For many microorganisms, small pH variations in the pH range ~6 to ~7 have very little or no effect on population kinetics. In more acidic foods, however, pH per se can greatly in uence microbial kinetics but can also accentuate the effect of other added preservative compounds. The pH of solid foods is often determined by homogenizing 10 g of a sample with 10 to 20 ml of distilled water and measuring the pH of the suspension using a standard combined electrode.
A3.1.5 Added Preservatives Including Organic Acids, Nitrate, and Spices
High concentrations of organic acids occur naturally in some foods and various organic acids including acetic acid, ascorbic acid, benzoic acid, citric acid, lactic acid, and sorbic acid are frequently added to foods. Organic acids can inhibit growth of microorganisms markedly and secondary models to predict their inhibitory effect are frequently needed. As for NaCl the secondary models must take into account the concentration of organic acids in the water phase of products. In addition, secondary models may need to describe the combined effect of organic acids and other environmental parameters particularly the pH.
In solution, organic acids exist either as the dissociated (ionized) or undissociated species. The Henderson-Hasselbalch equation (Equation A3.8) relates the proportion of undissociated and dissociated forms of organic acid to pH and p^a according to the following expression:
where [HA] is the concentration of undissociated form of the acid, [A-] the concentration of dissociated (ionized) form of the acid, and pKa is the pH at which the concentrations of the two forms are equal.
While both the dissociated and the undissociated forms of organic acids have inhibitory effects on bacterial growth the undissociated form is more inhibitory, usually by two to three orders of magnitude, than the dissociated form (Eklund, 1989).
Cross-multiplying and rearranging Equation A3.8 to solve for [HA] gives:
where [LAC] is the total lactic acid concentration and all other terms are as previously de ned.
As the concentration of an undissociated acid increases the growth rate of microorganisms decreases, eventually ceasing completely at a level described as the MIC. This behavior, and its dependence on the interaction of pH and total organic acid concentration, is included explicitly in several secondary models (Augustin and Carlier, 2000a; Presser et al., 1997).
Simple enzyme kits are available to determine several of the organic acids that are important in foods. Simultaneous determination of a range of organic acids is possible by HPLC analysis and is often an appropriate method to use (Dalgaard and Jergensen, 2000; Pecina et al., 1984).
Nitrite can be added to some types of meat products and its concentration in the water phase of products must be taken into account when secondary predictive models for these products are developed. Colorimetric methods are available to measure the concentration of nitrite in foods (Anon., 1995b; Karl, 1992).
Spices and herbs can have substantial antimicrobial activity and appropriate terms may need to be included in secondary models (Koutsoumanis et al., 1999; Skandamis and Nychas, 2000). The concentration of active antimicrobial components in spices, herbs, and essential oils can vary substantially as a function, e.g., of geographical region and season (Nychas and Tassou, 2000; Sofos et al., 1998). Therefore, the development of accurate secondary predictive models most likely will have to rely on the concentration of their active antimicrobial components. Recently, Lambert et al. (2001) showed the antimicrobial effect of the oregano essential oil quantitatively corresponded to the effect of its two active components, i.e., thymol and carvacrol. To quantitatively determine active components in spices, herbs, and essential oils appropriate extracts can be analyzed by GC/MS techniques (Cosentino et al., 1999; Cowan, 1999).
It has long been known that high concentrations of smoke components have strong antimicrobial activity (Shewan, 1949). Today many meat and seafood products are smoked but typically less intensively than some decades ago. However, even moderate concentrations of smoke components can in uence growth rates, growth limits, and rates of death/inactivation of microorganisms in foods (Leroi et al., 2000; Leroi and Joffraud, 2000; Ross et al., 2000b; Suñen, 1998; Thurette et al., 1998). Thus, to obtain accurate prediction of microbial kinetics in smoked foods it is important to include terms for the effect of smoke components in secondary models. Phenols are important antimicrobials in wood smoke, or in liquid smokes, and a few secondary models include the total phenol concentration as an environmental parameter (Augustin and Carlier 2000a,b; Giménez and Dalgaard, in press; Membré et al., 1997).
Classical colorimetric methods can be used to determine the total concentration of phenols in smoked foods. These methods rely on formation of colored complexes, e.g., between phenols and Gibb's reagent (2,6-dichloroquinone-4-chloroimide) or 4-aminoantipyrine (Leroi et al., 1998; Tucker, 1942). The total phenol concentration is a crude measure of how intensely foods have been smoked. By using GC/MS techniques more detailed information about specific smoke components can be obtained (Guillén and Errecalde, 2002; McGill et al., 1985; Tóth and Potthast, 1984). In the future, secondary models may be developed to include the effect of specific phenols, other specific smoke components, and possibly their interaction with NaCl. During the smoking of foods, phenols and other smoke components are mainly deposited in the outer 0.5 cm of the product (Chan et al., 1975). Modeling the effect of the spatial distribution in foods is another challenge.
The environmental parameters discussed above include those that are of major importance in traditional methods of food preservation. Many modern methods of food preservation also rely on combinations of these environmental parameters. However, the effect of a few well-known and several emerging food processing technologies relies on the antimicrobial effect of other environmental parameters, e.g., bacteriocins, gamma irradiation, high electric field pulses, high pressure, and UV light. Secondary models for the effect of some of these environmental parameters have been developed but will not be discussed here in detail. Other environmental parameters related to food structure and to the effect of microbial metabolism on changes in environmental parameters are discussed in Chapter 5 whereas the effect of time-varying environmental parameters is discussed in Chapter 7.
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