0.186 × 10−6

1

RT

37.3 × 10−5

Assay: mo

Pantothenic acid

0.969

kRT = 0.439 × 10−6

1

RT

15.8 × 10−5

Pelleting:

Marchetti et al. (1999)

Assay: mo

Pyridoxine

0.948

kRT = 0.478 × 10−6

1

RT

14.5 × 10−5

Assay: fluorometric

Riboflavin

0.967

kRT = 0.211 × 10−6

1

RT

32.8 × 10−5

Assay: fluorometric

Thiamine

0.980

kRT = 0.235 × 10−6

1

RT

29.5 × 10−5

Miscellaneous bioactive compounds

Ascorbic acid

Nambi et al. (2016)

Beetroot

Blanching (0–15 min)

k90 = 4.44 × 10−2

15.61

(5 mm3)

6 time interval sampling

k85 = 2.80 × 10−2

24.72

k80 = 1.94 × 10−2

14.7

1

0.950

70–90

35.75

k75 = 1.82 × 10−2

38.00

k70 = 1.24 × 10−2

55.95

Ascorbic acid

Assay: Indophenol

k90 = 11.3 × 10−2

6.11

Green peas

k85 = 8.01 × 10−2

8.65

(~7 +/− 0.5 mm dia)

k80 = 6.96 × 10−2

9.3

1

0.851

70–90

9.96

k75 = 4.98 × 10−2

13.92

k70 = 5.56 × 10−2

12.46

Ascorbic acid

k90 = 14.5 × 10−2

4.78

Egg plant

k85 = 9.37 × 10−2

7.40

(5 mm3)

k80 = 7.72 × 10−2

11.0

1

0.930

70–90

8.98

k75 = 6.54 × 10−2

10.59

k70 = 5.69 × 10−2

12.18

Ascorbic acid

k90 = 4.70 × 10−2

14.74

Green pepper

k85 = 3.22 × 10−2

21.51

(5 × 5 × 3 mm)

k80 = 2.36 × 10−2

16.3

1

0.994

70–90

29.37

k75 = 1.77 × 10−2

39.09

k70 = 1.26 × 10−2

55.14

Phenolics

Nambi et al. (2016)

Beetroot

Blanching (0–15 min)

k90 = 13.6 × 10−2

5.09

(5 mm3)

6 time interval sampling

k85 = 11.8 × 10−2

5.85

k80 = 10.3 × 10−2

9.2

1

0.987

70–90

6.72

k75 = 8.11 × 10−2

8.55

k70 = 6.49 × 10−2

10.68

k90 = 18.8 × 10−2

3.68

Nambi et al. (2016)

Green peas

Assay: Folin-Ciocalteau

k85 = 10.2 × 10−2

6.80

(~7 +/− 0.5 mm dia)

k80 = 9.73 × 10−2

17.8

1

0.954

70–90

7.12

UV-VIS – A751

k75 = 6.41 × 10−2

10.81

k70 = 3.93 × 10−2

17.62

Phenolics

k90 = 10.2 × 10−2

6.77

Egg plant

k85 = 6.98 × 10−2

9.93

(5 mm3)

k80 = 5.17 × 10−2

11.7

1

0.946

70–90

13.41

k75 = 4.69 × 10−2

14.77

k70 = 3.80 × 10−2

18.23

Phenolics

k90 = 15.3 × 10−2

4.54

Green pepper

k85 = 12.0 × 10−2

5.79

(5 × 5 × 3 mm)

k80 = 8.73 × 10−2

16.3

1

0.994

70–90

7.94

k75 = 5.75 × 10−2

12.06

k70 = 4.24 × 10−2

16.34

Antioxidant activity

Blanching (0–15 min)

Nambi et al. (2016)

Beetroot

6 time interval sampling

k90 = 1.27 × 10−2

54.54

k85 = 1.15 × 10−2

60.43

k80 = 1.03 × 10−2

5.4

1

0.999

70–90

67.56

*Assay: % DPPH inhibition

k75 = 0.925 × 10−2

74.93

k70 = 0.817 × 10−2

84.84

Antioxidant activity

UV-VIS – A516

k90 = 3.68 × 10−2

18.85

Green peas

k85 = 3.51 × 10−2

19.75

(~7 +/− 0.5 mm dia)

k80 = 3.86 × 10−2

8.3

1

0.622

70–90

17.97

k75 = 3.13 × 10−2

22.13

k70 = 1.70 × 10−2

40.70

Antioxidant activity

k90 = 4.63 × 10−2

14.97

Egg plant

k85 = 3.82 × 10−2

18.16

(5 mm3)

k80 = 1.91 × 10−2

15.1

1

0.879

70–90

36.35

k75 = 2.24 × 10−2

30.94

k70 = 1.31 × 10−2

52.99

Antioxidant activity

k90 = 2.55 × 10−2

27.19

Green pepper

k85 = 2.16 × 10−2

32.11

(5 × 5 × 3 mm)

k80 = 1.49 × 10−2

10.1

1

0.951

70–90

46.43

k75 = 1.46 × 10−2

47.38

% inhibition DPPH = [(Acontrol-Asample]∙100/[Acontrol]

k70 = 1.12 × 10−2

61.89

a Values reported by authors.

### Table 3.4   Excerpts from FDA Color Additives Approved for Use in Human Food

Part 73, Subpart A: Color Additives Exempt from Batch Certificationa

21 CFR Section

Straight Color

EEC#

Year Approvedb

Uses and Restrictions

73.30

Annatto extract

E160b

1963

Foods generally.

73.40

Dehydrated beets (beet powder)

E162

1967

Foods generally.

73.75

Canthaxanthinc

E161g

1969

Foods generally, ≤ 30 mg/lb of solid or semisolid food or per pint of liquid food; May also be used in broiler chicken feed.

73.85

Caramel

E150a-d

1963

Foods generally.

73.90

β-Apo-8′-carotenal

E160e

1963

Foods generally, ≤ 15 mg/lb solid, 15 mg/pt liquid.

73.95

β-Carotene

E160a

1964

Foods generally.

73.10

Cochineal extract

E120

1969

Foods generally

2009

Food label must use common or usual name “cochineal extract”; effective January 5, 2011.

73.10

Carmine

E120

1967

Foods generally.

2009

Food label must use common or usual name “carmine”; effective January 5, 2011.

73.13

Sodium copper chlorophyllinc

E141

2002

Citrus-based dry beverage mixes ≤ 0.2 percent in dry mix; extracted from alfalfa.

73.14

Toasted partially defatted cooked cottonseed flour

1964

Foods generally.

73.17

Grape color extractc

E163?

1981

Non-beverage food.

73.17

Grape skin extract (enocianina)

E163?

1966

Still & carbonated drinks & ades; beverage bases; alcoholic beverages (restrict. 27 CFR Parts 4 & 5).

73.25

Fruit juicec

1966

Foods generally.

1995

73.26

Vegetable juicec

1966

Foods generally.

1995

73.30

Carrot oil

1967

Foods generally.

73.34

Paprika

E160c

1966

Foods generally.

73.35

Paprika oleoresin

E160c

1966

Foods generally.

73.45

Riboflavin

E101

1967

Foods generally.

73.50

Saffron

E164

1966

Foods generally.

73.53

Spirulina extract

2013

Candy and chewing gum.

2014

Coloring confections (including candy and chewing gum), frostings, ice cream and frozen desserts, dessert coatings and toppings, beverage mixes and powders, yogurts, custards, puddings, cottage cheese, gelatin, breadcrumbs, and ready-to-eat cereals (excluding extruded cereals).

73.59

Tomato lycopene extract; tomato lycopene concentratec

E160

2006

Foods generally.

73.60

Turmeric

E100

1966

Foods generally.

73.62

Turmeric oleoresin

E100

1966

Foods generally.

a The color additives Astaxanthin, Astaxanthin dimethyldisuccinate, Ultramarine blue, Canthaxanthin, Haematococcus algae meal, Synthetic iron oxide, Dried algae meal, Tagetes (Aztec marigold) meal and extract, Corn endosperm oil, Paracoccus pigment, and Phaffia yeast are approved for specific uses in animal food (see 21 CFR 73.35, 73.37, 73.50, 73.75, 73.185, 73.200, 73.275, 73.295, 73.315, 73.352, and 73.355, respectively).

b The year approved is based on the date listed in the “Confirmation of Effective Date” notice for the action as published in the Federal Register.

c Petitioned for use after the 1960 amendments; not provisionally listed.

When dealing with vitamin C, it is important to understand the bioavailability and kinetics of retention in processing and storage of related compounds. Ascorbic acid and dehydroascorbic acid are the chemical forms with primary vitamin activity. Dehydroascorbic acid, however, is not stable to heat, and is rapidly hydrolyzed to 2,3-diketogulonic acid and further breakdown products. Other compounds such as ascorbate-2-phosphate, a fully active compound (Liao and Seib, 1988); ascorbigen, a form of ascorbic acid bound to phenols, with 10–20% bioavailability in guinea pigs (Matano and Kato, 1967); and isoascorbic acid (erythorbic acid) with about 5% vitamin activity and with a limiting effect on the absorption of ascorbic acid (Hornig et al., 1974), may need some consideration as well.

Taking into account the stability of ascorbic acid in food systems during processing, vitamin C as well as thiamine and folic acid are normally considered to be good indicators of the severity of a food process. If these vitamins are well retained, we may safely assume that all other nutrients are well retained during processing. Based on the recent FDA dietary recommendations and admonitions to increase fruit and vegetable, as well as whole grain consumption, the significance of understanding vitamin retention during processing and its bioavailability have become increasingly important.

Since fruits and vegetables are major contributors to micronutrients, in particular vitamin C, a vast number of studies have been published reporting kinetic information on the stability of this compound in blanching, canning, dehydration, high pressure sterilization, pasteurization, and freezing operations (Giannakourou and Taouki, 2003; Selman, 1994; Martins and Silva, 2003; Van den Broeck, et al., 1998; Viera et al., 2015; etc.). Killeit (1994), for example, reviewed vitamin retention during extrusion and pointed out mostly destructive effects, with vitamin C being the most sensitive. He also reported that through modification of the vitamin molecule, there was potential for improved stability. An example cited was the commercially available L-ascorbyl-2-polyphosphate (AsPP), a modified form of ascorbic acid, used for feed applications. An extruded feed for catfish containing AsPP showed improved stability through the process as compared to a traditionally used ethylcellulose-coated ascorbic acid (83% retention vs. 39%) and no losses during storage as compared with 78% loss of the traditionally coated ascorbic acid (Robinson et al., 1989). Reports indicate complete bioavailability of this compound for the animals (Grant et al., 1989). In a similar study, Marchetti et al. (1999) reported 80% loss of ascorbic acid during extrusion of a fortified fish feed when using a pure crystalline form of the vitamin, but only a 48% loss when using an encapsulated form of the vitamin; furthermore, during 6 months storage at room temperature, losses showed an additional 80% loss with the non-encapsulated ascorbic acid compared to <20% loss with the encapsulated form.

As summarized by Clydesdale et al. (1991), other components present in food systems may have a major impact on the kinetics of ascorbic acid degradation by changing the reactivity of the system, thus eventually impacting its bioavailability. For instance, minerals such as iron and copper, vitamin E, flavonoids, amino acids, and sugars can significantly change the retention of ascorbic acid during processing and storage, as well as its bioavailability. Van den Broeck et al. (1998) reported that ascorbic acid found in real food systems such as orange juice and tomatoes had less stability than buffered solutions of ascorbic acid (pH 4–8) subjected to heat (120°C–150°C) or combined pressure/thermal treatment (8.5 kbar [850 MPa]/65°C–80°C). Verbeyst et al. (2013) proposed a mechanistic biphasic model for the degradation of ascorbic acid and the consecutive formation and degradation of dehydroascorbic acid (DHAA) during thermal processing (80°C–140°C) of strawberries at atmospheric and at high pressure (700 MPa, 60°C–110°C), under aerobic and anaerobic conditions. They found oxidation of ascorbic acid to DHAA to be the most critical factor for overall conditions, while the anaerobic reaction was critical only at high temperatures (>120°C) for extended times (Table 3.3). Lavelli and Giovanelli (2003) reported pseudo-first-order kinetics of ascorbic acid degradation in tomato products as related to stability of various carotenoids and phenolics during storage (30°C–50°C/90 days) and found degradation of ascorbic acid even at 30°C.

Light-induced degradation of ascorbic acid in the presence of riboflavin has been a subject of investigation. Several studies suggest that riboflavin is first excit ed by visible light, and then, through an excitation transfer, ascorbic acid is oxidized. The reaction mechanism proposes the formation of H2O2 (Şahbaz and Somer, 1993; Şansal and Somer, 1997).

#### 3.3.1.2  Vitamin B1 (Thiamine)

Thiamine, also known as Vitamin B1, thiamine chloride, aneurin, and antiberiberi vitamin, occurs either in the free thiamine form, as a protein complex, as mono-, di-, or triphosphate esters (TMP, TPP, TTP, respectively), or as a phosphorous protein complex. The structure of the free base form is characterized by a pyrimidine ring linked by a methylene bridge to the 3-nitrogen atom in a substituted thiazole ring; its chemical name is 3-[(4′-amino-2′-methyl-5′-pyrimidinyl) methyl]-5-(2-hydroxyethyl)-4-methylthiazole (Figure 3.10). Thiamine hydrochloride is used as the US Pharmacopeia reference standard and is the form used for food and pharmaceutical fortification, as well as thiamine mononitrate. Thiamine is an essential nutrient required for carbohydrate, nucleic acid, and amino acid metabolism as well as being involved in nerve function; it is converted in vivo to thiamine diphosphate (TPP), the coenzyme in decarboxylation of α-keto acids. More recent work has shown involvement of TPP in the metabolism of 3-methyl-branched fatty acids (Foulon et al., 2003). More detailed reviews on the physiological aspects of thiamine can be found (Bates (2007; Bettendorf (2014). Foods considered to be rich in vitamin B1 include whole grains (cereal germ), nuts, brown rice (or white rice including aleurone layer [silver skin]), pork, egg yolk, yeast, fruit, and vegetables. Thiamine is known as one of the least stable of the water-soluble vitamins. While thiamine has been found unstable in neutral and alkaline solutions, it can withstand up to 120°C for one hour in acidic solutions, although is susceptible to cleavage by sulfites even in an acidic environment. In dry form it is stable to oxidation, but in solution it is very unstable to oxidation and reduction reactions. Since thiamine can exist in many forms, it is obvious that its stability and its kinetics of degradation are highly affected by the relative concentrations of the different forms (Farrer, 1955). Feliciotti and Esselen (1957), studying the thermal destruction of thiamine in pureed meats and vegetables, suggested that its destruction in foods was dependent on the interrelationship of pH and the relative proportions of the free and the combined forms of the vitamin. It has been observed that the enzyme-bound forms, cocarboxylases, appear to be less stable than the free forms. Mulley et al. (1975b) also reported that under identical conditions, cocarboxylase is destroyed faster than thiamine hydrochloride. It appears that the faster destruction of the cocarboxylase may be due to the pyrophosphoric acid group which is the basic difference between the two molecules, and that appears to cause additional reactivity or stress on the cocarboxylase molecule. The authors also reported that the presence of the cocarboxylase form does not affect the destruction of the free thiamine up to concentration levels of 35%. Since this appears to be the situation in most food products, it is expected that the cocarboxylase form will not interfere with the kinetics of degradation.

Figure 3.10   Chemical structures of the vitamin B1 group (thiamine).

A number of factors will be highly influential on the stability of thiamine, including water activity, pH, temperature, ionic strength, and the presence of other compounds. Some of the suggested mechanisms for thiamine degradation are presented in Figure 3.11. The particular instability of thiamine to heat under neutral and alkaline conditions has resulted in various studies on the chemistry of thiamine degradation. However, due to the high complexity of food materials, a number of studies have been carried out in model systems in order to clarify the mechanisms involved in thiamine degradation. Several authors such as Farrer (1955), Beadle et al. (1943), and Greenwood et al. (1943) established that the thermal destruction of thiamine in aqueous and buffered solutions followed a first-order reaction. Farrer and Morrison (1949) studied the thermal degradation of thiamine in buffered solutions and determined that the Arrhenius equation could be used to describe the effect of temperature. Two possible reactions have been considered leading to the degradation of thiamine, namely, (a) the breaking of the “CH-bridge” leaving the pyrimidine and thiazole moieties and (b) the breakdown of the thiazole ring with the production of hydrogen sulfite. Limited efforts have been made to determine the governing mechanism of thiamine degradation in food systems and rather an overall response is commonly monitored. In fact, the lack of comprehensive kinetic data has limited our understanding of the significance of the different mechanisms involved. Dwivedi and Arnold (1973) summarized the most important aspects affecting the degradation of thiamine in food products and model systems; they also determined that the destruction of the vitamer revolved around the breaking of the methylene bridge to form pyrimidine and thiazole moieties.

Figure 3.11   Degradation pathways of thiamine. (1) Dwivedi and Arnold, 1972a; 1973; (2) Dwivedi and Arnold, 1973; (3) Kawasaki and Daira, 1963; (4) Lhoest, 1958; (5) Zima and Williams, 1940; (6) Sykes and Todd, 1951; (7) Sykes and Todd, 1951; (8) Barger et al., 1935; (9) Metzler, 1960; Dwivedi and Arnold, 1972b.

Of great significance to the area of stability of thiamine has been the observations of s everal investigators that thiamine in natural foods is more heat-resistant than in aqueous and buffered systems. Thus, it appears that certain factors will influence the stability of the vitamin. For instance, Frost and McIntire (1944) indicated that α- and β-amino acids and some of their derivatives had a significant stabilizing effect upon thiamine at pH 6.0. In general, this effect became noticeable at pH values above the range 4.5–5.0. Other compounds such as proteins and starch have been found to improve the thermal stability of thiamine; however, the exact mechanisms involved are not well elucidated.

A controversy still exists regarding the role that oxygen plays in the thermal stability of thiamine. In fact, results presented by Williams and Spies (1938), Farrer (1955), and Mulley et al. (1975a) have demonstrated that the thermal degradation of thiamine can be described by a first-order kinetics and that the reaction was not oxidative in nature. On the other hand, other authors have suggested that for the case of products containing oxygen the reaction became a true first-order reaction upon the disappearance of oxygen (Farrer and Morrison, 1949). Fink and Kessler (1985) studied the retention of thiamine in milk in the temperature range from 4 to 150°C. The authors reported that for the range 35 to 50°C and 72 to 85°C, the reaction followed second-order kinetics. With regard to the effect of oxygen, the authors did not observe any effect on the rate of thiamine losses. Dennison et al. (1977) working with a dehydrated food system, also indicated that the presence of oxygen did not significantly affect the degradation of thiamine.

In model systems, looking at the effect of different solutes, Fernández et al. (1986) reported that the degradation of thiamine followed a first-order reaction and that the degradation was affected by the type of solute used in the formulation, increasing in the order sodium chloride, potassium chloride, glycerol, and sodium sulfate. Thus, not only the rate of degradation was affected by water activity, but also by the specific solute. Bell and White (2000) evaluated stability of thiamine in solid model systems using polyvinylpyrrolidone (used in the medical and pharmaceutical industry as a binder or lubricant) and found that thiamine degradation followed a pseudo-first-order reaction with increasing water activity (a w  > 0.4–0.77, pH 7.0, 20°C), but suggested that at lower aw (<0.4), a better correlation was found between glass transition temperature (Tg) and B1 degradation rates, indicating that this is a variable that should be recognized in formulating low moisture products.

It has been determined that in food products, compounds such as sulfites, phenols, and amino acids and proteins as well as lipids may have a significant effect on thiamine degradation and its associated kinetic parameters. Of particular significance is the effect of sulfites on thiamine due to the nucleophilicity of the sulfite ion. Hence, the destruction of thiamine by sulfite becomes a key issue in foods claiming to be a significant source of this vitamin (Vanderveen, 1988).

Fox et al. (1997) reported losses of thiamine in ground pork as a result of irradiation but little or no losses during conventional cooking, heat denaturation, or storage. It was also pointed out that the exclusion of oxygen may improve stability of thiamine during irradiation and that stability was dependent on the source of meat and type of cut (Fox et al., 1995). Van Calenberg et al. (1999) showed the effect of irradiation (3 kGy X-rays [0.05 kGy/min] or electrons [5 kGy/min]) on storage stability of thiamine content of packaged mince chicken meat (air or vacuum); samples were irradiated at −18°C and +5°C, followed by storage at those same designated temperatures. Although no major differences in thiamine loss between dose rates were observed after storage, the most notable differences appeared to be the temperature at which the dosing took place indicating better retention at −18°C compared with 5°C (11%–19% vs. 19%–30% loss of thiamine, respectively), the higher of each group being the vacuum-packaged samples and explained as drip/leaching losses.

Whole grains are noted for their relatively high content in B-vitamins and antioxidant potential. Several researchers have monitored and reported losses of thiamine during various steps in the baking process: up to 48% loss of B1 in white bread, depending upon the process, i.e., extended fermentation times resulted in higher levels of B1, B2, and B6 vitamins (Batifoulier et al., 2005); losses of 20%–45% B1 in sourdough bread production (Mihhalevski et al., 2013); and 20%–22% loss of B1 in rye bread compared with up to 56% loss in white wheat bread (Martinez-Villaluenga et al., 2009), indicating the importance of using whole grains, extended fermentation times, and specific cultures.

As previously indicated, retention of thiamine is normally considered to be an indicator of the intensity of thermal processes such as blanching, canning, freezing, extrusion, dehydration, etc. (Ilo and Berghofer, 1998; Selman, 1994). Butz et al. (2007) investigated effect of high pressure processing at elevated temperatures on thiamine and riboflavin in minced fresh pork and model systems (20°C–100°C, 0.1 MPa, 600 MPa). Under matched conditions for models and pork meat, authors found thiamine destruction nearly 30 times higher in the model systems compared with actual meat samples, emphasizing the importance of working with actual food systems when predicting nutritional quality. Further information on kinetic destruction of thiamine is required for less conventional food processes such as gamma irradiation, high pressure, pulse electric fields, and microwave and radio frequency processing.

#### 3.3.1.3  Vitamin B2 (Riboflavin)

Riboflavin, or vitamin B2 (also referred to as vitamin G, lactoflavine, and chemically as 7,8-dimethyl-10-(1′-ribityl) isoalloxazine), is a precursor of the flavin cofactors, FAD (flavin adenine dinucleotide [riboflavin-5′-trihydr ogen-diphosphate]) and FMN (flavin-mononucleotide [riboflavin-5′-monophosphate]), which function in many important enzymatic redox reactions in intermediary metabolism (Figure 3.12). Riboflavin exists in dietary sources predominantly in the form of its coenzyme derivatives, FAD and FMN, which in turn can carry out one- and two-electron transfer reactions involved in diverse biochemical catalytic reactions. Henriques et al. (2010) have presented an overview of many of the updated riboflavin biochemical mechanisms, with particular emphasis on deficiencies of the vitamer and their implications on fatty acid metabolism. The actual free form of riboflavin is more frequently found in commercial multivitamin applications. Common biological sources of B2 are similar to most of the other B-vitamins, including eggs, milk, cheese, meats (liver and kidneys), yeast, and leafy green vegetables. From a nutritional perspective, it should be pointed out that, although green plants can synthesize their own free riboflavin and mammals cannot, the relative amounts found in meat sources (as NAD and FMN) are significantly higher than totals found in most plants. In that FAD and FMN occur chiefly in non-covalently-bound forms to enzymes, while covalently-bound flavins are less available for absorption, are all factors to consider when carrying out vitamer analyses. More detailed reviews of the biochemical function of the flavins have been published (Powers, 2003; Henriques et al.; 2010; Pinto and Rivlin, 2014). Riboflavin is relatively stable in foods under ordinary conditions, as long as it is not exposed to light. It has relatively low water solubility (0.067–0.333 mg/ml) and exhibits a fluorescent yellow-green color (Merck, 2002), which can limit its ability for fortification from a visual perspective, although it may be used as a food colorant with potential health benefits (Table 3.4). FMN has slightly higher solubility and may be a better choice for liquid applications; however, color may still be an issue, as this is also used as a colorant in Europe (E101a). Stability of riboflavin is pH dependent, being more stable under acidic conditions, with maximum stability to heat being between pH 2.0 and 5.0 and destruction of the isoalloxazine ring at pH > 7.0 (Ball et al., 1994). With regard to FAD and FMN, they are both readily converted to riboflavin at pH < 5.0 (Russell and Vanderslice, 1990). This factor is actually used as a prestep when analyzing for total riboflavin; however, it should be avoided if analyzing for each of the three vitamers individually.

Figure 3.12   Chemical structures of the vitamin B2 group (riboflavin, FMN, FAD).

Photochemical cleavage of riboflavin under alkaline conditions results in the formation of the highly reactive compound, lumiflavin, which mediates the destruction of other vitamins. Under neutral and acidic conditions, this vitamin loses the ribityl side chain forming lumichrome (Figure 3.13). Both lumichrome and lumiflavin have no biological activity; moreover, the photolysis reaction is irreversible. Work carried out by Woodcock et al. (1982) in pasta products, indicated that lumichrome, a photolysate of riboflavin, was not the only one or the final degradation product. In fact, in these types of products, only 60% of the losses were accounted for by the presence of lumichrome and varied according to the process conditions. Palanuk and Warthesen (1988) studied the kinetics of degradation of riboflavin and lumichrome in milk and observed that the rate of degradation of riboflavin was 2.8 times greater than the rate of lumichrome formation, and that the rate of lumichrome formation was 6.3 times greater than the rate of lumichrome degradation. The combined effect was such that after an increase in lumichrome formation, leveling off of the reaction took place. According to the authors’ model, 23.4% of the riboflavin degraded to lumichrome, indicating that either riboflavin degraded to other products, or that lumichrome became bound to other components in the system, becoming unavailable for determination. Results reported by Furuya et al. (1984) also indicated that lumichrome content in both buffer systems and pasta leveled off during storage, while the concentration of riboflavin continuously decreased. Thus, simple monitoring of the formation of this compound will not be a reliable method to measure the losses of the vitamin. The degradation of riboflavin under aerobic conditions has been generally reported as following first-order reaction kinetics.

Figure 3.13   Degradation pathways of riboflavin. (1) Kuhn et al., 1933; Wagner-Jauregg, 1972; (2) Holmström and Oster, 1961; Kuhn et al., 1933; (3) Holmström and Oster, 1961; (4) Holmström and Oster, 1961; (5) Karrer et al., 1934; (6) Choe et al., 2005.

A list of some of the most important studies with the corresponding rates of degradation is presented in Table 3.3. Although the degradation of the vitamin has often been categorized as being a first-order reaction, the exact approach for the monitoring of the degradation will play a significant role. Woodcock et al. (1982) indicated that lumichrome production in pasta products followed two basic steps. The first stage followed first-order reaction kinetics that proceeded at a fast rate and a second stage that involved the disappearance of lumichrome and could not be easily described in kinetic terms. The photodegradation of riboflavin in milk has been determined to follow first-order kinetics (Singh et al. 1975; and Allen and Parks, 1979). Kinetic parameters were found to be influenced by temperature and the presence of light. From the point of view of kinetics, limited work has been carried out to clearly determine the mechanisms involved in the degradation of riboflavin and their contribution to the overall kinetic values. Based on some of the work reported by several authors, it is evident that the concentration of lumiflavin and lumichrome will influence the kinetic values reported, if these products of the reaction are the ones to be monit ored as a measurement of stability. It is well known through the many studies carried out looking at stability of riboflavin in milk under varied commercial retail light conditions that there is significant loss of shelf-life due to light induced oxidation and development of off-flavors and that proper light barrier packaging is needed (Cladman et al., 1998; Mestdaugh et al., 2005); however, with the recent introduction of LED lighting in commercial spaces, there is a whole new wave of experimental variables to consider. There has been a great deal of concern that LED lighting has potentially imposed new risks of loss in shelf-life quality in milk (De Jesus and Dando, 2016); a key factor pointed out by the authors is the specific wavelength involved for the specific LED light. On the other hand, studies by Brothersen et al. (2016) found exposure to LED at 4000 lx, or fluorescent light at 2200 lx for 24 hours vs. a control (no light) sample, that either light showed some changes in quality, but samples with LED showed less off-flavors than those samples exposed to fluorescent lighting from both a consumer taste panel perspective as well as an analytical analysis of both riboflavin and vitamin A degradation perspectives. It is obvious further work for clarification will need to be carried out.

