By Micha Peleg
Offering a unique view of the quantitative modeling of microbial development and inactivation styles in meals, 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 protection, water disinfection, microbial dose reaction curves, interpretation of abnormal count number files, and the way to estimate the frequencies of destiny outbursts. He focuses totally on the mathematical different types of the proposed replacement types and at the intent for his or her advent as substitutes to these presently in use. The ebook offers examples of ways a number of the equipment should be applied to persist with or expect microbial progress and inactivation styles, in genuine time, with loose courses published on the net, written in MS Excel?, and examples of the way microbial survival parameters might be derived at once from non-isothermal inactivation facts after which used to foretell the efficacy of alternative non-isothermal warmth remedies. that includes a number of illustrations, equations, tables, and figures, the booklet elucidates a brand new technique that resolves a number of extraordinary matters in microbial modeling and gets rid of inconsistencies usually present in present tools.
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Additional info for Advanced Quantitative Microbiology for Foods and Biosystems: Models for Predicting Growth and Inactivation (Contemporary Food Science)
Appl. M. , 2004, Int. J. , 95, 205–218, respectively. Again, it is easy to see that when T << Tc, exp [k(T – Tc)] << 1 and b(T) ≈ 0. However, when T >> Tc, exp (T – Tc) >> 1 and thus b(T) ≈ loge exp [k(T – Tc)] = k(T – Tc). , when (T >> Tc). 23. This model would be just as appropriate if the isothermal inactivation had truly followed a first-order kinetics, in which case k(T) would replace b(T) in the model’s equation. Unlike in the traditional secondary models, and because b(T) is not expressed as a logarithmic transform, the temperatures of intensive lethality receive an appropriate weight relative to that of the low temperatures, where hardly any or no inactivation occurs.
Yet, if α and β are determined by regression using the transformed logeS(t) or log10S(t) vs. t rather than the original S(t) vs. t data, then whenever the experimental survival data have a scatter, the location and shape factors thus calculated will only be an approximation of the distribution’s real parameters. This is because the relative weight given to the different parts of the distribution and, consequently, to the deviations is distorted by the logarithmic transformation. 16, the isothermal survival model can also be considered as an empirical power law model.
However, this is certainly not the case here simply because the time, on the order of minutes or even less, is too short for the spores to germinate and multiply. Therefore, the rise in the observed number has been attributed to dormant spores’ activation by their exposure to the high temperature. If these dormant spores had not been activated, they would not germinate during the recovery stage and would remain uncounted. This explains why, after a short heat treatment even at an otherwise lethal temperature, the number of spores appears to have grown.
Advanced Quantitative Microbiology for Foods and Biosystems: Models for Predicting Growth and Inactivation (Contemporary Food Science) by Micha Peleg