By Micha Peleg
Featuring a singular view of the quantitative modeling of microbial development and inactivation styles in nutrition, water, and biosystems, complicated Quantitative Microbiology for meals and Biosystems: types for Predicting progress and Inactivation describes new types for estimating microbial development and survival. the writer covers conventional and substitute versions, thermal and non-thermal maintenance, water disinfection, microbial dose reaction curves, interpretation of abnormal count number documents, and the way to estimate the frequencies of destiny outbursts. He focuses totally on the mathematical types of the proposed replacement types and at the cause for his or her creation as substitutes to these at the moment in use. The ebook presents examples of ways a few of the equipment might be applied to stick to or expect microbial progress and inactivation styles, in genuine time, with loose courses published on the internet, written in MS Excel?, and examples of ways microbial survival parameters could be derived without delay from non-isothermal inactivation info after which used to foretell the efficacy of different non-isothermal warmth remedies. that includes a variety of illustrations, equations, tables, and figures, the booklet elucidates a brand new process that resolves a number of striking concerns in microbial modeling and gets rid of inconsistencies frequently present in present equipment.
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Additional info for Advanced Quantitative Microbiology for Foods and Biosystems: Models for Predicting Growth and Inactivation (Contemporary Food Science)
We will refer to this kind of sigmoid curve as type B. © 2006 by Taylor & Francis Group, LLC Isothermal Microbial Heat Inactivation 35 Type A E. coli in apple juice Type B L. 17 Examples of experimental isothermal sigmoid survival curves of types A and B fitted with empirical models. The original experimental data are from Vassoni Penna and Morales, 2002, and Ingham and Vljas, 1998, respectively. , 2003, Crit. Rev. , 43, 645–658. ) One can argue, though, that if the heating had continued for a much longer time, accumulated damage in the sturdy survivors will eventually cause their demise, too; in this case, the survival curve’s concavity direction will change once more and becomes downward again.
However, when it comes to real microbial populations, one can expect that all three distributions’ characteristics — namely, the resistance spectrum’s mean or mode, variance, and coefficient of skewness — might be affected and, at least theoretically, need not vary in unison. (It is very unlikely that parameters based on the higher moments of the inactivation times’ distribution, like kurtosis, would be ever needed to characterize an organism’s heat resistance. ) Translation of a microbial survival curve obtained under nonisothermal conditions into an underlying distribution parameter is totally unnecessary and if done may yield misleading results, as will be shown in the next chapter.
With a few extreme exceptions, however, © 2006 by Taylor & Francis Group, LLC 16 Advanced Quantitative Microbiology for Foods and Biosystems whether a given microbial population is really a mixture or not cannot be decided on the basis of the survival curve’s shape alone. If the issue is of practical significance, it should be resolved by independent tests in which the existence of the presumed subpopulations could be proven by other means. All the preceding discussion is based on the ubiquitous observation that individual cells and spores are inactivated at different times, even under close to true isothermal conditions.