Studies presented by Toyosaki et al. (1988) indicate that the photolysis mechanism can be described according to the following reactions:

$Riboflavin + h v ⇄ k − 1 k 1 riboflavin-H → k 2 riboflavin decomposition$
$Riboflavin + h v false( multicomponents false) ⇄ k − 1 k 1 riboflavin-H + active oxygen → k 2 riboflavin decomposition$
$Riboflavin false( multicomponents false) + active oxygen → k 3 riboflavin decomposition$

According to the authors, standard riboflavin proceeded by one-phase decomposition under all the conditions studied. Standard riboflavin underwent photolysis in which no active oxygen was produced. On the other hand, milk serum riboflavin was photolyzed by a two-phase decomposition when the intensity of the irradiation was low. The presence of active oxygen was found to be involved in the reaction. Increased irradiation was reported to change the photolysis of the milk serum riboflavin from a two-phase to a one-phase decomposition mechanism, with smaller amounts of active oxygen being produced. Jung et al. (2007) identified formation of a single compound, 2,3-butanedione, during photolysis of riboflavin in phosphate buffer (0.1M, pH 6.5) as analyzed by SPME-GC/MS.

Photosensitization of riboflavin can produce reactive oxygen species such as superoxide anion, singlet oxygen, hydroxyl radical, and hydrogen peroxide. These reactive oxygen species and radicals have been found to affect the decomposition of proteins, lipids, vitamins, and other nutritional components (Choe et al., 2005). For example, Kim et al. (2010) found that in the presence of both riboflavin and light (4°C, 8 hours) in aqueous solutions of anthocyanins (from meoru grape) there was production of singlet oxygen resulting in significant degradation of the anthocyanins. On one hand, this validates the antioxidant properties of anthocyanins, but also cautions that the intentions of using anthocyanins as antioxidants which will be lost if improper storage or combinations of reactant species are present; riboflavin with anthocyanins without the presence of light or anthocyanins without riboflavin but in the presence of light showed little or no degradation of the anthocyanins. Similarly, Yang et al. (2008) found apparent first-order degradation kinetics of isoflavones (daidzein and genistein) from soybeans in the presence of riboflavin and light (7 hours, room temperature), with rate constants of 0.234 and 0.193/hr., respectively. Montaña et al. (2010) also carried out a kinetic and mechanistic study on riboflavin photo-generated reactive oxygen species and determined the induced degradation of isoflavones (quercetin, morin, and rutin), the extent of which depended upon differences in molecular structure of the reactants, which further emphasized the importance of combined reactants in terms of formulation and protective measures that are required for proper storage stability; reaction kinetics were reported to follow first-order kinetics. Cardoso et al. (2012) reviewed the mechanisms of riboflavin as a photosensitizer looking at the effects on overall quality of foods and the effect on human health; they found th at both carotenoids and polyphenols can counteract degradation effects induced by riboflavin exposed to light, although by different mechanisms. Riboflavin is considered to be relatively stable during food processing and storage, except under light, where absorption of light will produce excited triplet state riboflavin. On the other hand, as in the case of most water-soluble vitamins, substantial losses of riboflavin in many food products may be due to leaching during blanching into the process/cooking water. For instance, Prodanov et al. (2004) found significant losses of B-vitamins leached out during soaking and cooking of various legumes with as much as 70% loss of riboflavin from chickpeas. Thus, blanching operations and cooking of vegetables will result in extensive losses of this vitamin. Several authors (Petrou, et al. 2002) have reported kinetic information of vitamin degradation upon cooking, which in the light of the most recent developments in terms of nutritional claims, it has become information of critical importance to product processing and development. In fact, vitamin losses during cooking need to be accounted for when making nutritional claims in a finished product.

#### 3.3.1.4  Vitamin B3 (Nicotinic Acid/Nicotinamide)

Figure 3.14   Chemical structures of the niacin vitamin B3 group (nicotinic acid and nicotinamide).

#### 3.3.1.5  Vitamin B6 (Pyridoxal/Pyridoxine/Pyridoxamine)

Vitamin B6 is a collective term referring to a group of vitamers that possess similar biological activity and share the main chemical structure of 2-methyl-3-hydroxy-5-hydroxy methyl pyrimidine compounds. Originally identified by Gyorgy in the 1930s as a curative for dermatitis in rats, it was later referred to as pyridoxine (pyridoxol), which in addition to the structure aforementioned it has attached a hydroxymethyl group in the 4-position of the pyrimidine ring (Figure 3.15). The metabolically active form of B6 is pyridoxal-5′-phosphate. Other common vitamers include pyridoxal (with an aldehyde group at the 4-position) and pyridoxamine (with an aminoethyl group at the 4-position), all of which can be readily interconverted into one form or the other and each can exist in a phosphorylated form (Eitenmiller et al., 2008). Pyridoxine and pyridoxamine and their phosphorylated counterparts are the major form of B6 found in plant sources, whereas, pyridoxal and its phosphorylated form are more prevalent in animal foods. The most common commercially available form is pyridoxine hydrochloride. Limited kinetic information is available for the degradation of the vitamin B6 compounds. Several authors have reported the interconversion of the B6 vitamers. Results presented by Gregory and Hiner (1983) indicate that a bidirectional conversion of pyridoxal and pyridoxamine during processing exists. Prior to this work, Gregory and Kirk (1978) had reported on the rapid conversion of pyridoxamine to pyridoxal during storage at low moisture content in systems containing protein and reducing sugars, although the reverse reaction was not detected. Yonker (1984) had also observed interconversion of pyridoxamine-5′-phosphate to pyridoxamine to pyridoxine and pyridoxal.

Figure 3.15   Chemical structures of the vitamin B6 group (pyridoxine, pyridoxamine, pyridoxal).

Under storage conditions, Gregory and Kirk (1978) found first-order kinetics for the degradation of the different vitamers in model systems. Pyridoxamine appeared to be the vitamer with the highest complexity in its degradation mechanisms and kinetics. In systems fortified with pyridoxamine, the degradation of this vitamer followed first-order kinetics with conversion into pyridoxal through a transamination process. The limiting factor in this case was the degradation of pyridoxal since the reverse reaction, pyridoxal conversion into pyridoxamine, was not significant.

Navankasattusas and Lund (1982) reported on the stability of vitamin B6 vitamers in phosphate buffer solutions and cauliflower puree at high temperature, in the range 110°C–140°C. The authors reported that the thermal degradation of pyridoxamine followed pseudo-first-order reaction kinetics, whereby the degradation of the vitamer appeared to be slightly dependent on the initial concentration, while the degradation of pyridoxine and pyridoxal followed 1.5 and second-order kinetics, respectively. For the case of cauliflower puree, the kinetics of degradation were observed to deviate from first-order reaction kinetics throughout the entire heating time. Working with a model food system simulating a ready-to-eat breakfast cereal, Evans et al. (1981) determined that the degradation of pyridoxine followed first-order kinetics for the range 155°C–200°C.

Limited kinetic information is available with regard to the influence of pH on the stability of the different vitamers, although alkali pH has been shown to enhance the decomposition of all the B6 vitamers. Saidi and Warthesen (1983) evaluated the effect of pH as well as water activity, light, and temperature on B6 vitamer model systems. Pyridoxine was found to be very stable in the pH range 4.0–7.0 when held at 40°C and 60°C for up to 140 days. For the case of pyridoxamine under the same conditions, the authors indicated that the reaction followed first-order kinetics causing more vitamin losses. Pyridoxamine degradation appeared to follow the trend of higher degradation upon an increase in pH. The authors, however, indicated that the effect of pH was not totally clear. Experiments carried out in model systems, indicated that pyridoxal was much more light sensitive than pyridoxine or pyridoxamine. In general, the stability of vitamin B6 has been observed to be influenced by pH, light and temperature; however, additional information is needed to clearly characterize the effect of these parameters on the kinetics of degradation and on the mechanisms involved. Kinetic information on vitamin B6 stability in food products is reported in Table 3.3.

Information reported by Paul and Southgate (1978) indicated a substantial loss (~40%) of vitamin B6 during cooking of vegetables. Losses were significant in both root and leafy vegetables. Their information highlights significant losses of thiamine, folate, vitamin C, and vitamin B6 expected during industrial blanching operations, much of which has been later determined to be accounted for through leaching losses rather than degradation of the individual vitamers, such as the case found by Delchier et al. (2013) where intact folates were found in the discarded waste water of such operations with a total loss of 20%–25% of vitamer from the vegetables.

#### 3.3.1.6  Vitamin B12 (Cyanocobalamin)

Vitamin B12 is chemically the largest and most complex of all the vitamins, composed of a central cobalt atom planarly coordinated via nitrogen atoms to a porphyrin-like group referred to as corrin with axial coordination sites occupied by a 5,6-dimethylbenzimidazole base and a cyano group. Vitamin B12 is also referred to as cyanocobalamin and part of a larger group collectively known as cobalamins and plays a critical role in the methylation process as well as in lipid and carbohydrate metabolism. It is synthesized by a select group of microorganisms, and its main source in foods is of animal origin (e.g., meats [beef liver], fish, eggs, milk); consequently, deficiency risks commonly exist among groups with low intake of animal products (Gille and Schmid, 2015). Deficiency is particularly critical during pregnancy and lactation with potential risks of megaloblastic anemia (Pawlak et al., 2013; Griebe, 2017). One of the predominant forms of this vitamin is referred to as coenzyme B12, where the cyano group at the sixth coordination position is substituted by 5-deoxyadenosine, attached to the cobalt atom via a methylene group. Another common form found in foods is the hydroxocobalamin (also called vitamin B12a or hydroxo-B12), where the cyano group is replaced with a hydroxy group (Farquharson and Adams, 1976; Schneider, 1987). Other cobalamins include methyl- (CH3), nitrito- (NO2), and sufito- (HSO3) groups substituting for the cyano group. Vitamin B12 analogues refer to a group where the 5,6-dimethylbenzimidazole base is replaced with other substituent groups (Figure 3.16).

Figure 3.16   Chemical structures of vitamin B12 and cobalamin cofactors. (adapted from Matthews, 1984).

In general, limited kinetic information is available on the stability of vitamin B12. It has been reported, however, that this vitamin is slightly unstable in mild acid or alkaline solutions. In the pH range 4.0 to 7.0, this vitamin appears to have good stability. The presence of compounds such as ascorbic acid has been reported to influence the destruction of this vitamin by authors such as Herbert and Jacob (1974), while others such as Newmark et al. (1976) did not appear to detect any significant difference in food systems containing ascorbic acid. Iron, either ionic or in complexed forms, has been found to provide vitamin stability in the presence of ascorbic acid in liver extracts and pharmaceutical formulations (Shenoy and Ramasarma, 1955). It is considered that the stability of vitamin B12 in food systems is very different from that found in pharmaceutical formulations or model systems, since vitamin B12 in foods is tightly bound. For instance, in liver, cobalamin is present in the form of a coenzyme bound to a liver protein. Stability has been attributed to reduced accessibility of the vitamin for chemical attack.

Information on vitamin B12 retention when food products are heated or processed using microwaves is limited. Watanabe et al. (1998) reported that appreciable losses (~30%–40%) of vitamin B12 occurred when microwave heating raw beef, pork, and milk. The authors also reported that when microwave heating hydroxo vitamin B12, which predominates in foods, two biologically inactive degradation compounds were identified. Very limited information is available on other food-occurring vitamin B12 analogues with different β-ligands, such as methyl vitamin B12 and 5′-deoxyadenosyl vitamin B12. A more recent study on stability of different forms of B12 (commercially prepared cyano- and hydroxo-cobalamins, and an in situ form of B12 from a Propionibacterium freudenreichii culture) during different stages of the bread manufacturing process was carried out by Edelmann et al. (2016). They observed the proofing stage did not significantly affect vitamer levels, but 21%–31% of the hydroxocobalamin was lost during the baking step with straight- and sponge-dough processes; whereas, the cyanocobalamin form and in situ prepared B12 were stable. In sourdough baking, however, both commercial vitamers had losses (23% of cyanocobalamin and 44% of hydroxocobalamin), as well as the in situ B12, as determined by both micro (using L. delbrueckii) and HPLC methods of analysis. The authors found comparable results between methods of analysis for the commercial vitamers, but the micro method was not suitable for analysis of the in situ B12, perhaps due to the presence of other growth stimulating compounds, and thus resulting in overestimation. Considering the importance of B12 in the diet from a nutritional perspective, being able to incorporate this nutrient in a staple food form such as bread would facilitate better nutrition.

#### 3.3.1.7  Folates (Pteroylpolyglutamates)

Folates or folacin refer to a large group of heterocyclic derivatives with similar biological function and a common basic structure, N-[4-[{(2-amino-1, 4-dihydro-4-oxo-6-pteridinyl)-methyl} amino] benzoyl] glutamic acid, with or without additional L-glutamic acid residues conjugated via peptide linkages through the γ–carboxyl groups of succeeding glutamate molecules. Since its discovery as an important dietary factor in the early 1930s, it has undergone a series of name changes including vitamin M, vitamin U, vitamin Bc, B9, and L. casei factor. Folate deficiencies have become increasingly a worldwide concern at all socioeconomic levels. It is a common cause of megaloblastic anemia and is either directly or indirectly responsible for the defective synthesis of nucleic acids, frequently occurring in newborn infants. This is usually found due to a folate deficiency during gestation often subsequently resulting in intellectual disability. This group of compounds, of great nutritional significance, has not received adequate attention from the point of view of kinetics. In fact, a large number of parameters can affect the stability of folates, including pH, water activity, temperature, oxygen availability, light, metal traces, etc. Moreover, the stability is dependent upon the particular vitamer under consideration. Some of the most important derivatives of this group include 5-methyltetrahydrofolic, tetrahydrofolic, dihydrofolic, 5-formyltetrahydrofolic, and folic acids. Due to complications in the separation of this extremely large number of individual derivatives in this group of vitamins, a thorough analysis for the stability of the different vitamers has seldom been carried out. However, because of their individual biological activity, they each need to be taken into consideration. In fact, the literature is very scarce when it comes to the stability of folates in foods other than the parent compound, folic acid. Most information corresponds to model systems or buffer solutions, except for cases such as apple and tomato juices, for which kinetic information on 5-methyl tetrahydrofolate (Mnkeni and Beveridge, 1983) as well as on folic acid (Mnkeni and Beveridge, 1982) has been reported. Several authors, including Hawkes and Villota (1986), working with model systems, indicated that folic acid had greater stability as compared to tetrahydrofolic acid and 5-methyl-tetrahydrofolic acid, also biologically available compounds. It was also indicated that the degradation of these three folates followed first-order kinetics within narrow temperature ranges.

Oxidation of tetrahydrofolate (THF) or dihydrofolate (DHF) generally results in loss of the side chain, especially at neutral and low pH (Maruyama et al. 1978). THF has been shown to follow a number of degradation pathways in the presence of air, where both the rate and the mechanisms involved are highly dependent on the pH of the system (Reed and Archer, 1980). It should be mentioned that under neutral and acidic conditions, tetrahydrofolate is degraded to p-aminobenzoyl glutamates and pterin products with no vitamin activity. At higher pH, DHF is a product of the reaction with vitamin activity, but undergoes further oxidation to compounds without any activity. On the other hand, as summarized by Hawkes and Villota (1989a), folic acid is stable under anaerobic conditions in alkaline environment, although as reported by Temple et al. (1981), opening of the pyrimidine ring forming 2-pyrazine carboxylic acid will occur over long periods of storage. Under aerobic conditions, however, degradation will result in cleavage of the side chain to p-aminobenzoyl glutamic acid plus pterin-6-carboxylic acid. Acid hydrolysis, on the other hand, in the presence of oxygen yields a 6-methyl pterin. Hawkes and Villota (1986) indicated that the stability of folic acid, tetrahydrofolic acid, and 5-methyltetrahydrofolic acid decreased with a decrease in pH for the range 7.0–2.0. Folic acid solutions have also been shown to be sensitive to light and may undergo photodecomposition to p-aminobenzoyl-glutamic acid plus pterin-6-carboxylic acid (Lowry et al., 1949).

Most of the literature information appears to indicate that the degradation of folates follows a true first-order reaction. However, the effect of initial concentration has almost never been monitored. Studies have indicated that initial concentration is an important consideration for the kinetics of folate degradation, thus, following pseudo-first-order reaction kinetics. It has also been pointed out that the temperature range studied will affect the mechanism of degradation of the folates, thus resulting in different energies of activation (Hawkes, 1988).

Based on the work presented by different authors and summarized by Hawkes and Villota (1989a), it is clear that the presence of oxygen affects the specific pathways of degradation of folic acid. Considering the oxidative pathways for folic acid, it is obvious that aerobic and anaerobic degradation of the vitamin may occur simultaneously (Figure 3.17). This is of particular importance when fortifying food products subjected to various deleterious processing techniques such as in the case with spray dried fortified formulations (Hawkes and Villota, 1989b). Because of the high complexity existing in food systems, seldom has a clear characterization of the mechanisms involved been reported along with kinetic information. Li et al. (2011) reported on the stability of folic acid incorporated into a complex matrix utilizing a reconstituted fortified extruded rice product, “Ultra Rice”. The composition was a rice flour base with added nutrients including vitamins A and B1, stabilizers, and different sources of iron and titanium dioxide whitener, used to fortify white rice in developing countries. They found degradation during storage (4°C, 60% RH) to follow pseudo-first-order kinetics with greater than 60% retention of folic acid after 9 months, regardless of the iron source used, although there were some detrimental effects with the presence of titanium dioxide used as a whitening agent. Although there was no mention of folate encapsulation in this particular study, Shrestha et al. (2012) found improved stability by using an encapsulated form of 5-methyl THF during extrusion over a range of temperatures (100–150°C), with retentions of 84%–94.5% with the encapsulated form vs. the non-encapsulated form (65.3%–83.2%).

Figure 3.17   Degradation pathways of folic acid. (1) Hutchings et al., 1948; (2) Waller et al., 1950; (3) Baugh et al., 1979; (4) Temple et al., 1981; (5) Stokstad et al., 1948; (6) Brown et al., 1974; and Maruyama et al., 1978; (7) Lowry et al., 1949; (8) Reed and Archer, 1979. (from Hawkes and Villota, 1989a).

For the particular case of folate degradation, an added problem corresponds to the methodology by which folates are determined. At the time of the first edition of this chapter, it was pointed out that since microbiological tests were more widely accepted because of their ability to monitor the bioavailability of the different folates, less valid kinetic information has become available for the stability of these individual vitamers. Unfortunately, over the past decade, not much has changed in that respect. Rader et al. (2000) point out in a discussion on compliance for mandatory folic acid fortification of enriched cereal grain products in the U.S. that a more accurate methodology for free folic acid is needed over traditional microbiological assays. This concern is targeted particularly for distinguishing between specific forms of folate and total versus free folates. The need for better methodology impacts not only health concerns and proper nutritional labeling, but also costs and problems associated with over fortification of food products. It should be stressed that from the point of view of kinetics, the specificity and accuracy of the assay procedure is of critical importance to mathematically describe and understand the mechanisms of deterioration involved in the reaction. Moreover, the fact that free and bound folates have different stability, accurate techniques to monitor both are required. Unfortunately, a great deal of technical work is still needed to measure folates in food systems using selective and accurate techniques such as HPLC-MS. Release of the bound folates also needs to be properly controlled. Current methods for the measurement of bound folates have not yet reached the level of development needed to use the information in kinetic studies. Some more recent studies have shown some progress in the methodology in folate analysis through a stable isotope liquid chromatography-mass spectrometric (LC-MS) method for the quantitation of folic acid and 5-methyltetrahydrofolic acid in food systems (Pawlosky and Flanagan, 2001; Thomas et al., 2003; and Doherty and Beecher, 2003).

In summary, although some information is available for the stability and kinetics of folates as reported in Table 3.3, a better understanding of the mechanisms taking place in food systems is still needed.

#### 3.3.1.8  Pantothenic Acid

Pantothenic acid (or the salt form, pantothenate), D (+)-N-(2,4-dihydroxy-3,3-dimethylbutyryl)-β-alanine, is a member of the B-complex vitamins, also referred to as vitamin B5, “chick antidermatitis factor”, or “yeast growth factor”. It is ubiquitously present in almost all plant and animal tissue and is an essential precursor for the biosynthesis of coenzyme A. The biochemistry of pantothenic acid has been recently reviewed (Miller and Rucker, 2012; Gonzalez-Lopez et al., 2016). Prevalent sources of pantothenic acid include chicken, lean beef, potatoes, oat cereals, tomatoes, eggs, broccoli, whole grains (Rucker and Bauerly, 2012), liver, kidney, queen bee royal jelly, rice bran, peanuts, and peanut butter (Merck, 2001; Kelly, 2011). Only the natural dextrorotary form has vitamin activity. Pantothenic acid is most stable in the pH range 4–7. It undergoes alkaline hydrolysis to yield pantoic acid and β-alanine (Frost, 1943), or γ-lactone and pantoic acid under acid hydrolysis. In its acid form, it is present as a water soluble viscous yellow oil, and in its salt form as calcium pantothenate, it is a colorless crystalline substance. This vitamin has also been reported to be susceptible to thermal decomposition. Frost and McIntire (1944) determined that hydrolysis of pantothenic acid in the temperature range 10–100°C and in the pH range 3.7–4.0 followed first-order kinetics. Hamm and Lund (1978) working with buffer systems and meat and pea purees indicated that pantothenic acid was more stable in food products than in model systems, thus clearly indicating that the stability of this vitamin improved due to the presence of other compounds in food products. The degradation appeared to follow first-order kinetics (Table 3.3). The authors also indicated that for the systems studied, the vitamin was quite heat stable, contrary to other results reported in the literature indicating vitamin instability during processing (Schroeder, 1971). Cheng and Eitenmiller (1988) also reported that steam blanching, water blanching, canning, and frozen storage caused losses of pantothenic acid in spinach and broccoli to a different degree. Water blanching, in particular, resulted in large losses of pantothenic acid in both spinach and broccoli. More recently, Muhamed et al. (2015) reported appreciable amounts of pantothenic acid in tropical Averrhoa bilimbi fruits, along with nicotinic acid and catechin, recoverable for use as food supplements. The kinetics of these three components were investigated at temperatures ranging from 90°C to 120°C, as quantified by HPLC analysis, and found to follow first-order kinetics (Table 3.3); it was indicated that of those three components, pantothenic acid had the highest sensitivity to temperature change, based on their energy of activation. First-order kinetics for pantothenic acid degradation was also demonstrated by Gutzeit et al. (2007b) during storage of Sea Buckthorn juice stored at 25°C–40°C, resulting in a 18% loss after 7 days at ambient temperature. In general, however, it appears that due to problems associated with assay procedures, kinetic data are limited and inconclusive. Some of the pantothenic acid analogues include panthenol (pantothenol), pantethine (two pantetheine molecules linked by a disulfide bridge), and a calcium salt (Ca-pantothenate) which is commercially available and commonly used in extruded feeds for animals (chemical structures presented in Figure 3.18).

Figure 3.18   Chemical structures of pantothenic acid and some of its analogs, pantothenol, calcium pantothenate, and pantethine. (adapted from Rucker and Bauerly, 2012).

#### 3.3.1.9  Biotin

Biotin, also known as vitamin B7 (formerly vitamin H, “egg white injury factor”, coenzyme R) and chemically as hexahydro-2-oxo-1-H-thiene[3,4-d]imidazole-4-pentanoic acid, is a highly biologically active growth factor found in all living cells. The structure of biotin consists of fused imidazole and tetrahydrothiophene rings and a carboxyl-containing side chain. In the case of the biotin derivative, oxybiotin, the sulfur of the tetrahydrothiophene ring is replaced by oxygen, making it a tetrahydrofuran ring, and acts as a substitute for biotin in vivo (Figure 3.19). Biotin plays an essential role as a coenzyme in carboxylation, trancarboxylation, and decarboxylation reactions and functions in catalyzing critical steps in metabolism of fatty acids, amino acids, and in gluconeogenesis; it is also reported to influence regulation of gene expression (Zempleni et al., 2012). Its deficiency has been found to result in dermatitis and perosis in chicks and poults, basically reducing the activity of biotin-dependent enzymes (Dobson, 1970); other deficiencies of biotin in various animals, including mice, have shown to result in potential birth defects (Mock et al., 2003). Although there have not been many reports on deficiencies in humans, lack of biotin has been found to cause thinning of hair, skin rashes, lethargy, and depression. Zempleni and Mock (1999) and more recently Mock (2017) have reviewed biotin biochemistry from a nutritional requirement perspective as well as a potential therapeutic agent. Currently, there is no official USDA listing of biotin contents in foods; however, based on reports from other major population areas, the average intake of biotin has been estimated around 35–70 μg/day (Zempleni and Mock, 1999). Watanabe et al. (2014) have published tables of biotin contents of selected food items based on common foods in Japan, estimating biotin consumption to be about 50 μg/day for adults; they suggest this data could serve as a basis for developing more official required levels at a global level.

Figure 3.19   Some chemical structures of biotin and derivatives (from Scheiner, 1985).

Some of the richest sources of biotin are beef liver, yeast, peanuts, kidney, chocolate, egg yolk, soybeans, mushrooms; it can also be synthesized by bacteria in the gut, although it is unclear as to how much can be absorbed (Bhagavan and Ha, 2015). Suri and Tanumihardjo (2016) have reported biotin contents in whole-grain maize ﬂour to be 7.3 and 1.4 μg/100 g in degermed maize ﬂour, based on microbiological assays, indicating a reasonable source of biotin. Biotin occurs naturally as the d-isomer and is present as in a conjugated or bound form to proteins and polypeptides. In fact, raw egg whites can produce a biotin deficiency due to the glycoprotein, avidin, which forms a complex with biotin, rendering it unavailable. However, since egg white is heat labile, prolonged heating of egg white denatures avidin and destroys its biotin-binding capacity. The d-isomer has about twice the biological activity of the d,l-isomer, where the l-isomer is biologically inactive. Biotin is reported as stable to heat, oxygen, in moderately acid (pH 4.0) and neutral solutions up to pH 9.0, but less stable under alkaline conditions (Merck, 2001; Marcus, 2015). Hoppner and Lampi (1993) reported on relative losses of biotin and pantothenic acid in various legumes and found biotin to be significantly more stable. After 24 hours soaking, followed by conventional ­cooking for 20 minutes, they found 90% retention of biotin compared to only 44% pantothenic acid, but no mention was made of their kinetics of degradation. It is expected that the cooking and processing of foods can convert biotin to the oxidized forms. It has been reported that the different biotin derivatives such as desthiobiotin, oxybiotin, biotinol, norbiotin, biotin sulfoxide, and biotin sulphone have different biological activities. However, limited information is available on the stability of biotin and its derivatives. It was not until 1966 that biotin became more important commercially, particularly in fortification of feeds for livestock such as poultry and swine as well as pets. Watson and Marsh (2001) developed a patent for a biotin supplement for animals to withstand extrusion processing. Several reviews on the biochemistry of biotin have been published (Gyorgy and Langer, 1968; Zempleni et al., 2012; Mock, 2017), but few pertaining to stability in processing. Although advances have been made on the analysis of this vitamin (Sim et al., 2016), most efforts are working with pure vitamins, and not accounting for the difficulties of extraction from foods. Most analyses to date still use microbiological assays which lack chemical specificity, as well as producing confounding results due to the fact most biotin in foods is bound to protein. Avidin and Streptavidin-binding assays have also been used, but, again, anomalies may occur since biotin derivatives with similar structure may interfere with proper levels of true biotin being reported. Watanabe (2015) also points out critical aspects of developing more global standardized tables; researchers will need to be cognizant of consistency of methodology, sampling techniques, and documentation of different types of processing to which various food types may be subjected. It is clear that quantitative assessments of biotin still present a challenge, and that more work in this area is needed. Understandably, this contributes to the lack of information on stability data during processing of foods.

#### 3.3.2.1  Vitamin A

Vitamin A is generally classified into two ma in groups possessing biological activity: (a) C20 unsaturated hydrocarbons including retinol and its derivatives from animal origin and (b) C40 unsaturated hydrocarbons including carotene and a number of other provitamin A carotenoids of plant origin. Vitamin A is a generic descriptor for all β-ionone derivatives with the biological activity of all-trans-retinol (also referred to as vitamin A alcohol or vitamin A1). Provitamin A carotenoid is a generic descriptor for all carotenoids with the qualitative activity of β-carotene. Natural forms of vitamin A predominantly occur in the more stable form of all-trans-retinyl esters, along with small levels of 13-cis-retinol as found in fish livers. Other natural retinyl derivatives present include esters of 3-dehydroretinol (vitamin A2, with ~40% retinol activity) and retinal (vitamin A aldehyde with ~90% retinol activity). Commercially available forms of synthetic vitamin A may be found as either retinol acetate or palmitate and can be supplied in crystalline form or as concentrates in oil, emulsions, or in encapsulated forms. Similarly, different forms of provitamin A carotenoids are available. Some of the carotenoids with significant provitamin A activity include β-carotene (100%); 3,4-dehydro-β-carotene (75%); β-apo-8′-carotenal (72%); β-apo-12′-carotenal (120%); 3-hydroxy-β-carotene (50%–60%); α-carotene (50%–54%); and γ-carotene (42%–50%) (Bauernfeind, 1972). Because of the wide variety of forms of vitamin A and provitamin A carotenoids, labeling requirements report total vitamin A activity in terms of “retinol equivalents” (RE), where one RE is equivalent to 1 µg retinol, 6 µg β-carotene, and 12 µg other provitamin carotenoids. In terms of international units (IU), one RE = 3.33 IU retinol or 10 IU β-carotene (NRC, 1980).

A variety of pathways have been proposed to describe the destruction or autooxidation of carotenoids, depending on the process conditions, the presence of light, the presence of oxygen, and the composition of the system including peroxidizing lipids or enzymatic activity. A summary of the most important mechanisms is presented in Figure 3.20. It appears that at high temperatures, the destruction of carotenoids results in fragmentation including the formation of aromatic compounds. In the absence of oxygen, trans–cis isomerization seems to be one of the most important mechanisms of deterioration. Light catalyzed oxidation appears to result primarily in the formation of mutachrome. In general, the degradation of carotenoids has been considered to be an autooxidation reaction involving the formation of free radicals, thus giving origin to a propagation reaction and finally, a termination stage. A variety of approaches have been taken to describe the kinetics of carotenoid degradation. For instance, Ramakrisnan and Francis (1979a), using a microcrystalline cellulose/starch model system, stated that since this is an autooxidative process, the two principal reactants are oxygen and carotenoids, and since oxygen is in excess, the reaction is expected to follow first-order reaction kinetics. Other authors such as Chou and Breene (1972), Baloch et al. (1977c), Stefanovich and Karel (1982), Goldman et al. (1983), and Pesek and Warthesen (1987, 1988) have also reported that a first-order reaction would describe within limits, the degradation of carotenoids in model systems. On the other hand, authors such as Quackenbush (1963) working with corn, Baloch et al. (1977a,b) working with carrots, and Stefanovich and Karel (1982) working with butternut squash, sweet potato, and yellow corn, also concluded that first-order kinetics could describe the degradation of carotenoids, although the model did not take into account an induction period. Haralampu and Karel (1983) working with dehydrated sweet potato, indicated that the degradation of β-carotene was described with the use of pseudo-first-order reaction kinetics. Taking into consideration that carotenoid degradation is a chain reaction, other authors such as Alekseev et al. (1968), Finkel’shtein et al. (1974), Gagarina et al. (1970), Goldman et al. (1983), Stefanovich and Karel (1982), and Smith-Molina (1983) have applied a simplified free radical recombination model to describe the autooxidation of carotenoids. Finkel’shtein et al. (1973) indicated that the autooxidation of β-carotene followed the same basic trends regardless of the conditions, namely, (a) an induction period, (b) an acceleration period, (c) a stationary induction period, and (d) a retardation period.

Figure 3.20   Some mechanisms of β-carotene degradation. (1) Seely and Myer, 1971; (2i) Seely and Myer, 1971; (2ii) Pesek and Warthesen, 1988; (3) Seely and Myer, 1971; (4) Walter et al., 1970; (5) Sweeney and Marsh, 1971, 1970; (6i) Ishiwatari, 1980; Mader, 1964; Day and Erdman, 1963; (6ii) Ishiwatari, 1980; (6iii) Schreir et al., 1979; (6iv) Onyewu et al., 1982; (6v) Ouyang et al., 1980; (7) Chen et al., 1995.

Limited information is available with regard to the effect of initial concentration on the degradation of carotenoids. Budowski and Bondi (1960) working with model systems, indicated that the higher the initial concentration of carotenoids, the shorter the induction period, with an associated faster reaction rate. Gagarina et al. (1970) also indicated that when working with β-carotene in chloroform, the time required for complete consumption of the carotene was shorter upon an increase in the initial concentration. Similarly, Stefanovich and Karel (1982), working with β-carotene in a model system, observed that the kinetics of degradation were dependent on the initial concentration. The authors indicated that the dependence was related to the thickness of the carotene layer since the diffusion of oxygen becomes a limiting factor in the oxidation reaction. Smith-Molina (1983) indicated that in liquid systems the rate of degradation of carotenoids increased with an increase in the initial concentration. The high mobility of the reactants including free radicals was assumed to be the reason for the observed trends. The author also reported that when taking into consideration the history of the sample, systems where degradation of carotenoids had proceeded to a larger extent were also more reactive. Similarly, Goldman et al. (1983) observed that carotene degradation was strongly affected by the presence of free-radical initiators.

Although a certain amount of information is available for the kinetics of degradation of carotenoids, limited work has been done in trying to follow the most important mechanisms of deterioration on the degradation of carotenoids in food systems (Table 3.3). Moreover, analytical techniques have been limited in their ability to monitor isomerization of the carotenoids, which is expected to be one of the most critical changes occurring as a result of processing.

Working at high temperatures, Wilkinson et al. (1981) indicated that the destruction of vitamin A in beef liver puree, measured as trans-retinol, followed a first-order reaction. Wilkinson et al. (1982) indicated that increased concentrations of copper increased the losses of vitamin A, whereas increased pH (5.6–7.0) resulted in a decrease. The authors appeared to believe that changes in the copper concentration modified the mechanism by which the vitamin was lost.

Chen et al. (1995) reported effects of different processing techniques on stability of various carotenoids in carrot juice including α-carotene, β-carotene, and lutein. Depletion and/or conversion of the trans-isomeric forms to various cis-forms of the vitamers were monitored by HPLC. It was reported that acidification of fresh carrot juice to pH 4.0, followed by heating to 105°C/25 sec showed little change. HTST heating at 110°C/30 sec and 120°C/30 sec showed progressively higher levels of loss of the trans-carotenoids with the predominant cis-isomers being 13-cis-β-carotene, followed by 13-cis-lutein and 15-cis-α-carotene. Retort processing (121°C/30 min) showed the highest level of carotenoid destruction with formation of 13, 15-di-cis-β-carotene. Color changes as monitored by Lab-values showed decreases that paralleled losses of the trans-forms and increase in cis-forms. Lin and Chen (2005) indicated that in tomato juice during storage, both temperature and light would influence the proportion of isomers that predominate over time (4–35°C/12 wks). For instance, they found that all-trans-β-carotene degraded to di-cis-, 9-cis-, and 13-cis-β-carotene isomers after storage under light depending on temperature. In the absence of light, degradation products included 5-cis-, 9-cis-, and 13-cis-β-carotene, again, depending on temperature.

In food systems the effect of water on carotene oxidation appears to be dependent on composition (Kanner et al. 1978). As reported by Arya et al. (1979) and Maloney et al. (1966) an increase in water content could mobilize the pro-oxidant factors in the matrix or expose new sites in the matrix resulting in accelerated oxidation. On the other hand, Haralampu and Karel (1983) indicated that for the case of sweet potato flour, the degradation of β-carotene was inversely proportional to the water activity. With respect to the photosensitized oxidation of β-carotene, mutachrome has been identified to be the most important oxidation product, although other compounds such as aurochrome and a number of compounds absorbing in the violet and near ultraviolet region have also been detected. The 5,6-monoepoxide was not detected in significant amounts, although this compound is unstable and can be converted into mutachrome by acid traces and catalysts (Seely and Meyer, 1971). The authors determined that 5,6-monoepoxide was not the first product of photochemical oxidation. Pesek and Warthesen (1988) working with model dispersions indicated that the photodegradation of β-carotene followed a first-order reaction that was affected by temperature; the physical state of the sample, frozen vs. liquid; and the microenvironment. The authors also indicated that the presence of cis-isomers and the rate of their formation was larger than that of their degradation. Moreover, it had been previously shown by Zechmeister (1944) that the cis-isomers may convert back to the all-trans form, which is more stable, or undergo further degradation. Lemmens et al. (2011) have discussed the overall isomerization of all-trans-β-carotene and subsequent formation of different cis-isomers (9-, 13-, 15-) during thermal processing of carrot puree (80–150°C). They proposed a fractional conversion model which considers the interconversion reactions between all-trans-β-carotene and its cis-isomers until an equilibrium state is reached during prolonged heating. This would be plausible due to a protective effect of the carrot structural matrix; hence, an important factor when developing models for real food systems.

Heat stability studies of α-carotene and β-carotene indicate that β-carotene is about 1.9 times more susceptible to heat damage than α-carotene during normal blanching and cooking operations (Baloch et al. 1977a). With regard to cooking losses, Sweeney and March (1971), indicated that heating promotes the cis-trans isomerization of carotenoids in vegetables, with an increase of the cis isomers. In general, literature reports clearly indicate the instability of carotenoids at high temperatures such as those encountered in canning and drying operations, particularly in high temperature-long time type processes, while freezing and low temperature processing normally results in much lower losses.

Of critical importance in processing, particularly fruits and vegetables, would be the isomerization of all-trans-β-carotene sensitized by chlorophylls. As reported by O’Neil and Schwartz, (1995), the photoisomerization of β-carotene sensitized by chlorophyll will result in 9-, 13-, and 15-cis-β-carotene primarily, with a higher ratio of the 9-cis-isomer.

Most past studies on degradation of β-carotene as a function of temperature have been carried out with relatively static conditions at individual temperatures, without necessarily considering the dynamics of the specific industrial process of concern. González-Reza et al. (2015) studied degradation kinetics of β-carotene nanocapsules as used in a matrix subjected to a scraped surface heat exchanger. The process variables considered, other than temperature, were volumet ric flow (2.4 × 10−6–4.8 × 10−6 m3/sec), steam pressure (49–147 kPa), and rotor speed (10.4–31.2/sec); dispersion matrix composition was a 0.5 g carboxymethyl cellulose (CMC)/100 g water incorporating the equivalency of 70 μg/ml of β-carotene in nanocapsules. Using a traditional Arrhenius relationship for degradation of the active component to determine rate constants and Ea’s, the authors developed a fractional factorial RSM (response surface methodology) design model to factor in the influences of processing variables. They found the most influencing of the variables towards β-carotene degradation were steam pressure and volumetric flow rate, with optimal values for maximizing retention at 98 kPa and 4.4 × 10−6 m3/sec, respectively and a rotor speed of 38.29/sec. Optimum values for rate constant were reported at 0.049 min−1 and Ea at 171.5 kJ/mol (41 kcal/mol), with a total loss of β-carotene at 6.93%.

#### 3.3.2.2  Vitamin D

Vitamin D has been closely scrutinized over the past decades due to many reoccurring incidences of its hypovitaminosis worldwide (Kumar et al., 2009; Edmonds, 2010; Vierucci et al., 2013; Cashman et al, 2016). Vitamin D is well recognized for its role in growth and bone health. Originally in the early 1920s, the term vitamin D was given to the active component present in cod liver oil, which could cure or prevent rickets, a disease resulting in weakness and deformation of the bones. Later, the introduction of vitamin D fortified milk in the 1930s in the U.S. essentially eliminated rickets, as caused by a vitamin D deficiency. However, emerging scientific research indicates more far-reaching health benefits of vitamin D; other symptoms associated with its deficiency have been identified including increased risk of cancer, heart disease, infection, multiple sclerosis, diabetes, rheumatoid arthritis, and depression in the elderly (Mazahery and von Hurst, 2015; Lips, 2010; Milaneschi et al., 2010). Along with the fact that the incidence of rickets seems to be reemerging, due to lack of exposure to the sun in certain winter climates along with increased usage of sunscreen in warm climates (Lamberg-Allardt, 2006; Engelsen, 2010; Lips, 2010) in combination with a lack of general foods with high vitamin D content, recommendations are being made to increase the minimum required daily dosage of vitamin D from its current minimum (U.S.) of 400 IU to 1000 IU/day, particularly for the elderly. In 2016, the U.S. Food and Drug Administration announced that it would allow for manufacturers of milk and plant-based milk and yogurt alternatives to add more vitamin D to their products; to further emphasize the importance of this vitamin, starting in (2020), it will be a nutrient required to be declared on the new U.S. Nutrition Facts label (IDFA, 2016). Other products looking more seriously at being considered a good source of vitamin D fortification include bread products (D3 [Nikooyeh et al, 2016] and orange juice (D2 and D3 [Biancuzzo et al., 2010]). Some investigators have also warned of potential differences between the bioavailability of these two vitamers (Itkonen et al, 2016; Tripkovic et al., 2012), despite prior claims of biological equivalency. In fact, studies have been shown in vivo that vitamin D3 is significantly more potent than D2 as monitored by serum levels of the active 25-hydroxyvitamin D metabolite (Heaney et al., 2011; Aramas et al., 2004); this impact can also be influenced by available calcium both in fortified products and in serum levels. There has also been research on synergistic reactions between some forms of vitamin D and vitamin K derivatives in enhancing the maintenance of bone health (Torbergsen et al., 2015). This makes it even more imperative to definitively identify the appropriate derivatives to ensure proper maximum biological availability and stability through various types of processing. Quantitative and analytical data collection for relevant vitamin D derivatives and their rates of degradation in associated products will be critical.

The most important forms of vitamin D are D2 (ergocalciferol) and D3 (cholecalciferol). Vitamin D2, generally sourced from plant materials such as yeast or fungi, is formed by ultraviolet irradiation of the provitamin ergosterol. Similarly, D3, sourced from animal products such as lanolin or fish oil, is formed from the provitamin 7-dehydrocholesterol. The following are also considered to be provitamin D compounds: 22, 23-dehydroergosterol, 7-dehydro-sitosterol, 7-dehydrostigmasterol, and 7-dehydro-campesterol. The D provitamins do not have any vitamin activity unless the B ring is opened between carbons 9 and 10, and a double bond is formed between carbons 10 and 19 forming the 3(beta)-hydroxy-9, 10-seco-5, 7,10(19)-triene derivative (Figure 3.21). Limited information is available on the stability of the provitamin D compounds and the active derivatives, although this vitamin has been reported to be susceptible to oxygen and light. Photochemical transformations of provitamin D give origin to the anti-9:10 isomers, while thermal isomerization yields the syn-9:10 isomers, procalciferol and isopyrocalciferol (Sebrell and Harris, 1971). Yamada et al. (1983) indicated that vitamin D undergoes 1,4-cycloaddition and ene-type reactions with singlet oxygen, generating: a) two carbon (6) epimers of 6,19-epidioxyvitamin D (55%–65% yields) and b) two carbon (6) epimers of the Δ4,7,10(19) 6-hydroperoxide (15%–25% yields). Figure 3.21 presents some of the reaction pathways for Vitamin D as affected by light and heat. With regard to kinetic information, our understanding of the stability of vitamin and provitamin D in food systems is almost non-existent. Li and Min (1998), however, reported first-order rate constants (2 phases) for the degradation of vitamin D2 in model systems as a function of riboflavin concentration in the presence of light. The authors indicated riboflavin acted as a photosensitizer and accelerated the oxidation of vitamin D2 by singlet oxygen under light , but had no effect on stability in the absence of light. Further studies showed that the presence of carotenoids could quench singlet oxygen activity and provide stability to vitamin D2 in model systems. Li et al. (2000) reported quenching rate constants for retinol, retinyl acetate, fucoxanthin, and β-carotene (1.22 × 108, 5.98 × 108, 1.78 × 109, 5.00 × 109 M−1s−1, respectively) and indicated that with increasing number of carotenoid double bonds (5, 6, 10, 11, respectively), the quenching rate constant of carotenoid increased. Saffert et al. (2009) studied the effect of package light transmittance on vitamin content (D3, A, and B2) in fortified UHT low and whole fat and pasteurized whole milk; they found major losses of all vitamins after 12 weeks storage at 23°C in clear PET bottles (65% loss of D3, >90% loss of A and 100% loss of B2). By using a high density pigmented PET, it minimized losses to about 20% of the vitamin D, but still had significant losses of the other vitamins A and B2, which were clearly less stable to light; some stability data was calculated and presented in Table 3.3.

Figure 3.21   Some reaction pathways as affected by light or heat for the D vitamers. (adapted from Miller and Norman, 1984 and Li et al., 2000).

Although little actual kinetic data is available for degradation of vitamin D in food systems, with increased awareness for the need of fortification of vitamin D, several authors have reported on general stability during different processes. For instance, Hanson and Metzger (2010) have evaluated vitamin D3 fortification at levels of 100–250 IU/serving in HTST-processed 2% fat milk, UHT 2% fat chocolate milk, and low-fat strawberry yogurt and found no significant vitamin D3 losses during processing or normal shelf-life and no impact on flavor at elevated vitamin D levels; vitamin analysis was carried out using a pre-extraction/saponification step, followed by HPLC. Kazmi et al. (2007) investigated Vitamin D3 fortification by means of two methods, one with pre-dissolved crystalline D3 and the other emulsified D3 in cheddar cheese, yogurt, and ice cream. They found that during three month storage in cheese, the emulsified form was the more stable form; however, in yogurt and ice cream, either form retained suitable retention over the expected life time of the products. Wagner et al. (2008) found similar stability with vitamin D3 incorporated in hard cheeses at different stages of the process, including processing, ripening for 1 year at 3–8°C, or after thermal treatment at 232°C for 5 min.; any losses of vitamin D were accounted for by the amount entrained in the whey. Ganeson et al. (2011) also found Cheddar cheese to be a suitable vehicle for vitamin D3 fortification (emulsion format) with 9 months storage and 90% retention of vitamin D3 and no flavor impact up to 400 IU/serving. Jakobsen and Knuthsen (2014) investigated retention of vitamin D3 in different products such as eggs and margarine as well as both vitamins D3 and D2 in bread. They found stability of vitamin D3 was dependent upon the type of process used. In the case of heating eggs and margarine in an oven at “normal” baking temperatures, retention was at only 39%–45%; however, at frying temperatures, retention was at 82%–84%, similarly for boiled eggs. Retention in bread during baking showed D3 retention at 69%–85% and D2 at 73%–89%; this was also dependent upon the type of bread formula being used. Overall indications showed potential for fortification with vitamin D3 or D2, but optimization of cooking procedures should be taken into account. Success in fortification with vitamin D, of any form, will depend on reliable, repeatable analyses, which have been a challenge, particularly when it comes to extraction from food systems. Rybakova et al. (2008) have reviewed various vitamin D methods and determined HPLC is most likely the method of choice for reliability in a quality control environment.

#### 3.3.2.3  Vitamin E

Tocopherols, or compounds with vitamin E activity, are methyl-substituted hydroxychromans with an isoprenoid side chain. Overall, there are eight different forms of vitamin E identified and are composed of two homologous series: a) four tocopherols with a saturated side chain and b) four tocotrienols with a side chain unsaturated between carbons 3′ and 4′, 7′ and 8′, and 11′ and 12′ (Parrish, 1980), each of which is characterized by the number and position of methyl groups on the chromanol ring (i.e., trimethyl [α-], dimethyl [β- or γ-], or monomethyl [δ-form]), as illustrated in Figure 3.22. Vitamin E is synthesized by plants and predominantly found in plant oils; α-tocopherol is usually found in leaves and other green parts of the plant associated with the chloroplasts; whereas, β-, γ-, δ-vitamers generally occur outside those regions. Wheat germ, olive, and sunflower oils are rich sources of α-tocopherol, while corn and soy oils are rich in γ-tocopherol. Some plant tissues (germ fractions) contain tocotrienols, often in an esterified form, unlike the tocopherols which are present in the free alcohol form. Bioavailability of the E-vitamers has been shown to be dependent upon general fat intake, not only because of the inherent vitamin E content of the consumed food, but when taking vitamin E supplements, the presence of fat aids absorption in vivo (Hayes et al., 2001). It is considered that α-tocopherol is the compound with the most significant biological activity of all the E-vitamers, although it is also the least resistant to oxidation; since tocopherols are mono-ethers of a hydroquinone, they can be easily oxidized. Currently, α-tocopherol is the only form that has been established as being maintained in human plasma and consequently is the only one officially recognized as being a contributor to the RDA (FNB, 2000). In itself, chemically synthesized α-tocopherol can exist as a racemic mixture o f eight stereoisomers (designated as all-rac-α-tocopherol), however, not all have the same biological activity, and only half of those are officially recognized as contributors including the naturally occurring RRR-α-tocopherol and three other 2R-stereoisomeric forms (formerly referred to as dl-α-tocopherol). This, of course, can create further complications in proper calculation of actual levels to be labeled correctly on food packaging. Currently, the IU of vitamin E equals 1 mg all-rac-α-tocopheryl acetate, 0.67 mg RRR-α-tocopherol, or 0.74 mg RRR-α-tocopheryl acetate (FNB, 2000; Trabor, 2007). Some relative biological activities of the various α, β, γ, δ-forms of vitamin E, as based on animal studies, have been reported; it shows that non-α-tocopherols may have less than 20% activity of α-tocopherol, and yet the γ-form is the most prevalent in diets (Combs, 2008). However, suggestions have been made that γ-tocopherol may have greater impact than α-tocopherol in the prevention of Alzheimer’s and cardiovascular diseases (Usoro and Mousa, 2010 and Cordero et al., 2010, respectively), a factor to be considered in the importance of the mechanistic breakdown analysis of vitamin E. Supplements are often in the form of α-tocopherol esters such as α-tocopherol-acetate, -succinate, or -nicotinate; these all have greater stability in terms of shelf-life than naturally occurring α-tocopherol and can be readily hydrolyzed in the gut and absorbed as α-tocopherol (Cheeseman et el. 1995). As a natural antioxidant, tocopherols have been a topic of eminent interest in terms of biological activity; its functions have often been considered widespread such as has been implicated in helping reduce a variety of disease processes, such as deteriorating immune defenses, cataracts, and lipid peroxidation occurring in LDL during atherogenesis, a major cause of coronary heart disease (Hayes et al., 2001). At present there are no confirmatory studies for absolute corroboration of these claims, although it has been suggested that that in special circumstances under controlled population sets, there have been cases of improvement (Vardi et al., 2013). Comprehensive summaries of the most influential factors on the stability of tocopherols and reviews on their biochemical significance have been presented by Bauernfeind (1977, 1980) and Traber (2007), respectively.

Figure 3.22   Chemical structures of vitamin E indicating methyl group positioning for α-, β-, γ-, δ-forms and the stereoisomeric chiral centers at the 2, 4′, 8′ positions (adapted from Merck, 2001 and Trabor, 2007).

Storage studies carried out by Widicus et al. (1980) indicated that the degradation of α-tocopherol followed a first-order reaction in model systems not containing fat. Although, the presence of an autooxidation mechanism was not observed, the involvement of oxygen in the degradation of this compound was determined.

A number of studies have indicated that α-tocopherol is highly susceptible to degradation depending on moisture content, temperature, light, alkali, and the presence of metal ions such as iron and copper. Moreover, tocopherols have been found to be more unstable in peroxidizing systems. One of the main decomposition products of oxidized tocopherols in vivo is α-tocopherolquinone, although several others have been determined in vitro as well including dimers, trimers, dihydroxy compounds, and other quinones (Csallany et al., 1970; Csallany and Draper, 1963; Skinner and Parkhurst, 1964). The chemistry, antioxidant properties, and decomposition products of tocopherols and tocotrienols and their differentiating factors have been reviewed (Kamal-Eldin and Appelqvist, 1996). Some of the α-tocopherol structures and some of their pathways for their degradation are illustrated in Figure 3.23.

Figure 3.23   Some mechanisms of α-tocopherol degradation. (1) John et al., 1939; John and Emte, 1941; (2) Frampton et al., 1960, 1954; (3) Knapp and Tappel, 1961; (4) Knapp and Tappel, 1961; (5) Skinner and Alaupovic, 1963; Nelan and Robeson, 1962; (6) Dürckheimer and Cohen, 1962; Schnudel et al., 1972; (7) Schnudel et al., 1972; (8) Nelan and Robeson, 1962; (9) Csallany and Draper, 1963.

Due to the high instability of this vitamin, processing in particular has been reported to be detrimental to the stability of tocopherols. For instance, Thomas and Calloway (1961) indicated severe α-tocopherol losses (41%–65%) in various meat products as a result of canning. Livingston et al. (1968) reported losses of α-tocopherol ranging from 5% to 33% during the drying of alfalfa. Avocados are known as a good source of vitamin E (2 mg/100 mg fresh produce) and have become a subject for evaluation. Recent studies have shown that ultrasonic application for preservation (power densities 1000–5000 W/L at 23°C and 40°C) decreases natural vitamin E content of the fruit, but addition of the bioavailable α-tocopherol acetate holds up to the process more e fficiently, and thus can replace losses of natural vitamin E during such a process (Fernandes et al., 2016). It is speculated that the presence of H2O2 during the process may have initiated oxidative reactions with the naturally occurring antioxidant, α-tocopherol.

Widicus et al. (1980) indicated that for a non-lipid containing model system the degradation of α-tocopherol was directly related to the water activity, thus suggesting that the system was highly dependent on the rate of diffusion of the reactants. The reaction followed first-order kinetics. Similarly, Jensen (1969) reported higher stabilities of α-tocopherol during the storage of seaweed meal at lower moisture contents for the range 10%–25%. Frias and Vidal-Valverde (2001) reported on the stability of α-, β-, and δ-tocopherols, vitamin A, and thiamine during storage of enteral feeding formulas. Analysis of their data is presented in Table 3.3, along with some of the most relevant information on vitamin E stability in food systems.

Considerable losses of tocopherols have been reported during the storage of oils, as influenced by temperature, time, and the presence of other antioxidants. Since tocopherols are highly susceptible to free radical oxidation, it is obvious that their stability is influenced by the levels of lipid oxidation taking place in a food system. It has also been found that the relative stabilities of natural tocopherols may vary according to the biological source. For instance, Chow and Draper (1974) found that both vitamin E oxidation and peroxide formation occurred more rapidly in corn oil than in soybean oil. In the case of deep-fat frying of potato slices in palm, canola, and a 1:1 blend of oils (170°C–190°C), it was reported that kinetic deterioration of various vitamin E analogues followed an Arrhenius relationship using a fractional order of kinetics greater than one [1.4–1.8], (Mba et al., 2017). By observation of their data, it appears that the γ-forms are less sensitive to changes in temperature than the α-forms for either the tocopherols or tocotrienols. Choe (2013) subjected sunflower oil to temperatures between 40°C and 80°C, with and without light up to 30 days; the author measured degree of oxidation through peroxide value (POV) and conjugated dienoic acid (CDA) values as well as tocopherol degradation by HPLC. Trends showed greater stability of γ-tocopherol compared with α-tocopherol with respect to both type of light conditions.

With regard to the influence of metal traces, Cort et al. (1978) reported that both α- and γ-tocopherols were degraded by Fe3+ and Cu2+. Chelating compounds such as ascorbic acid and EDTA appeared to inhibit the Cu2+ oxidation, while ascorbic acid prevented the Fe3+ oxidation of tocopherols in alcohol solutions.

Tocopherols have been reported to combine with various proteins and amino acids, thus modifying their stability. The conjugate appears to be a protein-tocopherol linkage without the involvement of lipids (Voth and Miller, 1958). Binding affinities of proteins and free amino acids have shown a similar behavior, whereby an increase in the negative charge resulted in an increase in the binding of tocopherols. Thus, relatively positive amino acids such as lysine, arginine, and histidine do not participate in the binding of the tocopherols or modify the affinity of proteins for binding (Voth and Miller, 1958).

Fortification of foods with Vitamin E has become increasingly more important. Vitamin E is important in human nutrition since it has potent antioxidant activity, thus preventing the damage of cells through the inactivation of free radicals and oxygen species (Diplock, 1994). Due to its antioxidant activity, vitamin supplementation has been found to be effective on pigment and lipid stability in food products such as frozen beef. Lanari et al. (1994) demonstrated through kinetic analysis that vitamin E supplementation stabilized the oxymyoglobin complex by enhancing the deoxymyoglobin oxygenation and by decreasing the oxymyoglobin autoxidation rate. Vitamin E enhanced the pigment and lipid stability of frozen beef, stored in the dark or under constant illumination. Lanari et al. (1993) also indicated the significance of dietary supplementation of Holstein steers with vitamin E in delaying surface discoloration of meat after repeated freeze-thaw cycles and during dark storage or illuminated display. Similarly, Houben et al., 2000 studied the benefits of vitamin E supplementation to the diet of beef bulls on the color stability and lipid oxidation of minced beef. The authors corroborated previous studies on the proposed mechanisms of the vitamin E color stabilizing activity, which calls for indirectly delaying the oxidation of oxymyoglobin via direct inhibition of lipid oxidation. Others have researched introduction of vitamin E through various types of emulsions either as part of lipid-containing food components such as cream cheese or mayonnaise (Schneider et al., 2012) or as nanoemulsions that have potential for food systems as well as pharmaceutical delivery products (Saberi et al., 2013).

#### 3.3.2.4  Vitamin K

Vitamin K is a generic term referring to a group of lipid soluble bicyclic naphthoquinone derivatives with a common 2-methyl-1,4-naphthoquinone ring structure (menadione) and a hydrophobic polyisoprenoid side chain attached at the 3-carbon position of the nucleus; this side chain may vary in length and degree of saturation, with up to 15 units reported (Figure 3.24). It functions as a cofactor (in its reduced form, dehydro-vitamin K [KH2]) for the enzyme, γ-carboxyglutamyl carboxylase, which is required to catalyze the conversion of specific peptide-bound glutamate residues to γ-carboxy-glutamates (Gla). This γ-carboxylation is accompanied by oxidation of (KH2) to vitamin-K-epoxide which is then recycled back to vitamin K, completing this cycle. Although these vitamin K-dependent Gla-proteins have been traditionally known mostly for their importance as blood coagulation factors (e.g., prothrombin) in warm blooded animals, over the past couple of decades, advances in research have found these Gla-proteins to be more diverse in structure and function and present in many different cell and tissue types. Additional areas of human physiology include bone and cartilage health (Bügel, 2003; Torbergsen et al., 2015; Shea et al., 2015), arterial calcification inhibition (Vossen et al., 2015; Dourado Villa et al., 2017), brain and energy metabolism, immune response, and general cellular growth regulation (Vermeer, 2012). Synergistic reactions have also been shown between vitamins K and D in terms of calcium absorption and regulation of calcification (Iwamoto et al., 2005; Shea and Booth, 2007). Several reviews on some of the more recent studies on vitamin K and its biologically significant derivatives further clarify their roles in nutrition, and ongoing research has been published (Shearer et al., 2012; Shearer and Newman, 2014; Shea and Booth, 2016; Schwalfenberg, 2017). Furthering knowledge on elucidating reaction mechanisms relevant to metabolic pathways and identification of the most health relevant K-derivatives has led to reestablishing the importance of vitamin K in the diet and fortification in foods. As evidence of new relevant vitamin K derivatives emerges, further clarification of chemical structures and reaction pathways will evolve; this is important to the food scientist in order to understand what vitamers are the most critical to study for predicting their stability during processing and storage.

Figure 3.24   Some chemical structures of vitamin K and derivatives. (adapted from Berruti, 1985; Daines et al., 2003; Shearer and Newman, 2015; Fujii and Kagechika, 2017).

The most common naturally occurring forms of vitamin K are known as K1 (phylloquinone) and K2 (a group of vitamers known as menaquinones). Phylloquinone, also referred to as 3-phytyl-menadione, has a chemical structure established as 2-methyl-3-phytyl-1, 4-naphthoquinone. K1, having a similar phytyl side chain as chlorophyll, is found in the chloroplasts of plants forming part of the electron transport system in photosynthesis. Most notably, this side chain contains only one double bond at the 2–3 side chain position as compared to those of the K2 group (Figure 3.24). K1 is considered the primary source of vitamin K in the diet; common natural K1 sources are ubiquitously found in green and leafy vegetables and various plant oils, such as rapeseed, soybean, and olive oils. Although relatively heat stable, vitamin K1 is sensitive to light and oxidation. Ferland and Sandowski (1992) reported large ranges of up to 140–200 μg K1/100 g in rapeseed and soybean oils, 55 μg K1/100 g in olive oil, but as low as 3 μg K1/100 g in corn or peanut oils. They reported relatively good stability during cold processing, some losses with heat, but significant losses when exposed to sun or fluorescent light. Davidson et al. (1996), on the other hand, reported partial transformation of vitamin K1 to 2′, 3′-dihydrovitamin K1 (dK1) during hydrogenation. Peterson et al. (2002) carried out an assessment of vitamin K1 and dK1 in various types of commercial vegetable oils, fats, spreads, and salad dressings and found significant amounts of both in many of the plant-based oils as analyzed by HPLC, following a hexane extraction and purification step; they pointed out that the dK1 levels were dependent upon the degree of hydrogenation of the specific fats. Centi et al. (2015) indicated lower in vivo absorption rates for dK1 compared with vitamin K1. With the new required labeling after 2006 and converting away from trans-fat, this has become less of an issue, particularly for baked products containing less hydrogenated oils.

Vitamin K2 includes a group of derivatives with a similar central structure to K1, but with varying side chain length and multiple double bonds. K2 terminology is abbreviated as MK-n, where M is the menadione central bicyclic ring structure, K stands for vitamin K, and n represents the number of isoprenoid groups attached as the side chain (Figure 3.24). In earlier citations, one of the first vitamin K2 structures was referred to as farnoquinone, or MK-6. Since its discovery, several additional forms with higher nutritional activity have been found, including MK-4, MK-7, MK-8, and MK-9, with increasing numbers indicating lengthening of the side chain and increased hydrophobicity. Vitamin K2 can be found predominantly as MK-4 in egg yolks, avian fowl livers (e.g., geese, chicken), meats, and butter; as mostly MK-8 and MK-9 in fermented dairy products (e.g., cheeses); MK-7 in fermented soybeans (natto); and higher menaquinones (i.e., MK-10) can be synthesized by obligate and facultative anaerobic bacteria, including those from the microflora in the gut (Shearer et al., 2012; Schurgers and Vermeer, 2000). Vitamin K2’s general structure is 2-methyl-3-all-trans-polyprenyl-1, 4-naphtho-quinones with the MK-4 as one of the more biologically available derivatives; its chemical structure has been defined as either 2-methyl-3-(3,7,11,15-tetramethyl-2,6,10,14-hexadecatetraenyl)-1,4-naphthalene-dione; menatetrenone; or vitamin K2(20). Schurgers and Vermeer (2000) have reported levels of vitamins K1 and K2 with a relative distribution of the various MK-n’s in several food products; analyses were conducted with a solvent extraction based on food type, followed by reverse phase high pressure liquid chromatography (HPLC).

Still very limited information is available for the kinetics of degradation and stability of vitamin K in foods. Indyk (1988), working with vitamin K1 dissolved in hexadecane, observed stability of this vitamin to mild heat treatment, even in the presence of oxygen. Vitamin K was found to be susceptible to degradation and isomerization in the presence of light even at low intensity. The loss of the isomers, cis and trans, was described by zero-order kinetics, indicating the possibility of an autooxidation mechanism. Losses of either isomer did not result in the formation of the other. Several competing decomposition pathways have been proposed for the photolysis of vita min K depending upon conditions. A study on phylloquinone content in processed sea buckthorn berry juice and concentrate showed losses of 36–54% with additional losses during storage of 18%–32%; however, storage of freshly harvested berries resulted in phylloquinone increasing to levels ranging from 21% to 186% (Gutzeit et al., 2007a).

A third group of vitamin K derivatives includes a series of synthetic variations which have been developed over time, each in theory progressively improved over its predecessor. The basis for the activity of vitamin K revolves around the naphthoquinone nucleus (Figure 3.24). Menadione (2-methyl-1, 4-naphthoquinone, or often referred to as vitamin K3) was the first commercially available product with reportedly three times the biological activity of K1. Over time it was shown to have some serious side effects and was no longer in use for a number of years; however, more recent studies have been using this derivative in extreme cases. Menadione formed the basis, however, for later synthetic versions including menadione sodium bisulfite (MSB) and a later version, menadione sodium bisulfite complex (MSBC). It is reportedly water-soluble, stable to light and air, but not heat. Minimal information is available on kinetic stability. Other K-analogues include synthetic variations such as menadiol diacetate (acetomenaphthone, K4), menadiol sodium diphosphate, and menadione dimethylpyrimidinol bisulfite (Figure 3.24).

#### 3.3.3  Pigments

Pigments are complex compounds that absorb and reflect light in the wavelength of the visible region. In the original writing of this section, it was more or less assumed that the color pigments under discussion were associated with those naturally present in a given food product (i.e., chlorophyll in green peas, β-carotene in carrots, hemoglobin in meats, etc.). Other colors added to processed foods, usually artificial, were not discussed. Since then, food trends have grown exponentially in the direction of utilizing natural color additives in processed foods; thus, stability of these natural colors has become even more critical in terms of both the reactive substrates to which they are added as well as the stability of the raw natural pigments as supplied to food factories. With increasing numbers, types, sources, new methods of pigment extraction and processing, it becomes continually more crucial and challenging to monitor stability in order to best develop models to help the food industry predict retention and shelf life of food products and raw materials.

Color remains to be one of the first single most notable characteristics of food that often predetermines a consumer’s judgement on food quality (Spence, 2016), so, obviously, color stability is still an extremely important factor in foods. Over a decade ago, Griffiths (2005) reviewed acceptable synthetic and natural colors used in the US food industry and indicated a trend for an increased use of colors particularly in novelty snacks, desserts, and beverages, further emphasizing the importance of color stability in foods; this trend continues today, but mostly in the direction of natural colors. With increased awareness over health and safety concerns over synthetic food colorants and their potential toxicity, greater emphasis is being placed on more natural alternatives to meet consumer expectations (Amchova et al., 2015; Martins et al., 2016). Wrolstad and Culver (2012) more recently reviewed alternatives to artificial colors, and as new natural colorants have become more available, the numbers of acceptable color ingredients has been in constant flux; Table 3.4 presents natural colors approved for use in food in the US as of November 2017 (US FDA, 2017). Based on the US Code of Federal Regulations (CFR), natural colorant replacements must meet targets for hue, stability in specific application, and cost. These are rigid criteria to fall under as natural colors hardly ever meet the intensity of artificial dyes requiring significantly higher concentrations and creating potential off-flavors, increased cost, and decreased stability. Colorants such as carotenoids including β-carotene, annatto, paprika, and particularly lycopene are known to exhibit antioxidant activity. Flavonoids, including the anthocyanin group, have also been attributed to having health benefits such as antioxidant properties, anti-inflammatory effects, lowered blood pressure, and anti-tumor properties. Another group of colorants, known as the curcuminoids found in turmeric, are also found to have similar health-related properties as well as antithrombic effects and antimicrobial activity (Taylor, 1996). More recently, a new group of natural blue colors, phycocyanins, have been approved for use as colorants in certain types of foods and have potential health benefits as well as aesthetic appeal; they have been reported to have nutritional and antioxidant benefits (Eriksen, 2008). Overall changes in color may be due to a number of reactions such as pigment degradation or polymerization, interaction with other components in the food product, non-enzymatic browning, oxidation of tannins, and other reactions. The following section discusses some of the major sources of color pigments that are naturally present or added to processed foods and their relative stability to processing and/or storage conditions.

#### 3.3.3.1  Phycocyanins (Phycocyanobilins)

Recent market trends moving towards incorporating more natural ingredients in processed foods has spurred a great deal of effort in replacing artificial colors with natural counterparts. Since natural blue color is rare and one of the more challenging hues to provide both appealing color and stability in different food types, research emphasis has been on a replacement for artificial FD&amp;C Blue #1 (“brilliant blue”). The main focus has been on phycocyanins, blue water-soluble pigment-protein complexes belonging to the phycobiliprotein (PBP) family. Two additional important members of this group include allophycocyanins (turquoise/aqua) and phycoerythrins (red/pink), (Glazer, 1989; Singh et al., 2015), although others have been cited (MacColl, 1998). They may be found in cyanobacteria (often referred to as blue-green algae, e.g., Arthrospira platensis [formerly known as Spirulina platensis] and A. maxima) and certain eukaryote algae, such as Rhodophytes, Cryptomonades, and Glaucophytes, with colors ranging over a broad spectrum from red, orange, and yellow to blue-green (Glazer, 1989). Pigmentation of these organisms is a composite of contributions, not only from the predominating phycobiliproteins, but also the presence of chlorophyll and carotenoids, making it critical for their proper separation for consistent quality control in terms of color and stability. Phycobiliproteins, in themselves, contribute distinctive coloration depending upon the nature of their protein environment an d their covalently attached tetrapyrrole prosthetic groups (Figure 3.25); the three main PBP groups as mentioned above are categorized based on their energy levels: the highest level being the phycoerythrins (or phycoerthrocyanin); intermediate level, the phycocyanins; and lowest level, the allophycocyanins (MacColl, 1998). Their energy levels are characterized by their maximum absorption wavelengths (λmax); for instance, phycocyanin has a characteristic cobalt/gentian blue color with a λmax around 610–620 nm (depending on which specific type it is, C- or R-phycocyanin [prefixes original terminology derived from their algal source, but currently refer more to their λmax ranges and prosthetic group attachments, independent of source]), and also emits fluorescence at about 650 nm. On the other hand, allophycocyanin absorbs and emits at λmax around 650–655 nm, while phycoerythrin has a λmax between 540 and 575 nm (Glazer and Hixson, 1975; Glazer, 1989; MacColl, 1998; Singh et al., 2015). It should be pointed out that bilins (phycobilins) which absorb light energy and transfer this energy to other bilins are called donors, and those that both absorb excitation energy and fluoresce are called acceptors (Glazer, 1989).

Figure 3.25   Structures of important peptide-linked phycobilin chromophores (tetrapyrrole prosthetic groups) that make up the phycobiliproteins from cyanobacteria and red algae. (adapted from Glazer, 1989, 1994a,b and MacColl, 1998).

At the molecular level, the phycobiliproteins are comprised of polypeptides with covalently bound open-chain linear tetrapyrroles (referred to as phycobilins or bilins) that act as light energy absorbing (“light-harvesting”) chromophores, similarly to chlorophyll, but in different spectral regions (Glazer, 1989, 1994a,b). The basic structure of each of the phycobiliproteins consists of two dissimilar α- and β-polypeptide subunits, each of which have a specific amino acid sequencing (Apt et al., 1995) and contain one or more of these phycobilins covalently attached via thioether linkages to specific cysteinyl residues; in the case of certain phycoerythrins, γ-polypeptide subunits have also been identified (Glazer, 1989; Liu et al., 2005). There are currently four phycobilins identified from cyanobacteria and red algae (Figure 3.25); they include phycocyanobilin, phycoerythrobilin, phycoviolobilin (phycobiliviolin), and phycourobilin (Glazer, 1994a,b; MacColl, 1998). The phycobiliproteins (with attached phycobilins) are assembled in organized cellular structures called phycobilisomes (PBS), and they make up the major mass (~85%) of the total PBS protein complex (total mass reported 6000–8000 kDa, Glazer, 1994a,b). Some typical examples of phycobiliproteins and their phycobilin polypeptide linkages are presented in Table 3.5. Biochemical and structural analyses of phycobiliproteins have shown αβ monomers to be assembled into a disc-like trimeric (αβ)3 or hexameric (αβ)6 configuration along with additional specific “linker” polypeptide chains (generally without phycobilins attached and colorless), which further aid in the organizational formation of the phycobilisomes into two main structural domains of a core substructure (allophycocyanins) and peripheral rods (close-in phycocyanins and further-out phycoerythrins, depending upon the organism and growth conditions) adhering to the stroma side of the thylakoid membrane (Yu and Glazer, 1982; Arteni et al., 2009; Singh et al., 2015). It has been proposed that these highly organized geometrical phycobilisome structures allow transferal of absorbed light energy to chlorophyll-a of photosystem II within the cyanobacteria species, allowing their adaptability to the photosynthetic process to varying light growing conditions, referred to as complementary chromatic adaptation (MacColl, 1998; Ojit et al., 2015). Their molecular weight (MW) varies according to their aggregation state, pH, temperature, protein concentration, ionic strength, and solvent (Mishra et al., 2008), with the MW range for aggregates varying anywhere from 110 to 120 kDa for trimers (e.g., allophycocyanins), ~250 kDa for hexamers (e.g., phycoerythrins) and 20–40 kDa for monomers, depending on the type of phycobiliprotein and species origin (Glazer, 1994a; Glazer and Cohen-Bazire, 1971). Their structural configuration and function have been under constant review over the past decades as new species of cyanobacteria/microalgae are researched and taxonomic nomenclature has adapted (Cohen et al., 1995; MacColl, 1998; Liu et al., 2005; Kupka and Scheer, 2008; Kasai et al., 2009; Singh et al., 2015; Saer and Blankenship, 2017). Pigment color and intensity will depend on species of origin, their growth environment, extraction methods, and purity of separation of phycocyanin from other pigment contributors. These will be important factors for the bioengineer in developing the most efficient extraction process for phycocyanin pigments, predicting stability, and maintaining a constant color range, when final use is as a food colorant.

Commercially produced phycocyanin, known as Spirulina, is most commonly obtained from the blue-green cyanobacterium, Anthrospira platensis, under the class Cyanophyceae. Anthrospira platensis contains three types of phytopigments: 1. chlorophyll-a (green pigment), 2. carotenoid-based pigment (orange-yellow), and 3. phycobiliproteins (blue-red pigments). Despite its recent acceptance as a “natural” blue food colora nt in the U.S. (chewing gum/candy, 2013 and other confectionery, beverages, desserts, etc. 2014, US FDA, 2017), A. platensis has been historically used as a food source for thousands of years. Arthrospira strains have high nutritional value with high levels of γ-linolenic acid, α-tocopherol, β-carotene, protein levels above 60%, rich in vitamin B’s and minerals (Ciferri, 1983; Spolaore et al., 2006; Borowitzka, 2013), as well as pharmaceutical potentials such as antioxidants, anti-inflammatory, anti-carcinogenic, etc. (Eriksen, 2008). Mechanisms of action as an antioxidant for phycocyanin have been reviewed by Romay et al. (2003); studies have shown phycocyanin to be an efficient scavenger of oxygen free radicals. Lisi et al. (2000) and Bhat and Madyastha (2000) have suggested a mechanism involving the phycobilin chromophore in the scavenging activity of the protein. Using kinetic models, they showed that micromolar concentrations of phycocyanins are able to reduce peroxy radicals by half, indicating high antioxidant activity for this compound. Based on work by Chepelev et al., (2006) and MacLean et al. (2008) on general mechanisms of pyrroles, it may be speculated that the highly conjugated double bonds within the tetrapyrrole are susceptible to autoxidation through their active NH-groups, resulting in the H-transferal to peroxy radicals.

### Table 3.5   Maximum Absorption Wavelengths of Phycobilin Chromophores and Examples of Their Contributions with α, β-Subunit Positioning in Phycobiliproteins

 Phycobilinsb (Attached to Cys-Residues of Phycobiliprotein Subunits) Phycobilins Phycobiliprotein (Examples) α-subunits/β-subunits Phycocyanobilin PCB 620–650 C-Phycocyanin α β Allophycocyanin α β Phycoerythrobilin PEB 540–565 C-Phycoerythrin α β Phycoviolibilin (Phycobiliviolin) PVB (PXB) 568 Phycoerythrocyanin α β Phycourobilin PUB 490 Phycocyanin WH8501 α β

a Adapted from Glazer (1994a); from bMacColl (1998).

The wide variety of phycocyanin sources and diverse growing habitats of numerous cyanobacteria and eukaryote algae makes the study and understanding of the extracted pigments’ stability in food products a challenge. Traditionally, the organism of choice for production of phycocyanin has been Arthrospira platensis, grown photoautotrophically in alkaline media in open ponds rich in nutrient salts in tropical and/or subtropical climates. In this case, the high pH and alkalinity help inhibit possible contaminating and competing microorganisms, resulting in potentially higher yields and purity of the final product, barring other cross contamination that may occur in externally uncontrolled environments. Alternative production techniques including photoautotrophic, mixotrophic, heterotrophic, and recombinant methods, as well as various extraction and purification methods and their effect on total yields of pigment have been reviewed (Eriksen, 2008). With increased demands for this pigment/nutrient compound, new extraction methods have been explored as well as new sources of organisms. For instance, some phycocyanins found in the Synechoccocus lividus, strain I may thrive in hot springs at 73°C; whereas, the S. lividus, strain III only tolerates temperatures up to 55°C (MacColl et al., 1974). Another phycocyanin found in Cyanidium caldarium (red alga) grows at temperatures up to 57°C and pH as low as pH 0.05. Although thermophiles may contain slightly different polypeptide amino acid sequences making it stable under higher temperature growing conditions, there may still be different degrees of dissociation and levels of protein denaturation in vitro (Eisele et al., 2000). Once the phycobiliproteins are extracted from the phycobilisomes, the general structural organization is disrupted, and dissociation can occur, so it is difficult to predict exactly the behavior of these pigments based on their growth environment. Other species that may have higher phycocyanin pigment yield potential include Galdieria sulphuraria as discussed by Eriksen (2008) and Sørenson et al. (2013) and Anabaena circinalis (Ojit et al., 2015). Singh et al. (2009) studied optimization of growth medium on phycocyanin production in Phormidium ceylanicum using response surface methodology and found an optimum recovery efficiency of C-phycocyanin from crude extract to be 63.5%. Background knowledge of pigment source, growing conditions, and extraction methods used will be of utmost importance for consistent product quality and understanding of various mechanisms of degradation that may occur.

Furuki et al., 2003 conducted a study on efficiency of phycocyanin extraction from Arthrospira platensis by using ultrasonic radiation to disrupt cells; they reported first-order degradation kinetics for color with respect to the length of time of the irradiation exposure and higher purity of extract with a higher ultrasonic frequency (f u ) = 28 kHz compared to f u  = 20 kHz. Moraes et al. (2011), on the other hand, describe various extraction procedures (chemical [organic vs, inorganic acid treatment]; physical [freeze/thaw, sonication, homogenization], and enzymatic [lysozyme]) for C-phycocyanin from Spirulina (A. platensis); they found a method using combined sonication with glass beads being highly efficient with yields above 43%. A further purification strategy was developed using ion exchange chromatography to achieve analytical grade product (Moraes and Kalil (2009). Purity (P) of an extracted phycocyanin pigment is often evaluated on the basis of a ratio of absorbancies (A) of the phycocyanobilin (in this case, A 620) and aromatic amino acids (A 280) such that level of purity for phycocyanin would be defined as P = A 620/A 280 > 0.7 for food grade, 3.9, reactive grade, or ≫4.0, analytical grade. It should be kept in mind, however, that color intensity of a pigment extract is dependent upon both overall concentration and purity, and does not account for differentiation between phycocyanin and allophycocyanin. Yoshikowa and Belay (2008) developed a 2-wavelength method (620, 650 nm, λmax, respectively) to monitor level and purity of these two phycocyanins.

Thermal degradation kinetic data have been reported on a liquid phycocyanin extract from Arthrospira platensis (Spirulina) at pH 5–6, at temperatures between 50°C and 65°C, using first-order models. The authors found highest stability at pH 6 between 50°C and 55°C, followed by pH 5, 57°C–65°C, but increasingly unstable at pH 7 with increasing tempera ture (Antelo et al., 2008, Table 3.6). Sarada et al. (1999) reported on phycocyanin stability as affected by different types of extraction and drying procedures for A. platensis, finding about 50% loss with either cross-flow, oven, or spray drying. Generally, they found maximum stability during storage at pH 5.0–7.5 at 9°C and reduced stability at temperatures >40°C. Chaiklahan et al. (2012) found similar results, with A. platensis having a maximum stability at pH 5.5–6.0 with decreasing stability at temperatures >47°C; however, the addition of 20%–40% glucose or sucrose or 2.5% sodium chloride significantly increased retention at pH 7.0 and 60°C; similar results were found by Martelli et al. (2014) with addition of sugar or honey during processing 25°C–80°C. Mishra et al. (2008) found citric acid addition at 4 mg/ml to help stabilize phycocyanins from A. platensis up to 45 days at 35 ± 5°C with negligible loss as compared with a control phycocyanins without preservative stored at 0°C. Similarly, Colla et al. (2017) found increased degradation of non-extracted solid mass A. platensis in powdered form with increasing temperatures 25°C–50°C, particularly when exposed to light, with more than 50% loss of color pigment within 30 days storage at elevated temperatures; they emphasized the importance of low temperature storage and use of proper light-restricting packaging.

Kannaujiya and Sinha (2016) investigated thermostability of phycocyanin and phycoerythrin extracted from cyanobacterium, Nostoc sp. Strain HKAR-2 as affected by the presence of various preservatives (including benzoic, citric, ascorbic acids, sucrose, and calcium chloride) during storage over a temperature range 4°C–40°C; highest stabilities were found using benzoic, citric acids and sucrose, respectively at 5 mM concentration levels.

#### 3.3.2.1.1  Other Natural Blue Pigments

Most work on replacement of artificial blue color up to this point has focused on phycocyanins obtained from A. platensis (Spirulina), and this is currently the only accepted natural blue colorant allowed in foods in the US, although still not yet in Canada. However, it bears mentioning some work on other naturally derived sources of blue colorant that have been researched. Newsome et al. (2014) have extensively reviewed blue colorants from a variety of biological sources and classified them into seven structural classes and have evaluated them according to their potential use as food colorants, along with the physical and regulatory challenges that would be required to finalize usage as a new colorant in food products. One example in particular that has received attention is an iridoid-derived color pigment referred to as Gardenia blue from the fruit of the Gardenia jasminoides Ellis, and is currently used as a natural colorant in food and beverages in parts of Asia, although not currently accepted in the US, Canada, or Europe. The iridoids are a group of monoterpenoids with a cyclopentanodihydropan ring structure and exist in several different forms of which several hundred have been identified, and the iridoid geniposide from G. jasminoides has been the most studied (Pintea, 2008). Upon its initial extraction from the fruit, it is in the form of the colorless iridoid geniposide and gardenoside. Following extraction, geniposide is hydrolyzed with β-glucosidase, yielding genipen and glucose, which is transformed into the blue pigment through reactions with amino acids, glycine, lysine, or phenylalanine (Paik et al., 2001). Sadano (2011) patented a process for preparation of this colorant in Japan and filed an application in the US in 2013. Escheverry et al. (2011) also developed a patent for a similar blue pigment derived from Genipa Americana fruit (huito juice). Jespersen et al. (2005) also discusses multiple sources of natural blue pigments, including phycocyanins, gardenia blue, and indigo and discusses their stability to heat and light; they found phycocyanins have the greatest versatility in terms of the bright blue appearance in different applications, even though they had relatively low stability. It should be mentioned here that certain anthocyanins and metallocomplexes of such, under strict pH conditions may also act as a blue colorant; however, as pointed out in a later section, these are relatively unstable. A further review of natural sources of blue pigments has been published (Buchweitz, 2016).

#### 3.3.3.2  Chlorophylls

Chlorophylls refer collectively to a group of pigments providing color to green plant tissues. They range in color from a bright green to a dull olive brown and are often used as indicators of product quality of processed green vegetables, as measured by the intensity of their green color. The predominant green colored pigments include chlorophylls a and b at a reported ratio of about 3:1 as naturally occurring in plants. Both are derivatives of a tetrapyrrole phorbin (porphyrin ring with C9–C10 isocyclic ring) chelated with a centrally located magnesium atom and a C7 20-carbon phytol chain (Figure 3.26). Their main differences are their substituent groups at the C3 position and perceived color; chlorophyll a has a methyl group and is blue-green, and chlorophyll b has a formyl group with a yellow–green color (Belitz and Grosch, 1987). Isomeric forms may also exist as chlorophylls a′ and b′ or pheophytins a′ and b′, due to epimerization at the C10 center located on the isocyclic ring. Other less common forms that exist include chlorophyll c and chlorophyll d, isolated from marine algae. Chlorophyllides a and b are the respective acid derivatives of chlorophylls a and b resulting from enzymatic (e.g., naturally occurring chlorophyllase) or chemical hydrolysis of the C7 propionate ester and cleavage of the phytol chain; they too possess a green color. The main transformation or degradation products of chlorophylls a and b are respectively pheophytin a and b, which are formed through the replacement of the central magnesium of the porphyrin ring with hydrogen atoms. Also, pheophorbides a and b may be formed through the removal of the phytol chain from the pheophytins or through magnesium loss from the chlorophyllides. These degradation products all exhibit a dull-olive brown color.

Figure 3.26   Selected mechanisms of chlorophyll degradation. (1) Aronoff, 1966; (2) Schaber et al., 1984.; (3) Seely, 1966.; (4) Clydesdale et al., 1972.; (5) Jones et al., 1963.; (6) Schwartz and von Elbe, 1983.; (7) Jones et al., 1962.; (8) Minguez-Mosquera et al., 1989.; (9) Wagenknecht et al., 1952; (10) Canjura et al., 1999.

It has long been reported by many investigators that chlorophylls are susceptible to thermal treatment, being transformed into predominantly the dull green pheophytins a and b (Schwartz and von Elbe, 1983; LaBorde and von Elbe, 1994; Steet and Tong, 1996a,b; Heaton et al., 1996a,b; and Gunawan and Barringer, 2000). These compounds may also further degrade to pyropheophytin or other products through the destruction of the porphyrin ring. Schwartz and von Elbe (1983) working with spinach indicated that pyropheophytin was a predominant product of the thermal breakdown of chlorophylls and that its formation followed first-order kinetics. Heaton et al. (1996a) developed a general mechanistic model for rates of chlorophyll degradation to pheophytin, chlorophyllide and pheophorbide in green plant tissue, including models such as coleslaw, pickles, and olives. Their claim was that this model could discriminate between pathways of degradation and enable quantitative definition on which pathways were operational or predominate under different conditions. This would also allow better comparison of rates of chlorophyll degradation between various commodities. For instance, Heaton et al (1996b) found no significant change over time with chlorophyllide in coleslaw, but with pickles and olives, the formation of chlorophyllide with further degradation to pheophorbide was a predominant reaction pathway. Some of this variation may be due to the relative activity levels of chlorophyllase present, as well as pH and other environmental factors. This type of approach is important in understanding the mechanisms for discoloration and should aid the food processor in determining optimum shelf life.

Other factors influencing the stability of chlorophylls include light, oxygen, water activity, irradiation, pH, presence of metal traces, and enzymatic activity. Lajolo and Lanfer Marquez (1982) indicated higher rates of degradation with water activity in a spinach model system at 38.6°C. Similarly, the authors observed an increase in the rates of chlorophyll degradation upon a decrease in pH for the range 5.9–6.8. These results confirm the well-known and most common mechanism for chlorophyll degradation through its acid-catalyzed transformation into pheophytin (Figure 3.26). This reaction has been reported by several authors to follow first-order kinetics. The mechanisms by which chlorophylls degrade, of course, depend upon the process under consideration. For instance, Minguez-Mosquera et al. (1989) found that chlorophyllides were intermediary products in the fermentation of olives, and that the ratio of the various degradation products, including chlorophyllides a, b; pheophytins a, b; and pheophorbides a, b, were very dependent upon pH of the system (Figure 3.26).

Gunawan and Barringer (2000) studied the effect of acid (pH 3–8) and microbial growth on stability of green color of blanched broccoli under low temperature storage (7°C). Through HPLC determination, they found only conversion of chlorophylls to pheophytins. This conversion was greater at lower pH and fit a first-order kinetic model. Some isomers were also isolated, including chlorophylls a′ and b′, present in the blanched broccoli, and pheophytins a′ and b′ after acidification. The authors also found that chlorophyll degradation was dependent on the type of acid used. Acids containing a benzene ring resulted in more rapid color change than acids with a simple carbon chain; perhaps due to the hydrophobicity of the aromatic acids, they were able to diffuse more easily through the lipid membrane surrounding the chloroplasts. They also found that microbial growth increased loss of color and proposed two possible mechanisms by which this may occur. The first was simply that production of acid metabolite products would lower the pH and, thus, decrease chlorophyll stability. The second mechanism was due to the breakdown of the cellular structure of the broccoli, as evidenced by surface holes observed by scanning electron microscopy. This could result in exposing the chloroplasts more directly to the acidic medium.

Ryan-Stoneham and Tong (2000) developed a mathematical model to predict chlorophyll concentration as a function of time, temperature, and pH using pea puree as a model. Since pH naturally lowers during heating due to acid formation, the authors used a specially designed reactor to automatically adjust the pH of the medium to keep it constant during heating. They found that degradation of both chlorophylls a and b followed first-order kinetics. Reaction rate constants and energies of activation are presented in Table 3.6, as calculated by the conventional Arrhenius equation. The authors reported through the use of their modified model, factoring in pH as a variable, that the energies of activation were independent of pH.

### Table 3.6   Kinetic Parameters for Pigment Degradation/Formation During Thermal Processing and/or Storage

Commodity

Process/Conditions

r2

kT value (min−1)

Ea (kcal/mol)

Reaction Order

r2

Temp. Range (°C)

t1/2 (min)

Reference

Phycocyanins

Cyanobacterium – Arthrospira platensis (formerly known as Spirulina platensis)*

Antelo et al. (2008)

Phycocyanin (PC)

0.93

k65 = 17.40 × 10−2

3.98

0.94

k62 = 15.01 × 10−2

4.62

Cultures:

0.97

k60 = 6.60 × 10−2

10.50

pH 5.0

Grown in 450 L open outdoor bioreactor

0.94

k57 = 2.40 × 10−2

87.4

1

0.94

50–65

28.88

0.97

k55 = 1.20 × 10−2

57.77

Water supplemented with 20% Zarrouk medium

0.93

k53 = 0.180 × 10−2

385.08

0.95

k50 = 0.060 × 10−2

1155.25

0.95

k62 = 46.73 × 10−2

1.48

Filtered, pressed,

0.92

k60 = 22.85 × 10−2

3.03

pH 6.0

extruded,

0.96

k57 = 3.60 × 10−2

135.6

1.0

0.96

50–62

19.25

dried 50°C, 6 hr,

0.95

k55 = 0.300 × 10−2

231.05

frozen −18°C,

0.94

k53 = 0.120 × 10−2

577.62

ground, sieved

0.96

k50 = 0.048 × 10−2

144405

pH 7.0

0.95

k60 = 10.80 × 10−2

6.42

Extraction,

0.93

k57 = 8.40 × 10−2

8.25

pH 7.0

Centrifugation,

0.96

k55 = 0.600 × 10−2

111.2

1.0

0.88

50–60

115.52

Vacuum filtered

0.97

k53 = 0.180 × 10−2

385.08

0.97

k50 = 0.120 × 10−2

577.62

pH 5.0

Antelo et al. (2008)

10% sorbitol

0.94

k62 = 2.34 × 10−2

28.87

20% sorbitol

0.88

k62 = 1.38 × 10−2

57.75

30% sorbitol

0.95

k62 = 1.74 × 10−2

1

62

38.50

40% sorbitol

0.93

k62 = 1.32 × 10−2

57.75

50% sorbitol

0.94

k62 = 3.60 × 10−2

192.53

pH 6.0

Antelo et al. (2008)

10% sorbitol

Analysis:

0.96

k62 = 4.62 × 10−2

14.43

20% sorbitol

Spectrophotometric

0.95

k62 = 3.00 × 10−2

23.10

30% sorbitol

PC (mg/cm3) =

0.98

k62 = 7.20 × 10−2

1

62

115.52

40% sorbitol

(A615–0.474 × A652)/5.35

0.98

k62 = 6.60 × 10−2

115.52

50% sorbitol

0.95

k62 = 3.60 × 10−2

192.53

pH 7.0

10% sorbitol

0.92

k62 = 4.02 × 10−2

16.50

20% sorbitol

0.90

k62 = 2.40 × 10−2

28.88

30% sorbitol

0.92

k62 = 1.80 × 10−2

1

62

38.50

40% sorbitol

0.94

k62 = 1.26 × 10−2

57.77

50% sorbitol

0.92

k62 = 3.60 × 10−2

192.53

*Kasai et al. (2009)

Phycocyanins

Cyanobacterium – Arthrospira platensis (Spirulina platensis)

Chaiklahan et al. (2012)

Phycocyanin and Allophycocyanin

Cultured in Zarrouk’s medium in 100 L outdoor open raceway ponds

k74 = 10.7 × 10−2

6.5

k69 = 9.28 × 10−2

7.5

k64 = 7.35 × 10−2

9.4

pH 5.0

k59 = 3.20 × 10−2

23.9

1

0.983

47–74

21.6

Extraction/w/100 mM phosphate buffer (pH 7.0)

k55 = 2.02 × 10−2

34.4

k51 = 1.12 × 10−2

62.1

Centrifuged/filtered/freeze-dried

k47 = 0.60 × 10−2

116.5

k74 = 7.07 × 10−2

9.7

30 mg powder/30 ml citrate buffer (pH 5, 6, 7)

k69 = 4.81 × 10−2

14.5

k64 = 2.47 × 10−2

28.1

pH 6.0

Samples incubated 240 min

k59 = 1.50 × 10−2

28.8

1

0.992

47–74

46.4

Analysis: UV-VIS

A280; A620; A652

k55 = 0.75 × 10−2

92.7

k51 = 0.41 × 10−2

167.0

k47 = 0.22 × 10−2

309.4

mg/ml

k74 = 13.6 × 10−2

5.3

Chaiklahan et al. (2012)

CPC = [A620 − 0.474 (A652)]/5.34

k69 = 11.56 × 10−2

6.0

APC = [A652 − 0.208 (A620)]/5.09

k64 = 7.76 × 10−2

8.9

pH 7.0

k59 = 3.09 × 10−2

29.9

1

0.962

47–74

22.8

k55 = 1.46 × 10−2

47.5

k51 = 0.64 × 10−2

108.3

k47 = 0.54 × 10−2

128.6

Phycocyanin and Allophycocyanin

Chaiklahan et al. (2012)

Effect of preservative:

Glucose

0

0.99

k60 = 3.62 × 10−2

19.1

30 mg powder/30 ml citrate buffer (pH 7)

2.5

0.95

k60 = 3.62 × 10−2

19.1

5

0.98

k60 = 3.65 × 10−2

19.0

10

0.97

k60 = 3.27 × 10−2

1

60

21.2

Preservative added at designated % in 30 ml soln.

20

0.93

k60 = 2.06 × 10−2

33.6

40

0.98

k60 = 1.57 × 10−2

44.1

Sucrose

2.5

0.99

k60 = 3.52 × 10−2

19.7

5

0.81

k60 = 3.11 × 10−2

22.3

10

0.93

k60 = 2.96 × 10−2

1

60

23.4

20

0.82

k60 = 2.31 × 10−2

30.0

40

0.96

k60 = 1.73 × 10−2

40.1

NaCl

2.5

0.94

k60 = 1.02 × 10−2

68.0

5

0.93

k60 = 0.79 × 10−2

87.7

10

0.95

k60 = 0.88 × 10−2

1

60

78.8

20

0.95

k60 = 1.01 × 10−2

68.6

Phycocyanins

Spirulina platensis

Biomass protected from light/up to 63 days/25, 40, 50°C

0.827

k50 = 3.94 × 10−5

1.76 × 104

Colla et al. (2017)

0.910

k40 = 2.19 × 10−5

10.1

1

0.996

25–50

3.16 × 104

0.830

k25 = 1.03 × 10−5

6.70 × 104

Petri dishes/foil

Fluorescent light for 90 days/25°C

0.980

k25 = 3.38 × 10−5

1

25

2.05 × 104

Gelatin capsules

0.964

k25 = 1.65 × 10−5

1

25

4.21 × 104

Amber glass

0.977

k25 = 2.63 × 10−5

1

25

2.64 × 104

Petri dishes

UV light for 60 days/25°C

0.971

k25 = 3.51 × 10−5

1

25

1.98 × 104

Gelatin capsules

0.968

k25 = 2.86 × 10−5

1

25

2.43 × 104

Ambe r glass

0.968

k25 = 2.28 × 10−5

1

25

3.04 × 104

Phycocyanins

Cyanobacterium (Nostoc sp. Strain HKAR-2)

Kannaujiya and Sinha (2016)

Phycocyanin (PC)

Control

0.986

k40 = 60.2 × 10−6

1.15 × 104

Cultures grown in BG-11 medium/no N2/pH 7.0/20 + 2°C/daylight fluorescent tubes (94 mol photon m−2s−1/14/10 light/dark cycle)

0.987

k25 = 26.7 × 10−6

12.73

1

0.992

4–40

2.59 × 104

0.964

k4 = 4.34 × 10−6

15.98 × 104

CaCl2

0.885

k40 = 53.5 × 10−6

1.30 × 104

0.980

k25 = 22.9 × 10−6

12.55

1

0.995

4–40

3.02 × 104

0.993

k4 = 3.98 × 10−6

17.42 × 104

Ascorbic acid

0.989

k40 = 43.8 × 10−6

1.58 × 104

0.963

k25 = 19.3 × 10−6

12.64

1

0.993

4–40

3.60 × 104

Extraction,

0.936

k4 = 3.21 × 10−6

21.60 × 104

Separation of PC/PE,

Sucrose

0.987

k40 = 18.5 × 10−6

3.74 × 104

Freeze-dry,

0.932

k25 = 6.88 × 10−6

12.39

1

0.999

4–40

10.08 × 104

Dissolved pH 7 K-buffer (0.1 mg/ml)

0.996

k4 = 1.39 × 10−6

49.82 × 104

Citric acid

0.964

k40 = 9.82 × 10−6

7.06 × 104

Preservative Conc: 5.0 mM

0.965

k25 = 6.25 × 10−6

10.11

1

0.966

4–40

11.09 × 104

Storage: 30 days

0.936

k4 = 1.25 × 10−6

55.44 × 104

UV-VIS:

Benzoic acid

0.993

k40 = 5.60 × 10−6

12.38 × 104

Final purity ratio: A615/A280 = 3.19

0.980

k25 = 4.81 × 10−6

9.97

1

0.897

4–40

14.40 × 104

0.997

k4 = 0.764 × 10−6

90.72 × 104

Phycocyanins

Cyanobacterium (Nostoc sp. Strain HKAR-2)

Kannaujiya and Sinha (2016)

Phycoerythrin (PE)

Control

0.903

k40 = 37.03 × 10−6

1.87 × 104

Cultures grown in BG-11 medium/no N2/pH 7.0/20+2°C/daylight fluorescent tubes (94 mol photon m−2s−1/14/10 light/dark cycle)

0.862

k25 = 20.93 × 10−6

9.87

1

0.986

4–40

3.31 × 104

0.942

k4 = 4.86 × 10−6

14.26 × 104

CaCl2

0.832

k40 = 34.38 × 10−6

2.02 × 104

0.993

k25 = 19.28 × 10−6

11.95

1

0.973

4–40

3.60 × 104

0.990

k4 = 2.99 × 10−6

23.18 × 104

Ascorbic acid

0.893

k40 = 28.31 × 10−6

2.45 × 104

Extraction,

0.971

k25 = 17.83 × 10−6

11.94

1

0.955

4–40

3.89 × 104

Separation of PC/PE,

0.842

k4 = 2.507 × 10−6

27.65 × 104

Freeze-dry,

Sucrose

0.907

k40 = 22.92 × 10−6

3.02 × 104

Dissolved pH 7 K-buffer (0.1 mg/ml)

0.919

k25 = 11.19 × 10−6

13.48

1

0.980

4–40

6.19 × 104

0.953

k4 = 1.45 × 10−6

47.95 × 104

Citric acid

0.923

k40 = 18.51 × 10−6

3.74 × 104

Preservative Conc: 5.0 mM

0.996

k25 = 9.08 × 10−6

15.18

1

0.970

4–40

7.63 × 104

Storage: 30 days

0.923

k4 = 0.834 × 10−6

83.09 × 104

UV-VIS:

Benzoic acid

0.995

k40 = 8.44 × 10−6

8.21 × 104

Final Purity Ratio: A563/A280 = 7.3

0.942

k25 = 6.25 × 10−6

16.94

1

0.905

4–40

11.09 × 104

0.964

k4 = 0.285 × 10−6

243.36 × 104

Chlorophyll

Asparagus

Heated in distilled water

Lau et al. (2000)

Fresh/whole bud segment

(5–120 min)

0.95

k98 = 0.013

53

Color measure: Lab-values/hue angle (h)

0.91

k90 = 0.0087

12.9

1

70–98

80

0.98

k80 = 0.005

139

0.96

k70 = 0.0029

239

Fresh/whole butt segment

k98 = 0.0167

42

k90 = 0.0069

13.2

1

70–98

100

k80 = 0.0054

128

k70 = 0.0032

217

Broccoli juice

Fresh broccoli liquified

Weemaes et al. (1999b)

Chlorophyll a

Heat in 800 ml sealed vials

k120 = 0.1224

5.7

k110 = 0.0611

11.3

(0–180 min)

k100 = 0.0284

17.0

1

80–120

24.4

HPLC

k90 = 0.0187

37.1

k80 = 0.0101

68.6

Chlorophyll b

k120 = 0.0564

12.3

k110 = 0.027

25.7

k100 = 0.0128

16.0

1

80–120

54.2

k90 = 0.0083

83.5

k80 = 0.0055

126.0

Total chlorophyll

k120 = 0.0943

7.4

Weemaes et al. (1999b)

k110 = 0.0489

14.2

k100 = 0.0229

16.5

1

80–120

30.3

k90 = 0.0149

46.5

k80 = 0.0085

81.5

Brussel sprouts

Total chlorophyll

Dietrich and Neumann (1965)

Whole or halves

Water blanched, wire mesh immersion

0.999

k100 = 0.5639

12.29

0.996

k93.3 = 0.04603

12.9

1

0.949

87.8–100

15.06

0.980

k87.8 = 0.03113

22.27

Steam blanched

0.998

k115.6 = 0.12

5.78

0.991

k100 = 0.0689

12.3

1

0.987

87.8–115.6

10.06

0.993

k93.3 = 0.05027

13.79

0.996

k87.8 = 0.03476

19.94

Green beans

Total chlorophyll

Dietrich et al. (1959)

Whole

Water blanched, wire mesh immersion

0.998

k100 = 0.04872

14.23

(cross-cut)

0.999

k93.3 = 0.04293

5.2

1

0.999

87.8–100

16.15

0.994

k87.8 = 0.03847

18.02

Whole

Steam blanched

0.996

k100 = 0.07599

9.12

Spectrophotometric

0.996

k93.3 = 0.05322

9.2

1

87.8–100

13.02

A534/A556

0.956

k87.8 = 0.005027

138.00

Green beans (cross-cut)

Water blanched

k100 = 0.0931

1

100

Walker (1964)

Olives (pickled)

Heated water bath

Sánchez et al. (1991)

(0–60 min)

Surface color:

S-value (560, 590, 635 μ)

Color via

0.974

k90 = 0.004488

157.00

Spec-20

0.988

k80 = 0.003707

7.2

1

0.970

70–90

187.00

0.942

k70 = 0.0025282

274.00

L-value

Color via

0.914

k90 = 0.0031997

217.00

Hunter

0.972

k80 = 0.001757

10.5

1

0.937

70–90

394.00

0.946

k70 = 0.001367

506.00

B-value

Color via

0.963

k90 = 0.0034653

200.00

Hunter

0.964

k80 = 0.003329

5.1

1

0.836

70–90

208.00

0.921

k70 = 0.002309

300.00

Chlorophyll

Peas, whole

Total chlorophyll

Gold and Weckel (1959)

Blanched

Packed in 2% salt in cans

k137.8 = 0.382

16.1

1.81

k126.7 = 0.219

(16.1)a

1

0.999

115.6–137.8

3.17

Spectrophotometric analysis, Hunter colorimeter

k115.6 = 0.124

5.59

Unblanched

k137.8 = 0.312

14.3

2.22

k126.7 = 0.208

(12.6)a

1

0.992

115.6–137.8

3.33

k115.6 = 0.115

6.03

Peas, puree

pH 6.5

22a

1

79.4–137.8

Lenz and Lund (1977b)

Peas, puree

Freeze-dried/rehydrated

Ryan-Stoneham and Tong (2000)

Chlorophyll a

pH 5.5 (w/control)

k100 = 0.16

4.3

k90 = 0.08

16.3

1

0.994

80–100

8.7

k80 = 0.046

15.1

pH 6.2 (w/control)

k100 = 0.082

8.5

k90 = 0.046

17.2

1

0.997

80–100

15.1

k80 = 0.022

31.5

pH 6.8 (w/control)

k100 = 0.034

20.4

k90 = 0.016

18.1

1

0.996

80–100

43.3

k80 = 0.0085

81.6

pH 7.5 (w/control)

k100 = 0.017

40.8

k90 = 0.008

18.9

1

0.998

80–100

86.6

k80 = 0.004

173.0

Chlorophyll b

pH 5.5 (w/control)

k100 = 0.077

9.0

Ryan-Stoneham and Tong (2000)

k90 = 0.039

16.4

1

0.996

80–100

17.8

k80 = 0.022

31.5

pH 6.2 (w/control)

k100 = 0.031

22.4

k90 = 0.015

14.8

1

0.969

80–100

46.2

k80 = 0.01

69.3

pH 6.8 (w/control)

k100 = 0.013

53.3

k90 = 0.006

17.1

1

0.986

80–100

115.5

k80 = 0.0035

198.0

pH 7.5 (w/control)

k100 = 0.008

86.6

k90 = 0.0031

18.1

1

0.950

80–100

223.6

k80 = 0.002

346.6

Peas, puree

Freeze-dried/rehydrated

Steet and Tong (1996a)

MWKR system

*k90 = 0.0344

20

k90 = 0.0376

18

Chlorophyll a

HPLC

k80 = 0.017

19.5

1

70–90

41

k80 = 0.0175

0.997

40

k70 = 0.0075

92

k70 = 0.0074

94

k90 = 0.0152

46

Steet and Tong (1996b)

k90 = 0.016

43

Chlorophyll b

HPLC

k80 = 0.008

17.1

1

70–90

87

k80 = 0.0086

0.997

81

k70 = 0.0039

178

k70 = 0.0039

94

Total green color

Lab color

k90 = 0.0 184

38

(a-value)

k90 = 0.0187

37

k80 = 0.0092

18.2

1

70–90

75

k80 = 0.0087

0.999

80

* replicates

k70 = 0.0043

161

k70 = 0.0042

165

Peas, puree

Freeze-dried/rehydrated

Steet and Tong (1996b)

MWKR system

Chlorophyll a

HPLC

*k120 = 0.2672

2.59

k120 = 0.2536

2.73

k110 = 0.137

20.4

1

0.998

100–120

5.06

k110 = 0.1324

5.24

k100 = 0.0652

10.63

k100 = 0.063

11.00

Chlorophyll b

HPLC

k120 = 0.107

6.48

k120 = 0.1007

6.88

k110 = 0.0537

18.2

1

0.995

100–120

12.91

k110 = 0.0557

12.44

k100 = 0.0311

22.29

k100 = 0.0284

24.41

Total green color

Lab color

k120 = 0.154

4.50

Steet and Tong (1996b)

(a-value)

k120 = 0.1547

4.48

k110 = 0.0774

20.3

1

0.999

100–120

8.96

k110 = 0.0766

9.05

k100 = 0.0383

18.10

*replicates

k100 = 0.0381

18.19

Peas

Whole

Packed in distilled water in No. 303 cans (Hunter colorimeter)

17.5a

1

98.9–126.7

Rao et al. (1981)

reen peas

Koca et al. (2006)

Chlorophyll a

Heat: Blanching of whole peas in buffer solns.

k100 = 13.3 × 10−2

5.21

pH 5.5

k90 = 11.8 × 10−2

14.2

1

0.944

70–100

5.87

Buffers: 0.1M citric/0.1M dihydrogen phosphate

k80 = 5.09 × 10−2

k90 = 1.17 × 10−2

13.6

k70 = 2.74 × 10−2

25.3

Cooled in ice, mashed, and analyzed

k100 = 7.37 × 10−2

9.4

pH 6.5

k90 = 3.62 × 10−2

12.1

1

0.965

70–100

19.14

k80 = 2.83 × 10−2

24.5

k70 = 1.64 × 10−2

42.3

Analysis: acetone extraction, HPLC

k100 = 1.82 × 10−2

38.1

pH 7.5

k90 = 1.73 × 10−2

4.9

1

0.871

70–100

40.1

k80 = 1.50 × 10−2

46.2

k70 = 1.01 × 10−2

68.6

Chlorophyll b

Heat: Blanching of whole peas in buffer solns.

Koca et al. (2006)

k100 = 0.53 × 10−2

131

pH 5.5

k90 = 0.25 × 10−2

10.5

1

0.786

70–100

277

Buffers: 0.1M citric/0.1M dihydrogen phosphate

k80 = 0.14 × 10−2

495

k70 = 0.16 × 10−2

433

k100 = 0.39 × 10−2

178

pH 6.5

Cooled in ice, mashed, and anlyzed

k90 = 0.14 × 10−2

11.5

1

0.832

70–100

495

k80 = 0.12 × 10−2

578

k70 = 0.09 × 10−2

770

Analysis: acetone extraction, HPLC

k100 = 0.16 × 10−2

433

pH 7.5

k90 = 0.12 × 10−2

7.0

1

0.997

70–100

578

Koca et al. (2006)

k80 = 0.09 × 10−2

770

k70 = 0.07 × 10−2

990

Green peas (pH 5.5)

k100 = 2.69 × 10−2

25.8

Koca et al. (2006)

Δ -a-value

Heat: Blanching of whole peas in buffer solns.

k90 = 2.16 × 10−2

8.4

1

0.944

70–100

32.1

k80 = 1.24 × 10−2

55.9

k70 = 1.08 × 10−2

64.2

Buffers: 0.1M citric/0.1M dihydrogen phosphate

k100 = 2.79 × 10−2

24.8

Δ -a/b-value

k90 = 2.12 × 10−2

9.5

1

0.995

70–100

32.7

Cooled in ice, mashed, and anlyzed

k80 = 1.40 × 10−2

49.5

k70 = 0.92 × 10−2

75.3

k100 = 0.55 × 10−2

126

Δ h-value

k90 = 0.39 × 10−2

8.2

1

0.996

70–100

178

Analysis: CIE-Lab Minolta CR-300

k80 = 0.28 × 10−2

248

k70 = 0.21 × 10−2

330

Green peas (pH 6.5)

k100 = 1.80 × 10−2

38.5

Δ -a-value

Heat: Blanching of whole peas in buffer solns.

k90 = 0.97 × 10−2

12.3

1

0.974

70–100

71.4

k80 = 0.76 × 10−2

91.2

k70 = 0.39 × 10−2

178

Buffers: 0.1M citric/0.1M dihydrogen phosphate

k100 = 1.91 × 10−2

36.3

Koca et al. (2006)

Δ -a/b-value

12.0

1

0.999

70–100

59.2

k80 = 0.76 × 10−2

91.2

Cooled in ice, mashed, and anlyzed

k70 = 0.46 × 10−2

151

k100 = 0.39 × 10−2

178

Δ h-value

k90 = 0.23 × 10−2

12.1

1

0.994

301

Analysis: CIE-Lab Minolta CR-300

k80 = 0.16 × 10−2

433

k70 = 0.09 × 10−2

770

Green peas (pH 7.5)

k100 = 0.60 × 10−2

116

Koca et al. (2006)

Δ -a-value

Heat: Blanching of whole peas in buffer solns.

k90 = 0.41 × 10−2

11.2

1

0.885

70–100

169

k80 = 0.18 × 10−2

385

k70 = 0.18 × 10−2

385

Buffers: 0.1M citric/0.1M dihydrogen phosphate

k100 = 0.62 × 10−2

118

Δ -a/b-value

k90 = 0.55 × 10−2

9.1

1

0.8 00

70–100

126

k80 = 0.23 × 10−2

301

Cooled in ice, mashed, and anlyzed

k70 = 0.25 × 10−2

277

k100 = 0.12 × 10−2

578

Δ h-value

k90 = 0.12 × 10−2

8.9

1

0.799

70–100

578

Analysis: CIE-Lab Minolta CR-300

k80 = 0.05 × 10−2

1386

k70 = 0.05 × 10−2

1386

Thompson seedless grapes (Vitis vinifera)

Zheng et al. (2014)

Total chlorophyll

Fresh grapes homogenized/heated in covered 50 mm dia. glass beakers/w/stirring up to 30 min (7 pts)

0.966

k80 = 4.46 × 10−2

Natural pH 3.4

0.982

k70 = 2.89 × 10−2

0.991

k60 = 2.80 × 10−2

0.965

k50 = 1.76 × 10−2

8.35

1

0.935

20–80

Analysis:

0.986

k40 = 1.67 × 10−2

Acetone extraction/UV-VIS/664 nm

0.953

k30 = 0.766 × 10−2

0.859

k20 = 0.306 × 10−2

Natural pH 3.4

0.871

k20 = 0.296 × 10−2

Zheng et al. (2014)

pH 2.0

0.917

k20 = 2.13 × 10−2

pH 3.0

0.916

k20 = 0.423 × 10−2

pH 4.0

0.895

k20 = 1.54 × 10−2

pH 5.0

0.970

k20 = 2.28 × 10−2

1

20

pH 6.0

0.911

k20 = 0.883 × 10−2

pH 7.0

0.957

k20 = 0.711 × 10−2

pH 8.0

0.904

k20 = 1.24 × 10−2

pH 9.0

0.984

k20 = 0.682 × 10−2

Chlorophyll

Spinach

Pureed in pyrex tubes

Gupte et al. (1964)

Chlorophyll a

pH 6.5

k148.9 = 0.658

1.05

k143.3 = 0.5099

1.36

k137.8 = 0.3947

15.4

1

0.999

126.7–148.9

1.76

k132.6 = 0.3056

(143)a

2.27

k126.7 = 0.2365

2.93

Chlorophyll b

pH 5.5

k148.9 = 0.3024

2.29

k143.3 = 0.2667

2.60

k137.8 = 0.235

7.6

1

0.999

126.7–148.9

2.95

k132.6 = 0.2072

3.35

k126.7 = 0.1828

3.79

Spinach

Schwartz and von Elbe (1983)

Chlorophyll a

Pureed in cans (natural pH)

0.992

ak126 = 0.2666

27.3

2.60

0.994

k121 = 0.1777

(25.2)a

1

0.998

116–126

3.90

HPLC analysis

0.984

k116 = 0.11

6.30

Chlorophyll b

0.982

k126 = 0.1195

24.7

5.80

0.998

k121 = 0.0845

(22.5)a

1

0.995

116–126

8.20

0.996

k116 = 0.0537

12.91

Pheophytin a

k126 = 0.07877

24.5

8.80

k121 = 0.05545

(20.7)a

1

0.996

116–126

12.50

k116 = 0.03555

19.50

Pheophytin b

k126 = 0.1035

16.9

6.70

k121 = 0.07877

(15.7)a

1

0.999

116–126

8.80

k116 = 0.05975

11.60

Chlorophyll

Spinach, puree

Blanched/freeze-dried/rehumidified

Lajolo and Lanfer Marquez (1982)

Chlorophyll a

pH 5.9

no glycerol

aw

g H2O/100 g

0.11

k56.7 = 2.00 × 10−5

62.0

1

46–56.7

34.7 × 103

k46 = 0.083 × 10−5

325 × 103

0.32

5.9

k56.7 = 13.95 × 10−5

5.0 × 103

6.0

k46 = 2.30 × 10−5

34.0

1

0.999

38.6–56.7

30.1 × 103

6.4

k38. 6 = 0.717 × 10−5

96.7 × 103

0.52

8.3

k56.7 = 17.67 × 10−5

3.9 × 103

10.9

k46 = 5.58 × 10−5

21.0

1

0.997

38.6–56.7

12.4 × 103

12.2

k38.6 = 2.82 × 10−5

24.6 × 103

0.75

17.7

k32 = 6.25 × 10−5

11.1 × 103

29.2

k38.6 = 13.40 × 10−5

9.7

1

32–38.6

5.2 × 103

38.0

k32 = 18.83 × 10−5

3.7 × 103

0.75

17.7

k32 = 6.25 × 10−5

11.1 × 103

29.2

k38.6 = 13.40 × 10−5

9.7

1

32–38.6

5.2 × 103

38.0

k32 = 18.83 × 10−5

3.7 × 103

pH 5.9

with glycerol

aw

g H2O/100 g

0.32

8.5

k38.6 = 7.20 × 10−5

1

38.6

9.6 × 103

0.32

6.1

k38.6 = 3.85 × 10−5

1

38.6

18.0 × 103

0.52

14.1

k38.6 = 10.28 × 10−5

1

38.6

6.7 × 103

0.32

k38.6 = 1.83 × 10−5

1

38.6

37.9 × 103

0.52

12.6

k38.6 = 6.58 × 10−5

1

38.6

10.5 × 103

0.32

5.4

k38.6 = 1.03 × 10−5

1

38.6

67.3 × 103

0.52

10.2

k38.6 = 6.63 × 10−5

1

38.6

10.5 × 103

0.75

27.7

k38.6 = 51.87 × 10−5

1

38.6

1.3 × 103

0.32

5.0

k38.6 = 1.00 × 10−5

1

38.6

69.3 × 103

0.52

10.2

k38.6 = 5.18 × 10−5

1

38.6

13.4 × 103

0.75

27.7

k38.6 = 21.78 × 10−5

1

38.6

3.2 × 103

Chlorophyll

Yerba Maté leaves (Ilex paraguariensis Saint Hilaire)

Schmalko et al. (2005)

Blanched leaves (16 + 5%, wb)

Chlorophyll a

Air-dried to reach desired MC

0.993

k80 = 9.06 × 10−3

76.5

aw (0.789–0.812)

0.998

k70 = 3.57 × 10−3

15.58

1

0.975

50–80

194.1

Leaves ground to 40 mesh

0.996

k60 = 2.46 × 10−3

282.1

0.980

k50 = 1.03 × 10−3

670.8

0.993

k80 = 7.64 × 10−3

90.7

aw (0.652–0.690)

Water activities equilibrated over specified saturated salt solns.

0.998

k70 = 2.34 × 10−3

21.75

1

0.988

50–80

296.0

0.997

k60 = 0.963 × 10−3

719.5

0.995

k50 = 0.415 × 10−3

1670.2

0.995

k80 = 3.76 × 10−3

184.4

aw (0.497–0.514)

Pigment extraction: acetone: water

0.995

k70 = 2.22 × 10−3

24.59

1

0.975

50–80

312.2

0.988

k60 = 0.618 × 10−3

1121.0

0.993

k50 = 0.157 × 10−3

4424.3

Analysis: HPLC UV-VIS

0.995

k80 = 1.71 × 10−3

404.6

aw (0.260–0.305)

0.996

k70 = 1.11 × 10−3

23.48

1

0.971

50–80

626.3

0.994

k60 = 0.263 × 10−3

2632.2

0.994

k50 = 0.088 × 10−3

7846.9

0.993

k80 = 1.09 × 10−3

634.9

aw (0.105–0.111)

0.990

k70 = 1.02 × 10−3

21.55

1

0.924

50–80

680.7

0.998

k60 = 0.230 × 10−3

3013.7

0.992

k50 = 0.077 × 10−3

9041.1

Chlorophyll b

Air-dried to reach desired MC

0.994

k80 = 4.95 × 10−3

140.1

Schmalko et al. (2005)

aw (0.789–0.812)

996.000

k70 = 1.88 × 10−3

17.22

1

0.969

50–80

369.7

Leaves ground to 40 mesh

0.998

k60 = 1.32 × 10−3

523.8

0.997

k50 = 0.442 × 10−3

1569.4

0.980

k80 = 3.76 × 10−3

184.2

aw (0.652–0.690)

Water activities equilibrated over specified saturated salt solns.

0.995

k70 = 1.02 × 10−3

24.12

1

0.990

50–80

681.8

0.997

k60 = 0.398 × 10−3

1740.1

0.994

k50 = 0.147 × 10−3

4726.0

0.990

k80 = 1.67 × 10−3

416.3

Schmalko et al. (2005)

aw (0.497–0.514)

0.993

k70 = 0.993 × 10−3

24.74

1

0.976

50–80

697.8

0.986

k60 = 0.233 × 10−3

2970.6

0.997

k50 = 0.072 × 10−3

9671.8

0.997

k80 = 0.965 × 10−3

718.3

aw (0.260–0.305)

0.997

k70 = 0.557 × 10−3

20.29

1

0.988

50–80

1245.2

0.995

k60 = 0.178 × 10−3

3886.8

0.993

k50 = 0.072 × 10−3

9671.8

0.997

k80 = 0.828 × 10−3

836.8

aw (0.105–0.111)

0.998

k70 = 0.633 × 10−3

19.66

1

0.958

50–80

1094.4

0.998

k60 = 0.185 × 10−3

3746.7

0.994

k50 = 0.070 × 10−3

9902.1

a-value

Air-dried to reach desired MC

0.985

k80 = 1.25 × 10−3

556.7

Schmalko et al. (2005)

aw (0.789–0.812)

0.988

k70 = 0.888 × 10−3

10.90

1

0.971

50–80

780.3

Leaves ground to 40 mesh

0.990

k60 = 0.602 × 10−3

1152.0

0.989

k50 = 0.287 × 10−3

2415.1

0.948

k80 = 1.49 × 10−3

465.2

aw (0.652–0.690)

Water activities equilibrated over specified saturated salt solns.

0.994

k70 = 0.463 × 10−3

18.52

1

0.975

50–80

1496.0

0.991

k60 = 0.265 × 10−3

2615.6

0.994

k50 = 0.117 × 10−3

5941.3

0.992

k80 = 0.862 × 10−3

804.4

aw (0.497–0.514)

0.991

k70 = 0.307 × 10−3

20.83

1

0.980

50–80

2260.3

0.986

k60 = 0.178 × 10−3

3886.8

0.994

k50 = 0.048 × 10−3

14341.0

0.993

k80 = 2.50 × 10−4

2772.6

Schmalko et al. (2005)

aw (0.260–0.305)

Color Touch Colorimeter CIELAB scale

0.995

k70 = 1.33 × 10−4

14.40

1

0.999

50–80

5198.6

0.991

k60 = 0.733 × 10−4

9452.0

0.990

k50 = 0.370 × 10−4

18904.0

0.995

k80 = 1.02 × 10−4

6817.8

aw (0.105–0.111)

0.990

k70 = 1.43 × 10−4

15.66

1

0.798

50–80

4835.9

0.995

k60 = 0.483 × 10−4

14341.0

0.988

k50 = 0.150 × 10−4

46209.8

Anthocyanins

Blackberry juice

(cyanidin-3-glucoside)

Debicki-Pospišil, et al. (1983)

Control

k70 = 0.001178

15.0

588

k50 = 0.00035

(14.8)a

1

0.998

24–70

1,980

k24 = 0.0000395

17,548

With furfural

k70 = 0.001395

13.2

497

k50 = 0.0005067

(13.0)a

1

0.996

24–70

1,368

k24 = 0.0000717

9,667

HMF

k70 = 0.001478

13.1

469

k50 = 0.0005783

(12.7)a

1

0.992

24–70

1,199

k24 = 0.000078

8,887

benzaldehyde

k70 = 0.001927

11.3a

1

24–70

360

formaldehyde

k70 = 0.003367

6.8a

1

24–70

206

Cyanidin-3-glucoside

Debicki-Pospišil, et al. (1983)

Control (in citrate buffer)

k70 = 0.00096

20.5

722

k50 = 0.0001823

(20.1)a

1

0.998

24–70

3,802

k24 = 0.00000933

74,290

With furfural

k70 = 0.001217

17.9

570

k50 = 0.0003067

(17.4)a

1

0.996

24–70

2,260

k24 = 0.0000217

31,940

HMF

k70 = 0.001525

18.8

455

k50 = 0.0003683

(14.9)a

1

0.995

24–70

1,882

k24 = 0.00002183

31,750

Anthocyanins

Boysenberry juice (A520/A420)

0.907

k100 = 0.001546

(20)a

1

20–120

488

Ponting et al. (1960)

Black currant (Ribes nigrum) Juice

Hellström et al. (2013)

Total anthocyanins

From frozen concentrate

k21 = 22.8 × 10−6

0.304 × 105

k9 = 5.88 × 10−6

18.17

1

0.999

4–21

1.16 × 105

65 Bx; dil. 1:30 + 100 mg benzoic acid/L (pH 3.27)

k4 = 3.39 × 10−6

2.05 × 105

Delphinidin 3-glucoside

k21 = 28.8 × 10−6

0.241 × 105

k9 = 7.14 × 10−6

19.75

1

0.999

4–21

0.971 × 105

Storage: capped 50 ml tubes/2–3 ml air headspace

k4 = 3.56 × 10−6

1.95 × 105

Delphinidin 3-rutinoside

k21 = 25.4 × 10−6

0.273 × 105

k9 = 6.49 × 10−6

19.09

1

0.999

4–21

1.07 × 105

Stored dark up to 22 wks.

k4 = 3.39 × 10−6

2.05 × 105

Cyanidin 3-glucoside

k21 = 2.14 × 10−6

0.034 × 105

k9 = 5.46 × 10−6

19.50

1

0.999

4–21

1.27 × 105

k4 = 2.72 × 10−6

2.55 × 105

Cyanidin 3-rutinoside

Analysis: HPLC λ = 518 nm/MS

k21 = 19.6 × 10−6

0.354 × 105

k9 = 5.13 × 10−6

19.01

1

0.999

4–21

1.35 × 105

k4 = 2.62 × 10−6

2.64 × 105

Anthocyanins

Chokeberry (Aronia mitchurinii) Juice

Hellström et al. (2013)

Total anthocyanins

From frozen whole berries, mashed

k21 = 10.2 × 10−6

0.678 × 105

k9 = 2.89 × 10−6

15.39

1

0.987

4–21

2.40 × 105

65 Bx; dil. 1:30 + 100 mg benzoic acid/L (pH 3.27)

k4 = 2.12 × 10−6

3.28 × 105

Cyanidin 3-galactoside

k21 = 9.78 × 10−6

0.709 × 105

k9 = 2.69 × 10−6

19.15

1

0.995

4–21

2.58 × 105

Storage: capped 50 ml tubes/2–3 ml air headspace

k4 = 1.27 × 10−6

5.46 × 105

Cyanidin 3-glucoside

k21 = 10.9 × 10−6

0.635 × 105

k4 = 3.95 × 10−6

17.03

1

0.973

4–21

1.75 × 105

Stored dark up to 22 wks

k21 = 1.17 × 10−6

4.06 × 105

Cyanidin 3-arabinoside

k21 = 11.3 × 10−6

0.615 × 105

Analysis: HPLC λ = 518 nm/MS

k9 = 3.27 × 10−6

18.53

1

0.994

4–21

2.12 × 105

k4 = 1.56 × 10−6

4.46 × 105

Anthocyanins

Chokeberry juice cont. -

k21 = 9.39 × 10−6

0.738 × 105

Hellström et al. (2013)

Cyanidin 3-xyloside

k9 = 2.33 × 10−6

18.13

1

0.997

4–21

2.97 × 105

k4 = 1.43 × 10−6

4.85 × 105

Crowberry (Empetrum nigrum) Juice

Hellström et al. (2013)

Total anthocyanins

From frozen concentrate

k21 = 31.5 × 10−6

0.220 × 105

k9 = 9.42 × 10−6

16.49

1

0.999

4–21

0.736 × 105

65 Bx; dil. 1:30 + 100 mg benzoic acid/L (pH 3.27)

k4 = 5.59 × 10−6

1.24 × 105

Delphinidin 3-galactoside

k21 = 47.4 × 10−6

0.146 × 105

k9 = 9.99 × 10−6

20.90

1

0.999

4–21

0.694 × 105

Storage: capped 50 ml tubes/2–3 ml air headspace

k4 = 5.33 × 10−6

1.30 × 105

Delphinidin 3-arabinoside

k21 = 45.2 × 10−6

0.153 × 105

k9 = 9.64 × 10−6

19.93

1

0.996

4–21

0.719 × 105

Stored dark up to 22 wks

k4 = 5.73 × 10−6

1.21 × 105

Cyanidin 3-galactoside

k21 = 44.4 × 10−6

0.156 × 105

k9 = 10.3 × 10−6

19.71

1

0.999

4–21

0.672 × 105

Analysis: HPLC λ = 518 nm/MS

k4 = 5.64 × 10−6

1.23 × 105

Cyanidin 3-arabinoside

k21 = 43.5 × 10−6

0.159 × 105

k9 = 10.9 × 10−6

19.44

1

0.999

4–21

0.635 × 105

k4 = 5.59 × 10−6

1.24 × 105

Petunidin 3-galactoside

k21 = 40.2 × 10−6

0.172 × 105

Hellström et al. (2013)

k9 = 9.39 × 10−6

19.72

1

0.999

4–21

0.738 × 105

k4 = 5.09 × 10−6

1.36 × 105

Peonidin 3-galactoside

k21 = 41.9 × 10−6

0.165 × 105

k9 = 10.2 × 10−6

19.75

1

0.999

4–21

0.679 × 105

k4 = 5.29 × 10−6

1.31 × 105

Peonidin 3-arabinoside

k21 = 37.4 × 10−6

0.1853 × 105

k9 = 9.29 × 10−6

19.09

1

0.999

4–21

0.746 × 105

k4 = 5.09 × 10−6

1.36 × 105

Malvidin 3-galactoside

k21 = 37.4 × 10−6

0.185 × 105

k9 = 9.63 × 10−6

19.50

1

0.999

4–21

0.720 × 105

k4 = 5.21 × 10−6

1.33 × 105

Malvidin 3-arabinoside

k21 = 40.0 × 10−6

0.173 × 105

k9 = 10.3 × 10−6

19.01

1

0.999

4–21

0.671 × 105

k4 = 5.78 × 10−6

1.20 × 105

Anthocyanins

Blueberry

IQF blueberries (MC = 86.5%)

Martynenko and Chen (2016)

Tot. Anthocyanin:

Lab-scale HTD processing

0.964

k105 = 17.9 × 10−3

38.7

Hold times: 0–400 min

0.945

k95 = 8.60 × 10−3

80.6

15 g puree/solvent extract

0.979

k87.5 = 5.60 × 10−3

15.85

1

0.994

70–105

123.8

Analysis: Differential spectrophotometry (A520/A700)

0.933

k80 =3.70 × 10−3

187.3

0.882

k70 =2.00 × 10−3

346.6

PPC Formation:

PPC = PC/CD × 100%

0.921

k105 = 29.5 × 10−2

2.3

(% polymeric color)

PC = polymeric color

0.987

k95 = 16.5 × 10−2

4.2

CD = color density

0.980

k87.5 = 6.20 × 10−2

18.95

0

0.951

70–105

11.2

0.980

k80 = 3.69 × 10−2

18.8

0.978

k70 = 2.62 × 10−2

26.5

Anthocyanins

Cherries

Total anthocyanins

Ochoa et al. (2001)

Fresh fruit in sucrose soln./ packed in glass jars

Pasteurized: 90°C/20 min

(0.073 m dia × 0.012 m h)

Store: 10 mo

Sour (Prunus cerasus)

With light

a0.986

k20 = 3.194 × 10−5

1

20

2.170 × 104

No light

0.995

k20 = 2.410 × 10−5

1

20

2.876 × 104

Sweet (Prunus avium)

With light

0.992

k20 = 2.500 × 10−5

1

20

2.773 × 104

No light

0.982

k20 = 2.083 × 10−5

1

20

3.328 × 104

Store: 5 mo

Sour (Prunus cerasus)

With light

0.975

k40 = 3.729 × 10−6

1.859 × 105

0.998

k20 = 1.729 × 10−6

8.49

1

0.998

4–40

4.009 × 105

0.991

k4 = 0.764 × 10−6

9.073 × 105

Sweet (Prunus avium)

With light

0.963

k40 = 4.826 × 10−6

1.436 × 105

0.991

k20 = 2.694 × 10−6

8.31

1

0.999

4–40

2.573 × 105

0.957

k4 = 0.889 × 10−6

7.797 × 105

Anthocyanins

Cherry juice (sour)

Cemeroglu et al. (1994)

15° Brix

Heated: 20 ml Pyrex tubes/w/minimal headspace

0.982

k80 = 5.661 × 10−4

1.22 × 103

0.937

k70 = 2.048 × 10−4

3.38 × 103

0.960

k60 = 0.875 × 10−4

16.37

1

0.936

50–80

7.92 × 103

0.949

k50 = 0.665 × 10−4

10.42 × 103

Max. 48 h

45° Brix

0.976

k80 = 9.532 × 10−4

0.727 × 103

0.982

k70 = 4.052 × 10−4

1.71 × 103

0.972

k60 = 1.832 × 10−4

18.13

1

0.997

50–80

3.78 × 103

0.916

k50 = 0.858 × 10−4

8.08 × 103

71° Brix

0.996

k80 = 16.192 × 10−4

0.428 × 103

0.927

k70 = 6.745 × 10−4

1.03 × 103

0.931

k60 = 3.125 × 10−4

19.14

1

0.999

50–80

2.22 × 103

0.951

k50 = 1.250 × 10−4

5.55 × 103

Pasteurized/stored up to 160 days

0.958

k37 = 1.281 × 10−5

0.541 × 105

45° Brix

0.910

k20 = 0.367 × 10−5

15.58

1

0.992

5–37

1.89 × 105

0.906

k5 = 0.694 × 10−6

9.99 × 105

71° Brix

0.970

k37 = 1.659 × 10−5

0.418 × 105

0.904

k20 = 0.455 × 10−5

18.02

1

0.981

5–37

1.524 × 105

0.949

k5 = 0.569 × 10−6

12.17 × 105

Cherry juice model system

Pelargonidin-3, 5-diglucoside

pH 2.5

k108 = 0.0426

16

Ioncheva and Tanchev (1974)

k98 = 0.01608

27.4

1

0.999

78–108

43

k88 = 0.006

116

k78 = 0.001896

366

pH 3.5

k108 = 0.03612

19

k98 = 0.015

27.8

1

0.998

78–108

46

k88 = 0.004596

151

k78 = 0.00165

420

Pelargonidin-3, 5-diglucoside

pH 4.5

k108 = 0.02928

24

Ioncheva and Tanchev (1974)

k98 = 0.01206

26.7

1

0.999

78–108

57

k88 = 0.00396

175

k78 = 0.001482

468

Cyanidin-3, 5-diglucoside

pH 2.5

k108 = 0.0357

19

k98 = 0.01626

23.5

1

0.999

78–108

43

k88 = 0.00657

106

k78 = 0.002532

274

pH 3.5

k108 = 0.0288

24

k98 = 0.01296

25.4

1

0.996

78–108

53

k88 = 0.00417

166

k78 = 0.001746

397

pH 4.5

k108 = 0.03228

21

k98 = 0.01392

24.2

1

0.999

78–108

50

k88 = 0.005466

127

k78 = 0.002112

328

Peonidin-3, 5-diglucoside

pH 2.5

k108 = 0.03822

18

k98 = 0.01566

27.2

1

0.998

78–108

44

k88 = 0.00495

140

k78 = 0.001842

376

pH 3.5

k108 = 0.03156

22

k98 = 0.01338

24.3

1

0.934

78–108

52

k88 = 0.004548

152

k78 = 0.002136

325

pH 4.5

k108 = 0.02952

23

k98 = 0.01161

24.3

1

0.999

78–108

60

k88 = 0.004986

139

k78 = 0.001848

375

Petunidin-3, 5-diglucoside

pH 2.5

k108 = 0.0507

14

k98 = 0.02532

19.4

1

0.999

78–108

27

k88 = 0.012

58

k78 = 0.00573

121

Petunidin-3, 5-diglucoside

pH 3.5

k108  = 0.03846

18

Ioncheva and Tanchev (1974)

k98 = 0.01836

19.6

1

0.999

78–108

38

k88 = 0.00918

76

k78 = 0.004152

167

Petunidin-3, 5-diglucoside

pH 4.5

k108 = 0.0291

24

Ioncheva and Tanchev (1974)

k98 = 0.01296

20.4

1

0.998

78–108

53

k88 = 0.006

116

k78 = 0.002904

239

Malvidin-3, 5-diglucoside

pH 2.5

k108 = 0.0513

14

k98 = 0.01986

26.5

1

0.999

78–108

35

k88 = 0.00762

91

k78 = 0.002544

272

pH 3.5

k108 = 0.02628

26

k98 = 0.0114

26.0

1

0.998

78–108

61

k88 = 0.003816

182

k78 = 0.001458

475

pH 4.5

k108 = 0.02244

31

k98 = 0.00942

26.2

1

0.998

78–108

74

k88 = 0.00309

224

k78 = 0.001212

572

Anthocyanins

Cornelian cherries (Cornus mas L.)

Moldovan and David (2014)

cyanidin-3-glucoside

Extract (no preservative)

Frozen cherries crushed/extracted/w/acidified water

0.992

k75 = 137.7 × 10−5

503

extract: distilled water/HCl

0.919

k22 = 1.45 × 10−5

14.0

1

0.954

2–75

47,803

pH 3.02

0.963

k2 = 0.80 × 10−5

86,643

Extract + Na-benzoate

0.991

k75 = 131.4 × 10−5

528

(0.1 g/L)

50 ml portions, capped, kept from light

0.974

k22 = 1.55 × 10−5

13.5

1

0.947

2–75

44,719

0.973

k2 = 0.95 × 10−5

72,963

Extract + K-sorbate

0.991

k75 = 128.9 × 10−5

538

(0.1 g/L)

Analysis: UV-VIS

0.970

k22 = 1.88 × 10−5

13.0

1

0.954

2–75

36,804

0.990

k2 = 1.08 × 10−5

63,983

Anthocyanins

Cranberries

Pigment extracted and concentrated solution in pH 2.5 phos-buffer

Attoe and von Elbe (1981)

Cyanidin-3-arabinoside

No light

k55 = 0.000283

26.8

1

40–55

2,450

k40 = 0.000395

17,500

With light

k55 = 0.000435

8.7

1

40–55

1,590

(400 ft-c)

k40 = 0.00023

3,010

Cyanidin-3-galactoside

No light

k55 = 0.000267

26.7

1

40–55

2,600

k40 = 0.0000373

18,600

With light

k55 = 0.000363

7.7

1

40–55

1,900

Attoe and von Elbe (1981)

(400 ft-c)

k40 = 0.000207

3,350

Peonidin-3-arabinoside

No light

k55 = 0.000287

24.9

1

40–55

2,420

k40 = 0.0000458

15,100

With light

k55 = 0.000422

7.6

1

40–55

1,640

(400 ft-c)

k40 = 0.000242

2,860

Peonidin-3-galactoside

No light

k55 = 0.000265

26.4

1

40–55

2,620

k40 = 0.000038

18,200

With light

k55 = 0.000337

5.4

1

40–55

2,060

(400 ft-c)

k40 = 0.0000227

30,500

Anthocyanins

18 Anthocyanins (averaged)

Tanchev (1983)

Fruit juice

k108 = 0.01925

36

k98 = 0.008351

22.2

1

0.999

78–108

83

k88 = 0.003667

189

k78 = 0.001561

444

Citrate buffer

k108 = 0.02666

26

k98 = 0.01005

25.1

1

0.999

78–108

69

k88 = 0.004101

169

k78 = 0.00154

450

Anthocyanins

Concord grape pigments

Sastry and Tischer (1952)

Buffer solution (McIlvaine)

pH 3.4

0.674

k121 = 0.01879

37

0.880

k98.9 = 0.00743

13.1

1

0.999

76.7–121

93

0.838

k76.7 = 0.002263

306

Anthocyanins

Grape juice

(Total Anthocyanins)

Ponting et al. (1960)

Alicante bouschet (A)

0.991

k100 = 0.002822

1

100

246

Carignane (B)

Evelyn colorimeter (A520/A420)

0.943

k100 = 0.003438

1

100

202

Zinfandel (C)

0.913

k100 = 0.003443

1

100

201

Grape blend

0.996

k100 = 0.002592

(28)a

1

20–120

267

(45A:45B:10C)

Anthocyanins

Concord grape pigments (V. labrusca)

Calvi and Francis (1978)

CON -

(Control: 0.1M citrate-phosphate buffer)

pH 3.2

0.958

k95 = 0.005467

127

0.990

k90 = 0.0036

18.9

1

0.991

85–95

193

0.992

k85 = 0.00265

262

GLU -

(buffer + 15% glucose)

pH 3.2

0.990

k95 = 0.005383

129

0.992

k90 = 0.0037

19.7

1

0.999

85–95

187

0.984

k85 = 0.002533

274

SUC -

(buffer + 15% sucrose)

pH 3.2

0.994

k95 = 0.008233

84

0.998

k90 = 0.004783

23.8

1

0.995

80–95

145

0.978

k85 = 0.0033

210

0.990

k80 = 0.002

347

FJD -

(buffer + 15% sucrose + 10% white grape juice)

pH 3.2

0.994

k95 = 0.008783

79

0.996

k90 = 0.0066

17.9

1

0.993

85–95

105

0.990

k85 = 0.004433

156

Anthocyanins

Calvi and Francis (1978)

CON

pH 2.8

k90 = 0.0044

1

90

158

GLU

pH 2.8

k90 = 0.003983

1

90

174

SUC

pH 2.8

k90 = 0.004667

1

90

149

CON

pH 3.6

k90 = 0.003867

1

90

179

GLU

pH 3.6

k90 = 0.003517

1

90

197

SUC

pH 3.6

k90 = 0.005133

1

90

135

Anthocyanins

Plum juice

Cyanidin-3-rutinoside

pH 2.5

k108 = 0.02028

34

Tanchev and Joncheva (1973)

k98 = 0.00768

21.8

1

0.991

78–108

90

k88 = 0.00348

199

k78 = 0.0017

408

pH 3.5

k108 = 0.02052

34

k98 = 0.00762

20.6

1

0.984

78–108

91

k88 = 0.0037

187

k78 = 0.00196

354

pH 4.5

k108 = 0.02664

26

k98 = 0.00876

22.6

1

0.981

78–108

79

k88 = 0.00395

175

k78 = 0.00201

345

Peonidin-3-rutinoside

pH 2.5

k108 = 0.02904

24

Tanchev and Joncheva (1973)

k98 = 0.01014

29.9

1

0.999

78–108

68

k88 = 0.00327

212

k78 = 0.000996

696

pH 3.5

k108 = 0.03

23

k98 = 0.01254

23.4

1

0.997

78–108

55

k88 = 0.00477

145

k78 = 0.00219

317

pH 4.5

k108 = 0.02988

23

k98 = 0.01428

22.9

1

0.995

78–108

49

k88 = 0.00509

136

k78 = 0.00239

290

Anthocyanins

Pomegranate juice

(mostly delphinidin-3, 5-diglucoside)

Mishkin and Saguy (1982)

Total anthocyanins

k92 = 0.0018

385

k90 = 0.00088

25.0

1

0.945

70–92

788

k80 = 0.00054

1,284

k70 = 0.00015

4,621

Raspberries

Total anthocyanins

Ochoa et al. (2001)

Fresh fruit in sucrose soln./ packed in glass jars

Pasteurized: 90°C/20 min

(0.073 m dia × 0.012 m h)

Store: 10 mo

With light

a0.975

k20 = 3.819 × 10−5

1

20

1.815 × 104

No light

0.995

k20 = 1.528 × 10−5

1

20

4.537 × 104

Store: 5 mo

With light

0.975

k40 = 4.931 × 10−6

1.406 × 105

0.998

k20 = 2.347 × 10−6

6.24

1

0.998

4–40

2.953 × 105

0.991

k4 = 1.389 × 10−6

4.990 × 105

Anthocyanins

Raspberry juice

Tanchev (1972)

Total anthocyanins

Malling Promise

pH 3.2

k108 = 0.01986

35

k98 = 0.00852

22.0

1

0.999

78–108

81

k88 = 0.00396

175

k78 = 0.00162

428

New Burg

pH 3.4

k108 = 0.01638

42

k98 = 0.0064 8

24.1

1

0.999

78–108

107

k88 = 0.002874

241

k78 = 0.00105

660

Bulgarian ruby

pH 3.3

k108 = 0.0204

34

Tanchev (1972)

k98 = 0.00894

21.2

1

0.998

78–108

78

k88 = 0.00351

197

k78 = 0.00195

355

Raspberry juice

Tanchev (1972)

Malling Promise

pH 3.2

k108 = 0.0171

41

k98 = 0.00615

23.0

1

0.988

78–108

113

k88 = 0.00345

201

k78 = 0.001164

595

New Burg

pH 3.4

k108 = 0.01416

49

k98 = 0.00618

23.6

1

0.999

78–108

112

k88 = 0.002652

261

k78 = 0.000972

713

Bulgarian ruby

pH 3.3

k108 = 0.02052

34

k98 = 0.00867

23.6

1

0.999

78–108

80

k88 = 0.00348

199

k78 = 0.001434

483

Anthocyanins

Raspberry pulp (Rubus idaeus L.)

Summen and Erge (2014)

Anthocyanin

Fresh fruit/homogenized

0.96

k90 = 36.5 × 10−4

189.9

(total monomeric)

Heat: water bath/20 ml test tubes, 2 cm ID (8pts)

0.94

k80 = 19.0 × 10−4

11.9

1

0.985

60–90

364.8

0.91

k70 = 13.2 × 10−4

526.4

Time up to 7 hrs

0.94

k60 = 7.83 × 10−4

884.9

L-ascorbic acid

Extract 1 g pulp/w/MeOH + 0.1% HCl/centrifuge/supernatant evap. × 2

0.94

k90 = 7.00 × 10−4

990.2

0.90

k80 = 6.00 × 10−4

1155.2

0.90

k70 = 4.67 × 10−4

3.8

1

0.928

60–90

1485.3

Analyses: UV-VIS

0.92

k60 = 4.50 × 10−4

1540.3

a-value

Anthocyanins: A700/A527

0.99

k90 = 10.8 × 10−4

644.8

Ascorbic acid: A500

0.98

k80 = 4.10 × 10−4

1690.6

0.97

k70 = 2.48 × 10−4

18.4

1

0.986

60–90

2791.2

Color: Hunterlab

0.94

k60 = 0.983 × 10−4

7049.0

b-value

0.77

k90 = 12.3 × 10−4

565.8

0.95

k80 = 10.6 × 10−4

651.9

0.94

k70 = 6.75 × 10−4

12.0

1

0.909

60–90

1026.9

0.84

k60 = 2.73 × 10−4

2535.9

Anthocyanins

Summen and Erge (2014)

Chroma

0.97

k90 = 10.9 × 10−4

634.0

0.98

k80 = 5.03 × 10−4

1377.1

0.93

k70 = 3.07 × 10−4

16.3

1

0.992

60–90

2260.3

0.94

k60 = 1.35 × 10−4

5134.4

Anthocyanins

Strawberry juice

In oxygen

pH 3.05

k45 = 5.95 × 10−4

1

45

1,165

Lukton et al. (1956)

pH 3.55

k45 = 10.83 × 10−4

1

45

640

pH 4.30

k45 = 15.33 × 10−4

1

45

452

In nitrogen

pH 3.05

k45 = 0.85 × 10−4

1

45

8,155

pH 3.55

k45 = 1.00 × 10−4

1

45

6,966

pH 4.30

k45 = 1.24 × 10−4

1

45

5,590

Anthocyanins

Strawberry juice

Spectrophotometer

Ponting et al. (1960)

(A490/A420)

(19)a

1

20–120

Anthocyanins

Wine grapes

Peron et al. (2017)

Juçara grapes

Extraction

0.96

k90 = 12.5 × 10−4

0.555 × 103

(Euterpe edulis Martius)

Filtration

0.91

k80 = 6.48 × 10−4

1.07 × 103

0.95

k70 = 4.26 × 10−4

23.3

1

0.955

50–90

1.63 × 103

malvidin-3-O-glu predominates

0.97

k60 = 1.01 × 10−4

6.86 × 103

0.97

k50 = 0.220 × 10−4

31.5 × 103

Italia grapes

Analysis:

0.97

k90 = 54.5 × 10−4

0.127 × 103

(Evitus vinifera L.)

UV/VIS – A528

0.97

k80 = 28.3 × 10−4

0.245 × 103

0.97

k70 = 15.3 × 10−4

22.0

1

0.980

50–90

0.453 × 103

0.99

k60 = 4.57 × 10−4

1.52 × 103

0.99

k50 = 1.23 × 10−4

5.64 × 103

Anthocyanins

Wine model systems

Baranowski and Nagle (1983)

Malvidin-3-glucoside

Glass tubes,

k52 = 6.60 × 10−5

1.05 × 104

(M-3-G)

O2-free atmos.,

k42 = 9.60 × 10−4

16.9

1

0.778

22–52

0.0722 × 104

HPLC

k32 = 4.08 × 10−6

(28)a

16.99 × 104

k22 = 4.38 × 10−6

15.83 × 104

M-3-G + d-catechin

k52 = 3.24 × 10−5

2.14 × 104

k42 = 1.98 × 10−5

12.0

1

0.971

22–52

3.50 × 104

k32 = 1.26 × 10−5

(11)a

5.50 × 104

k22 = 4.68 × 10−6

14.81 × 104

M-3-G + d-catechin + equimolar acetaldehyde

k52 = 2.94 × 10−5

2.36 × 104

k42 = 2.04 × 10−5

13.0

1

0.832

22–52

3.40 × 104

k32 = 1.68 × 10−5

(10)a

4.13 × 104

k22 = 3.30 × 10−6

21.00 × 104

M-3-G + d-catechin + XS acetaldehyde

k52 = 9 .60 × 10−5

0.722 × 104

Baranowski and Nagle (1983)

k42 = 5.94 × 10−5

12.7

1

0.994

22–52

1.17 × 104

k32 = 2.88 × 10−5

(13)a

2.41 × 104

k22 = 1.32 × 10−5

5.25 × 104

Wine model systems

(grape skin pigments in potassium hydrogen tartrate, pH 3.5)

Storage:

Romero and Bakker (2000)

50 ml vials/air/dark/up to 140 days

HPLC

PA/TA Ratio

Malvidin-3-glucoside

0

0.984

k32 = 59.03 × 10−6

1.17 × 10−4

0.984

k20 = 29.17 × 10−6

0.32

1

10–32

2.38 × 10−4

PA = pyruvic acid

0.963

k15 = 5.56 × 10−6

12.48 × 10−4

TA = total anthocyanins

0.975

k10 = 4.86 × 10−6

14.26 × 10−4

300

0.975

k32 = 39.58 × 10−6

1.75 × 10−4

0.981

k20 = 20.14 × 10−6

0.24

1

10–32

3.44 × 10−4

0.987

k15 = 8.33 × 10−6

8.32 × 10−4

0.986

k10 = 4.86 × 10−6

14.26 × 10−4

Malvidin-3-acetylglucoside

0

0.933

k32 = 54.86 × 10−6

1.17 × 10−4

Romero and Bakker (2000)

0.994

k20 = 34.03 × 10−6

0.31

1

10–32

2.38 × 10−4

0.919

k15 = 5.56 × 10−6

12.48 × 10−4

0.973

k10 = 4.86 × 10−6

14.26 × 10−4

300

0.927

k32 = 41.67 × 10−6

1.66 × 10−4

0.921

k20 = 18.06 × 10−6

0.23

1

10–32

3.84 × 10−4

0.933

k15 = 11.81 × 10−6

5.87 × 10−4

0.875

k10 = 5.56 × 10−6

12.48 × 10−4

Malvidin-3p-coumaryl-glucoside

0

0.984

k32 = 104.9 × 10−6

0.661 × 10−4

0.997

k20 = 53.47 × 10−6

0.37

1

10–32

1.30 × 10−4

0.933

k15 = 8.33 × 10−6

8.32 × 10−4

0.906

k10 = 5.56 × 10−6

12.48 × 10−4

300

0.963

k32 = 67.36 × 10−6

1.03 × 10−4

0.927

k20 = 26.39 × 10−6

0.30

1

10–32

2.63 × 10−4

0.938

k15 = 10.42 × 10−6

6.65 × 10−4

0.925

k10 = 5.56 × 10−6

12.48 × 10−4

Anthocyanins

Black rice (Oryza sativa L.) Extract

Loypimai et al. (2016)

pH 2.0

0.901

k100 = 1.16 × 10−3

598

Cyanidin-3-O-glucoside

0.875

k80 = 0.520 × 10−3

6.05

1

0.866

60–100

1326

3 g extract/1 L 2.0M acetate buffer/pH adjust

0.952

0.931

k60 = 0.43 × 10−3

1614

k100 = 2.42 × 10−3

286

Cyanidin-3-O-rutinoside

0.931

k80 = 0.99 × 10−3

6.98

1

0.885

60–100

696

Heat: waterbath/capped 15 ml brown glass vials 0–120 min (7 pts)

0.936

k60 = 0.77 × 10−3

900

0.936

k100 = 4.36 × 10−3

159

Delphinidin

0.969

k80 = 1.79 × 10−3

9.43

1

0.984

60–100

387

0.958

k60 = 0.94 × 10−3

738

Analysis: HPLC

0.895

k100 = 3.78 × 10−3

184

Loypimai et al. (2016)

Cyanidin

0.923

k80 = 1.03 × 10−3

9.07

1

0.820

60–100

672

0.964

k60 = 0.85 × 10−3

816

0.957

k100 = 3.96 × 10−3

175

Pelargonidin

0.971

k80 = 1.68 × 10−3

10.42

1

0.998

60–100

413

0.973

k60 = 0.73 × 10−3

954

0.955

k100 = 3.81 × 10−3

182

Total

0.880

k80 = 1.04 × 10−3

11.41

1

0.936

60–100

666

0.928

k60 = 0.59 × 10−3

1176

pH 33.0

0.931

k100 = 1.83 × 10−3

374

Cyanidin-3-O-glucoside

0.931

k80 = 1.19 × 10−3

3.89

1

0.937

60–100

583

0.965

k60 = 0.98 × 10−3

714

0.895

k100 = 3.61 × 10−3

192

Cyanidin-3-O-rutinoside

0.953

k80 = 1.71 × 10−3

9.06

1

0.998

60–100

406

0.971

k60 = 0.83 × 10−3

834

0.904

k100 = 6.32 × 10−3

110

Delphinidin

0.925

k80 = 2.44 × 10−3

7.91

1

0.914

60–100

284

0.902

k60 = 1.73 × 10−3

401

0.923

k100 = 5.93 × 10−3

117

Cyanidin

0.913

k80 = 2.17 × 10−3

7.12

1

0.829

60–100

319

0.901

k60 = 1.84 × 10−3

377

0.927

k100 = 7.18 × 10−3

97

Pelargonidin

0.929

k80 = 3.46 × 10−3

5.59

1

0.874

60–100

200

0.903

k60 = 2.87 × 10−3

242

0.886

k100 = 4.12 × 10−3

168

Total

0.907

k80 = 1.26 × 10−3

10.77

1

0.948

60–100

550

0.963

k60 = 0.71 × 10−3

978

pH 4.0

0.948

k100 = 3.16 × 10−3

219

Cyanidin-3-O-glucoside

0.894

k80 = 2.47 × 10−3

5.47

1

0.954

60–100

281

0.943

k60 = 1.31 × 10−3

529

0.925

k100 = 4.84 × 10−3

143

Cyanidin-3-O-rutinoside

0.925

k80 = 2.82 × 10−3

8.58

1

0.991

60–100

246

0.953

k60 = 1.21 × 10−3

573

0.902

k100 = 9.71 × 10−3

71

Loypimai et al. (2016)

Delphinidin

0.941

k80 = 4.75 × 10−3

8.46

1

0.997

60–100

146

0.924

k60 = 2.46 × 10−3

282

0.899

k100 = 7.26 × 10−3

95

Cyanidin

0.929

k80 = 3.12 × 10−3

6.09

1

0.840

60–100

222

0.914

k60 = 2.67 × 10−3

260

0.933

k100 = 9.45 × 10−3

73

Pelargonidin

0.900

k80 = 7.76 × 10−3

6.26

1

0.910

60–100

89

0.865

k60 = 3.46 × 10−3

200

0.876

k100 = 4.87 × 10−3

142

Total

0.951

k80 = 3.67 × 10−3

5.04

1

0.980

60–100

189

0.951

k60 = 2.16 × 10−3

321

pH 5.0

0.897

k100 = 4.73 × 10−3

147

Cyanidin-3-O-glucoside

0.885

k80 = 3.85 × 10−3

7.55

1

0.894

60–100

180

0.922

k60 = 1.41 × 10−3

492

0.884

k100 = 7.33 × 10−3

95

Cyanidin-3-O-rutinoside

0.921

k80 = 5.18 × 10−3

6.83

1

0.969

60–100

134

0.930

k60 = 2.44 × 10−3

284

0.918

k100 = 18.8 × 10−3

37

Delphinidin

0.944

k80 = 7.51 × 10−3

6.98

1

0.871

60–100

92

0.922

k60 = 5.98 × 10−3

116

0.921

k100 = 15.2 × 10−3

46

Cyanidin

0.894

k80 = 5.87 × 10−3

7.61

1

0.896

60–100

118

0.903

k60 = 4.37 × 10−3

159

0.887

k100 = 16.1 × 10−3

43

Pelargonidin

0.911

k80 = 10.4 × 10−3

5.38

1

0.999

60–100

67

0.904

k60 = 6.73 × 10−3

103

0.989

k100 = 1.23 × 10−3

56

Loypimai et al. (2016)

Total

0.908

k80 = 8.59 × 10−3

5.16

1

0.998

60–100

81

0.925

k60 = 5.34 × 10−3

130

pH 2.0

0.950

k100 =  4.30 × 10−3

161

Δ L

3 g extract/1 L 2.0M acetate buffer/pH adjust

0.980

k80 = 3.27 × 10−3

6.43

1

0.947

60–100

212

0.967

k60 = 1.53 × 10−3

453

0.989

k100 = 7.12 × 10−3

97

Δ C

Heat: waterbath/capped 15 ml brown glass vials 0–120 min (7 pts)

0.960

k80 = 5.55 × 10−3

5.52

1

0.954

60–100

125

0.975

k60 = 2.93 × 10−3

237

0.980

k100 = 1.79 × 10−3

387

Δ h

0.973

k80 = 1.51 × 10−3

3.55

1

0.962

60–100

459

0.936

k60 = 1.01 × 10−3

686

pH 3.0

0.920

k100 = 6.64 × 10−3

104

Δ L

0.919

k80 = 5.54 × 10−3

4.93

1

0.929

60–100

125

0.944

k60 = 3.01 × 10−3

230

0.968

k100 = 16.1 × 10−3

43

Δ C

0.986

k80 = 9.82 × 10−3

9.34

1

0.974

60–100

71

Analysis: Hunterlab CIELAB

0.913

k60 = 3.57 × 10−3

194

0.978

k100 = 3.74 × 10−3

185

Δ h

0.977

k80 = 2.37 × 10−3

6.11

1

1.000

60–100

292

0.968

k60 = 1.39 × 10−3

499

pH 4.0

0.963

k100 = 8.65 × 10−3

80

Δ L

L = lightness

0.982

k80 = 7.73 × 10−3

3.76

1

0.904

60–100

90

0.909

k60 = 4.73 × 10−3

147

0.922

k100 = 17.1 × 10−3

41

Δ C

C = chroma

0.913

k80 = 11.3 × 10−3

4.99

1

0.998

60–100

61

0.925

k60 = 7.61 × 10−3

91

0.957

k100 = 2.38 × 10−3

291

Δ h

h = hue angle

0.984

k80 = 2.20 × 10−3

1.56

1

0.968

60–100

315

0.973

k60 = 1.85 × 10−3

375

pH 5.0

0.894

k100 = 9.28 × 10−3

75

Δ L

0.926

k80 = 9.15 × 10−3

0.37

1

0.934

60–100

76

0.903

k60 = 8.75 × 10−3

79

0.908

k100 = 24.8 × 10−3

28

Δ C

0.955

k80 = 15.2 × 10−3

6.73

1

0.999

60–100

46

0.930

k60 = 8.34 × 10−3

83

0.913

k100 = 4.43 × 10−3

156

Loypimai et al. (2016)

Δ h

0.972

k80 = 2.81 × 10−3

3.24

1

0.833

60–100

247

0.940

k60 = 2.60 × 10−3

267

Anthocyanins

Luna-Vital et al., 2017

Purple corn

pH

ASE 300 Extraction:

PCW

2.0

50C/5 min°

k22 = 2.62 × 10−5

2.65 × 10−4

(purple corn water extraction)

2.5

Cell pressure:

k22 = 1.92 × 10−5

3.61 × 10−4

3.0

1500 psi

k22 = 1.24 × 10−5

5.60 × 10−4

3.5

N2 purge/resin filtra­-tion/vacuum eva-poration/freeze dried

k22 = 1.06 × 10−5

1

22

6.54 × 10−4

4.0

k22 = 0.752 × 10−5

9.22 × 10−4

5.0

k22 = 0.451 × 10−5

15.4 × 10−4

6.0

k22 = 0.157 × 10−5

44.2 × 10−4

EA

2.0

EA

k22 = 0.124 × 10−5

55.9 × 10−4

(purple corn prepurified – water extract)

2.5

Further partition/w/water, ethyl acetate

k22 = 0.203 × 10−5

34.1 × 10−4

3.0

k22 = 0.330 × 10−5

21.0 × 10−4

3.5

k22 = 0.578 × 10−5

1

22

12.0 × 10−4

4.0

k22 = 0.692 × 10−5

10.0 × 10−4

5.0

k22 = 1.12 × 10−5

6.22 × 10−4

6.0

k22 = 2.33  × 10−5

2.98 × 10−4

F1

2.0

F1/F2

k22 = 0.159 × 10−5

43.4 × 10−4

(purple corn extract/w/condensed forms)

2.5

Ethyl acetate phase

k22 = 0.259 × 10−5

26.8 × 10−4

3.0

k22 = 0.555 × 10−5

12.5 × 10−4

3.5

k22 = 1.01 × 10−5

1

22

6.84 × 10−4

4.0

k22 = 2.48 × 10−5

2.79 × 10−4

5.0

k22 = 2.09 × 10−5

3.32 × 10−4

6.0

k22 = 5.52 × 10−5

1.26 × 10−4

F2

2.0

k22 = 0.137 × 10−5

50.6 × 10−4

Luna-Vital et al. (2017)

(purple corn extract w/o condensed forms)

2.5

Assay:

k22 = 1.22 × 10−5

5.66 × 10−4

3.0

k22 = 0.431 × 10−5

16.1 × 10−4

3.5

k22 = 0.581 × 10−5

1

22

11.9 × 10−4

4.0

k22 = 0.819 × 10−5

8.46 × 10−4

5.0

k22 = 1.44 × 10−5

4.82 × 10−4

6.0

k22 = 5.63 × 10−5

1.23 × 10−4

Anthocyanins

Purple sweet potato (Ipomoea batatas) extract in soft drink model

Li et al. (2014)

Ascorbic acid:

50 mg/L in 0.1M citric-sodium citric buffer (pH3)

0.996

k90 = 14.4 × 10−4

481

0.996

k85 = 12.1 × 10−4

574

0 mg/L

Beverage model:

0.996

k80 = 11.3 × 10−4

5.8

1

0.983

70–90

613

0.993

k75 = 10.0 × 10−4

692

Per 1000 ml – 86 g sugar,

0.999

k70 = 8.78 × 10−4

789

0.14 g Na-benzoate, 0.18 g

0.997

k90 = 13.8 × 10−4

500

K-sorbate, 1.52 g citric acid,

ascorbic acid

0.997

k85 = 12.3 × 10−4

563

40 mg/L

Heat: waterbath/capped 7 ml tubes/N2 headspace flush/wrapped/w/aluminum foil

0.994

k80 = 10.3 × 10−4

7.5

1

0.997

70–90

672

0.999

k75 = 8.82 × 10−4

786

0.994

k70 = 7.68 × 10−4

902

0.986

k90 = 12.5 × 10−4

557

Li et al. (2014)

Cooled in ice bath to 25°C

0.979

k85 = 11.0 × 10−4

633

120 mg/L

0.971

k80 = 9.67 × 10−4

6.6

1

0.999

70–90

717

0.989

k75 = 8.47 × 10−4

819

Assay: UV-VIS

Absorbance at 527 nm

0.986

k70 = 7.25 × 10−4

956

0.998

k90 = 15.7 × 10−4

441

0.996

k85 = 14.3 × 10−4

485

360 mg/L

0.980

k80 = 13.1 × 10−4

6.1

1

0.968

70–90

530

0.994

k75 = 11.6 × 10−4

597

0.988

k70 = 9.42 × 10−4

736

Purple sweet potato (commercial pigment extract)

Li et al. (2014)

In: pH 3.0 Buffer

50 mg/L in 0.1M citric-sodium citric buffer (pH3)

0.997

k90 = 5.33 × 10−4

1.30 × 103

0.996

k85 = 4.87 × 10−4

1.42 × 103

0.997

k80 = 4.37 × 10−4

3.9

1

0.961

70–90

1.59 × 103

0.994

k75 = 4.05 × 10−4

1.71 × 103

0.994

k70 = 3.93 × 10−4

1.76 × 103

Purple sweet potato extract in soft drink model

Li et al. (2014)

Ascorbic acid (mg/L):

0

50 mg/L in 0.1M citric-sodium citric buffer (pH3)

0.998

k25 = 1.60 × 10−5

1

25

43.2 × 105

40

0.994

k25 = 1.85 × 10−5

1

25

37.5 × 105

120

0.998

k25 = 2.02 × 10−5

1

25

34.3 × 105

360

Beverage model:

0.997

k25 = 2.62 × 10−5

1

25

26.5 × 105

(mg/L∙min)

0

Per 1000 ml – 86 g sugar, 0.14 g Na-benzoate, 0.18 g K-sorbate, 1.52 g citric acid, ascorbic acid

0.998

k4 = 2.63 × 10−4

0

4

1.90 × 105

40

0.999

k4 = 2.77 × 10−4

0

4

1.81 × 105

120

0.999

k4 = 2.85 × 10−4

0

4

1.76 × 105

360

0.999

k4 = 2.81 × 10−4

0

4

1.78 × 105

Anthocyanins

Fernandez-Lopez et al. (2013)

(commercial red pigment extracts)

Elderberry

Heat: waterbath/capped 100 × 14 mm id tubes 0–6 hr (8 pts)

0.97

k90 = 10.0 × 10−4

693

(Sambucus nigra L.)

0.99

k70 = 6.67 × 10−4

10.5

1

0.928

50–90

1040

0.99

k50 = 1.67 × 10−4

(3.7)a

4159

Red cabbage

500 mg/50 ml in 100 mM pH 5.5 cit-phos buffer

0.98

k90 = 16.67 × 10−4

416

(Brassica oleracea L.)

0.98

k70 = 10.0 × 10−4

7.0

1

0.997

50–90

693

0.99

k50 = 5.00 × 10−4

(6.3)a

1386

Hibiscus

Assay: UV-VIS

Absorbance at 535 nm

0.94

k90 = 43.3 × 10−4

160

(Hibiscus sabdariffa L.)

0.97

k70 = 15.0 × 10−4

9.5

1

0.961

50–90

462

0.97

k50 = 8.33 × 10−4

(9.1)a

832

Anthocyanins

Chinese red radish (Oraphanus sativus L.) extract

Liu et al. (2014)

In: Apple juice

0.822

k90 = 8.30 × 10−4

835

Conc: 0.1 g RRA/1 liter juice beverage

0.868

k80 = 5.60 × 10−4

11.42

1

0.995

70–90

1238

0.927

k70 = 3.30 × 10−4

2100

Grape juice

0.971

k90 = 11.5 × 10−4

603

0.926

k80 = 7.90 × 10−4

7.23

1

0.969

70–90

877

0.872

k70 = 6.40 × 10−4

1083

Peach juice

6 ml plastic tubes capped/N2 flush/wrapped in aluminum foil

0.952

k90 = 13.3 × 10−4

521

Liu et al. (2014)

0.940

k80 = 10.2 × 10−4

6.60

1

1.000

70–90

680

0.862

k70 = 7.80 × 10−4

889

Pear juice

0.989

k90 = 8.30 × 10−4

835

0.959

k80 = 6.70 × 10−4

10.38

1

0.935

70–90

1035

0.967

k70 = 3.60 × 10−4

1925

Pomegranate juice

0.908

k90 = 12.7 × 10−4

546

0.942

k80 = 8.60 × 10−4

8.27

1

0.988

70–90

806

0.928

k70 = 6.50 × 10−4

1066

Lemon juice

0.913

k90 = 11.5 × 10−4

603

0.946

k80 = 7.80 × 10−4

7.82

1

0.979

70–90

889

0.912

k70 = 6.10 × 10−4

1136

Chinese red radish (Oraphanus sativus L.) Extract

In: Apple juice

200 ml juice beverage in capped glass vials/N2 flush/aluminum foil wrapped

0.992

k25 = 5.76 × 10−6

1

25

1.21 × 105

Liu et al. (2014)

Grape juice

0.999

k25 = 6.39 × 10−6

1

25

1.09 × 105

Peach juice

0.998

k25 = 5.90 × 10−6

1

25

1.17 × 105

Pear juice

0.972

k25 = 11.6 × 10−6

1

25

59.8 × 105

Pomegranate juice

0.984

k25 = 5.76 × 10−6

1

25

1.20 × 105

Lemon juice

Vials pasteurized 85°C/15 min/cooled/stored 4 weeks

0.999

k25 = 6.25 × 10−6

1

25

1.11 × 105

mg/L∙min

Apple juice

0.997

k4 = 1.69 × 10−4

0

4

2.96 × 105

Grape juice

0.999

k4 = 2.13 × 10−4

0

4

2.35 × 105

Peach juice

Analysis: UV-VIS

0.997

k4 = 2.65 × 10−4

0

4

1.88 × 105

Pear juice

Absorbance: 520 nm

0.997

k4 = 2.38 × 10−4

0

4

2.10 × 105

Pomegranate juice

0.997

k4 = 1.09 × 10−4

0

4

4.59 × 105

Lemon juice

0.997

k4 = 1.34 × 10−4

0

4

3.73 × 105

Strawberry Color

Strawberry color – pH 3.7

k140 = 7.32

0.0947

Rodrigo et al. (2007)

L*∙a*/b*

Wash, cut, homogenize, filter (nat. pH = 3.7)

k130 = 3.75

0.1848

k120 = 2.55

16.2

1

0.991

100–140

0.2718

Heat: 0–120 min

k110 = 1.55

0.4472

Inox tubes (5 mm ID × 100 mm)

k105 = 1.01

0.6863

k100 = 0.857

0.8088

Strawberry Color cont. -

Strawberry color – pH 2.5

Rodrigo et al. (2007)

L*∙a*/b*

k140 = 7.78

0.0891

k130 = 3.26

0.2126

k120 = 1.63

20.7

1

0.977

100–140

0.4252

k110 = 0.781

0.8875

k100 = 0.536

1.2932

Strawberry color – pH 5.0

k140 = 9.91

0.0699

L*∙a*/b*

k120 = 5.57

12.9

1

0.966

100–140

0.1244

k110 = 2.63

0.2636

k100 = 1.98

0.3501

Betalains

Beets

Von Elbe et al. (1974)

Beet puree

Natural pH

0.995

ak116 = 0.01419

a8.7

49

(Betanine)

(electrophoretic separation)

0.996

ak110 = 0.01219

(10 + 2)a

1

0.996

102–116

57

0.950

ak102 = 0.009333

74

Beet juice

(Betanine)

pH 3.0

k100 = 0.079

1

100

8.8

pH 5.0

k100 = 0.024

1

100

29.0

pH 7.0

k100 = 0.135

1

100

5.1

Betanine solution

pH 3.0

k100 = 0.094

1

100

7.4

(citric-phosphate buffer)

pH 4.0

k100 = 0.051

1

100

13.6

pH 5.0

k100 = 0.048

1

14.4

pH 5.0

k75 = 0.0078

a12.6

1

0.976

25–100

89.0

pH 5.0

k50 = 0.0022

10.5

1

0.998

25–75

315

pH 5.0

k25 = 0.00061

14.7

1

0.979

50–100

1136

pH 6.0

k100 = 0.079

1

100

8.8

pH 7.0

k100 = 0.118

1

5.9

pH 7.0

k75 = 0.035

a8.5

1

0.974

25–100

20.0

pH 7.0

k50 = 0.0138

7.1

1

0.992

25–75

50.0

pH 7.0

k25 = 0.0062

10.1

1

0.986

50–100

112

Beet powder

Kopelman and Saguy (1977)

Betanine

Drum-dried

k45 = 3.04 × 10−6

2.28 × 105

Dry powders sealed in glass vials:

(4% MC)

k40 = 2.65 × 10−6

2.62 × 105

k35 = 2.37 × 10−6

5.9

1

0.992

25–45

2.92 × 105

k31 = 1.99 × 10−6

3.48 × 105

k25 = 1.63 × 10−6

4.25 × 105

Air-dried

k45 = 3.49 × 10−6

1.99 × 105

(4% MC)

k40 = 3.28 × 10−6

2.11 × 105

k35 = 2.79 × 10−6

6.6

1

0.949

25–45

2.48 × 105

k31 = 2.10 × 10−6

3.30 × 105

k25 = 1.84 × 10−6

3.77 × 105

Vulgaxanthin

Drum-dried

k45 = 3.01 × 10−6

2.30 × 105

Kopelman and Saguy (1977)

(4% MC)

k40 = 2.68 × 10−6

2.59 × 105

k35 = 2.19 × 10−6

5.6

1

0.935

25–45

3.17 × 105

k31 = 1.80 × 10−6

3.85 × 105

k25 = 1.75 × 10−6

3.96 × 105

Vulgaxanthin cont. –

Air-dried

k45 = 3.23 × 10−6

2.15 × 105

(4% MC)

k40 = 2.90 × 10−6

2.39 × 105

k35 = 2.38 × 10−6

6.5

1

0.986

25–45

2.91 × 105

k31 = 1.96 × 10−6

3.54 × 105

k25 = 1.67 × 10−6

4.15 × 105

Beet powder

Freeze-dried/ground/rehumidified to 0.75 aw

Cohen and Saguy (1983)

Betanine

g H2O/100 g solids

25.8

0.982

k35 = 5.80 × 10−5

1

35

1.20 × 104

Beet:CMC (3:1)

20.1

0.984

k35 = 5.35 × 10−5

1

35

1.30 × 104

Beet:CMC (1:1)

14.9

0.984

k35 = 2.92 × 10−5

1

35

2.37 × 104

Beet:pectin (1:1)

13.6

0.992

k35 = 3.01 × 10−5

1

35

2.30 × 104

Vulgaxanthin-I

g H2O/100 g solids

25.8

0.986

k35 = 4.47 × 10−5

1

35

1.55 × 104

Beet:CMC (3:1)

20.1

0.972

k35 = 4.04 × 10−5

1

35

1.72 × 104

Beet:CMC (1:1)

14.9

0.992

k35 = 2.45 × 10−5

1

35

2.83 × 104

Beet:pectin (1:1)

13.6

0.460

k35 = 0.150 × 10−5

1

35

13.86 × 104

Betalains

Beet juice

Saguy (1979)

Betanine

pH 4.8

k100 = 0.113

6.2

k85.5 = 0.0405

18.2

1

0.991

61.5–100

17.0

k75.5 = 0.0243

29.0

k61.5 = 0.0063

110

pH 5.2

k100 = 0.098

7.1

k85.5 = 0.0374

18.6

1

0.999

61.5–100

19.0

k75.5 = 0.0165

42.0

k61.5 = 0.0056

124

pH 5.8

k100 = 0.0946

7.3

k85.5 = 0.032

19.6

1

0.999

61.5–100

22.0

k75.5 = 0.0146

47.0

k61.5 = 0.0045

154

pH 6.2

k100 = 0.1177

5.9

k85.5 = 0.0405

19.9

1

0.999

61.5–100

17.0

k75.5 = 0.0168

41.0

k61.5 = 0.0 055

126

Vulgaxanthin-I

pH 4.8

k100 = 0.1337

5.2

k85.5 = 0.056

15.4

1

0.997

61.5–100

12.0

k75.5 = 0.0341

20.0

k61.5 = 0.0119

58.0

pH 5.2

k100 = 0.1204

5.8

k85.5 = 0.0497

16.4

1

0.999

61.5–100

14.0

k75.5 = 0.0251

28.0

k61.5 = 0.0095

73.0

pH 5.8

k100 = 0.1146

6.0

k85.5 = 0.0456

16.5

1

0.999

61.5–100

15.0

k75.5 = 0.0234

30.0

k61.5 = 0.0088

79.0

Vulgaxanthin-I

pH 6.2

k100 = 0.1239

5.6

Saguy (1979)

k85.5 = 0.0493

16.9

1

0.999

61.5–100

14.0

k75.5 = 0.0234

30.0

k61.5 = 0.0091

76.0

Beet slices

Partially freeze-dried/rehumidified

Saguy et al. (1980)

Betanine

Moisture:

g H2O/100 g solids

0.03

0.99

k90 = 0.00297

233

0.98

k80 = 0.00214

9.1

1

0.99

70–90

324

0.98

k70 = 0.00142

(13.1)a

488

0.41

0.99

k90 = 0.00397

175

0.98

k80 = 0.00283

10.4

1

0.99

70–90

245

0.99

k70 = 0.00172

(14.9)a

403

0.56

0.99

k90 = 0.00733

95

0.99

k80 = 0.00546

10.8

1

0.97

70–90

127

0.98

k70 = 0.00306

(15.5)a

227

4.26

0.99

k90 = 0.00872

79

0.98

k80 = 0.00659

11.3

1

0.96

70–90

105

0.99

k70 = 0.0035

(16.2)a

198

6.67

0.97

k90 = 0.00984

70

0.98

k80 = 0.0069

12.0

1

0.98

70–90

100

0.98

k70 = 0.0037

(17.4)a

187

Moisture:

g H2O/100 g solids

Vulgaxanthin-I

0.03

0.99

k90 = 0.00081

856

0.99

k80 = 0.00069

5.3

1

0.98

70–90

1005

0.98

k70 = 0.00053

(7.3)a

1308

0.41

0.99

k90 = 0.00194

357

0.98

k80 = 0.00096

10.4

1

0.86

70–90

722

0.99

k70 = 0.00083

(15.0)a

835

0.56

0.99

k90 = 0.00456

152

0.98

k80 = 0.00292

13.0

1

0.99

70–90

237

0.99

k70 = 0.00159

(16.7)a

436

Vulgaxanthin-I

4.26

0.95

k90 = 0.00598

116

Saguy et al. (1980)

0.97

k80 = 0.00336

13.4

1

0.99

70–90

206

0.99

k70 = 0.00207

(18.8)a

335

6.67

0.99

k90 = 0.00718

97

0.99

k80 = 0.00403

13.4

1

0.99

70–90

172

0.98

k70 = 0.00243

(19.2)a

285

Betalains

Betanine → Betalamic acid

pH 5.5/spectro-photometric

k86 = 0.0461

15

Saguy et al. (1978c)

k81 = 0.0296

20.4

1

0.999

60–86

23

Amax = 535 nm

k75 = 0.0175

(20.4)a

40

k60 = 0.0049

141

Betalamic acid → Betalamic acid brown compounds

k86 = 0.00341

203

k81 = 0.00261

20.3

1

0.909

60–86

266

pH 5.5

k75 = 0.00081

(20.7)a

856

Amax = 430 nm

k60 = 0.0039

178

Betanine

0.1M citrate-phosphate buffer: pH 5.0

Huang and von Elbe (1985)

Forward reaction:

2-ml glass vials, in N2 atmos.

ak90 = 6.3 + 0.3

0.11

k85 = 4.5 + 0.2

17.9

1

0.999

65–90

0.15

k75 = 2.1 + 0.1

(17.3)a

0.33

k65 = 1.01 + 0.05

0.69

Reverse reaction:

k90 = 86.7

0.0080

k85 = 85.6

0.66

1

0.999

65–90

0.0081

k75 = 83.3

(0.64)a

0.0083

k65 = 81

0.0086

Betalamic acid

k90 = 1.6 + 0.2

0.43

k85 = 1.1 + 0.1

17.7

1

0.999

65–90

0.63

k75 = 0.53 + 0.06

(18.2)a

1.30

k65 = 0.26 + 0.02

2.70

Cyclodopa-5-0-glycoside

k90 = 0.22 + 0.03

3.20

k85 = 0.15 + 0.02

25.4

1

0.998

65–90

4.60

k75 = 0.048 + 0.005

(29)a

14.00

k65 = 0.017 + 0.002

41.00

Betacyanins

Fernandez-Lopez et al. (2013)

Red beet

0.91

k90 = 5.33 × 10−3

130

(Beta vulgaris L.)

0.91

k70 = 3.17 × 10−3

8.1

1

0.988

50–90

219

0.94

k50 = 1.33 × 10−3

(8.5)a

520

Opuntia fruits (Prickly Pear)

0.89

k90 = 15.2 × 10−3

46

(Opuntia stricta)

0.90

k70 = 5.83 × 10−3

12.9

1

0.998

50–90

119

0.90

k50 = 1.67 × 10−3

(12.8)a

416

Betalains

Pigment extracted and concentrated solution in pH 5.0 phosphate buffer

Attoe and von Elbe (1981)

Cranberries

Betanine

No light

k55 = 0.00368

188

k40 = 0.000642

25.1

1

0.999

25–55

1,080

k25 = 0.0000765

9,060

With light

k55 = 0.00468

148

(400 ft-c)

k40 = 0.00117

19.3

1

0.999

25–55

592

k25 = 0.000238

2,910

Prickly pear fruit

10-ml aqueous solution of extracted pigment

Merin et al. (1987)

Betacyanine

Dilute

k90 = 0.05711

15.7

pseudo-1

0.973

50–90

12.1

k70 = 0.01076

(7.7)a

64.4

k50 = 0.0038

182.4

Concentrated

k90 = 0.0299

21.9

pseudo-1

0.992

50–90

23.2

(× 10)

k70 = 0.0069

(10.7)a

100.4

k50 = 0.0007

990.0

Carotenoids (as pigment)

Blue crab

Himelbloom et al. (1983)

Astaxanthin

XS-water cook color measured by a-value (colorimeter)

k100 = 3.47

0.20

k93.9 = 2.853

0.24

k87.8 = 1.051

22.5

1

0.941

76.6–100

0.66

k82.2 = 0.7674

0.90

k76.6 = 0.5377

1.29

Paprika

Ramakrishnan and Francis (1973)

(Total carotenoids)

0.998

k125 = 0.003786

1

125–150

183

Carotenoids (as pigment)

Salmon

Freeze-dried/rehydrated

Martinez and Labuza (1968)

Astacene

aw = 0

k37 = 1.83 × 10−5

3.79 × 104

aw = 0.11

k37 = 1.67 × 10−5

1

37

4.15 × 104

aw = 0.32

k37 = 0.83 × 10−5

8.35 × 104

aw = 0.40

k37 = 0.17 × 10−5

40.77 × 104

Tomato juice

Heat: 0–7 min

0.896

k130 = 0.024105

28.8

Miki & Akatsu (1970) (from Shi & LeMaguer (2000a))

Lycopene loss

0.885

k127 = 0.020146

34.4

0.884

k124 = 0.017068

40.6

0.882

k121 = 0.014357

48.3

0.683

k118 = 0.01068

21.2

1

0.977

90–130

64.9

0.917

k115 = 0.009462

73.3

0.822

k110 = 0.005581

124.2

0.756

k100 = 0.002107

329.0

0.855

k90 = 0.001647

420.9

Tomato paste

Barreiro et al. (1997)

Overall color change

Color measure:

(Lycopene)

Gardner XL-23

∆E

0.992

k100 = 0.458

10.20

0

0.977

70–100

1.51

9 ml glass vials/70, 80, 90, 100°C/5–90 min

0.991

k70 = 0.122

5.68

L-value: phase 1

0.986

k100 = 0.015

11.50

1

0.996

70–100

46.20

0.988

k70 = 0.0036

192.54

Effect of color parameter on determination of reaction order and Ea

L-value: phase 2

0.960

k100 = 0.00146

5.73

1

0.979

70–100

474.76

0.968

k70 = 0.000762

909.64

a-value

0.996

k100 = 0.0172

9.79

apparent 1

0.983

70–100

40.30

0.979

k70 = 0.005

138.63

b-value

0.958

k100 = 0.00321

20.50

apparent 1

0.952

70–100

215.93

0.902

k70 = 0.00024

2888.11

Tomato paste

a/b

0.964

k100 = 0.011

6.86

apparent 1

0.986

70–100

69.31

Barreiro et al. (1997)

0.962

k70 = 0.0046

150.68

hue angle

0.960

k100 = 0.00924

7.57

apparent 1

0.993

70–100

75.02

0.966

k70 = 0.00384

180.51

SI

0.998

k100 = 0.0144

10.10

apparent 1

0.989

70–100

48.14

0.986

k70 = 0.00406

170.73

Carotenoids

Tomato peel

Kaur et al. (2006)

Lycopene

Peel air-dried

0.9985

k100 = 10.4 × 10−4

666

25 g samples in petri dishes

0.9967

k90 = 8.52 × 10−4

813

Heat: up to 10 hrs.

0.9976

k80 = 7.30 × 10−4

4.4

1

0.995

50–100

949

0.9988

k70 = 5.92 × 10−4

1172

0.9972

k60 = 5.20 × 10−4

1333

Extraction:

0.9985

k50 = 4.06 × 10−4

1706

Color (a × b)

hexane:acetone:alcohol

0.9962

k100 = 5.75 × 10−4

1205

(2:1:1)

0.9954

k90 = 4.62 × 10−4

1502

Analysis: UV-VIS

0.9952

k80 = 3.54 × 10−4

6.9

1

0.993

50–100

1957

A503

0.9942

k70 = 2.84 × 10−4

2438

Hunterlab

0.9915

k60 = 1.84 × 10−4

Lycopene-Color rate constant correlation:

3760

0.9949

k50 = 1.38 × 10−4

0.984

5041

Carotenoids

Tomato pomace

Aw

Raw pomace

Lavelli et al. (2011)

Lycopene

0.17

Washed, cut, screw extractor, dispersed in 1% citric acid, seeds separated, freeze dried

k30 = 11.8 × 10−6

0.587 × 10−5

0.22

k30 = 6.94 × 10−6

0.998 × 10−5

0.32

k30 = 4.86 × 10−6

1

30

1.43 × 10−5

0.56

k30 = 4.17 × 10−6

1.66 × 10−5

0.75

k30 = 3.47 × 10−6

2.00 × 10−5

Tomato pomace

Aw

Raw pomace

β-carotene

0.17

k30 = 11.1 × 10−6

0.624 × 10−5

0.22

k30 = 10.4 × 10−6

0.665 × 10−5

0.32

Analysis: HPLC

k30 = 10.4 × 10−6

1

30

0.665 × 10−5

0.56

k30 = 15.3 × 10−6

0.454 × 10−5

0.75

k30 = 11.8 × 10−6

0.587 × 10−5

Rutin

0.75

Raw pomace

k30 = 4.58 × 10−6

1.51 × 10−5

Chlorogenic acid

0.75

k30 = 1.94 × 10−6

3.56 × 10−5

Carotenoids

Tomato pomace

Aw

Heated pomace

Lavelli et al. (2011)

Lycopene

0.17

Washed, cut, heat (3 h/100°C), screw extractor, dispersed in 1% citric acid, seeds separated, freeze dried.

k30 = 13.2 × 10−6

0.525 × 10−5

0.22

k30 = 6.94 × 10−6

0.998 × 10−5

0.32

k30 = 6.25 × 10−6

1

30

1.11 × 10−5

0.56

k30 = 3.47 × 10−6

2.00 × 10−5

0.75

k30 = 3.47 × 10−6

2.00 × 10−5

Tomato pomace

Aw

Heated pomace

Lavelli et al. (2011)

β-carotene

0.17

k30 = 9.03 × 10−6

0.768 × 10−5

0.22

k30 = 9.72 × 10−6

0.713 × 10−5

0.32

Analysis: HPLC

k30 = 8.33 × 10−6

1

30

0.832 × 10−5

0.56

k30 = 6.25 × 10−6

1.11 × 10−5

0.75

k30 = 6.94 × 10−6

0.998 × 10−5

Heated pomace

Rutin

0.75

k30 = 1.18 × 10−6

5.87 × 10−5

Chlorogenic acid

0.75

k30 = 0.556 × 10−6

12.5 × 10−5

Tomato pulp

Heat: 0–3 hr

Cole & Kapur (1957b) (from Shi & LeMaguer (2000b)

Lycopene loss

Dark/CO2

0.633

k100 = 0.0002635

1

100

2631.0

Dark/O2

0.986

k100 = 0.00198997

1

100

348.3

Light/CO2

0.978

k100 = 0.00065683

1

100

1055.0

Light/O2

0.990

k100 = 0.00223662

1

100

309.9

Lycopene model

Cole & Kapur (1957a) (from Shi & LeMaguer (2000))

(Hexane/light petroleum solution)

Lycopene

0.956

k100 = 0.00234117

1

100

296.1

0.950

k65 = 0.0016133

1

65

429.6

Lycopene + Cu-stearate

0.985

k100 = 0.0129717

1

100

53.4

0.999

k65 = 0.0050983

1

65

136.0

Carotenoids (color only)

Tomato color

Wash, cut, homogenize, filter, concentrated from 4 to 20Brix under vacuum

k140 = 8.63

0.0803

Rodrigo et al. (2007)

L*∙a*/b*

k130 = 3.27

0.2120

k125 = 1.87

0.3707

Heat: 0–120 min

k120 = 1.29

30.1

1

0.990

100–140

0.5373

Inox tubes (5 mm ID × 100 mm)–

k115 = 0.784

0.8841

k110 = 0.344

2.0150

(Pressure experiments showed little change in color (0–700 MPa/65°C/60 min)

k105 = 0.268

2.5864

k100 = 0.189

3.6674

Carotenoids

Tomato puree (fresh)

Heat:

Shi et al. (2003)

Lycopene

90–150°C, up to 6 hr

all-trans-lycopene → mono & poly-cis isomers

k150 = 0.0146383

47.4

k120 = 0.0071483

7.98

1

0.959

90–150

97.0

k110 = 0.00425

a(0.982)

163.1

k90 = 0.0032233

215.1

cis isomers → oxidized by-products

k150 = 0.014455

48.0

k120 = 0.005965

11.80

1

0.967

90–150

116.2

k110 = 0.00263333

a(0.891)

263.2

k90 = 0.001521667

455.5

Carotenoids

Shi et al. (2003)

Tomato puree cont. –

k150 = 0.0257833

26.9

all-trans-lycopene → oxidized by-products

k120 = 0.01461667

7.00

1

0.996

90–150

47.4

k110 = 0.0113833

a(1.507)

60.9

k90 = 0.0064833

106.9

Tomato puree (fresh)

Storage:

25°C/1–6 days

Light: 400 µmol/m−2 s−1

all-trans-lycopene → mono & poly-cis isomers

k25 = 0.000057639

1

25

12025.7

cis isomers → oxidized by-products

k25 = 0.0027076

1

25

256.0

all-trans-lycopene → oxidized by-products

k25 = 0.00282639

1

25

245.2

Light: 500 µmol/m−2 s−1

all-trans-lycopene → mono & poly-cis isomers

k25 = 0.000083333

1

25

8317.8

cis isomers → oxidized by-products

&ndas