Limitations of the Log-Logistic Model for the Analysis of Sigmoidal Microbial Inactivation Data for High-Pressure Processing (HPP) |
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Authors: | Vinicio Serment-Moreno J Antonio Torres Claudio Fuentes José Guadalupe Ríos-Alejandro Gustavo Barbosa-Cánovas Jorge Welti-Chanes |
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Affiliation: | 1.Escuela de Ingeniería y Ciencias,Tecnológico de Monterrey Centro de Biotecnología FEMSA,Monterrey,México;2.Food Process Engineering Group, Department of Food Science and Technology,Oregon State University,Corvallis,USA;3.Department of Statistics,Oregon State University,Corvallis,USA;4.Center for Nonthermal Processing of Food,Washington State University,Pullman,USA |
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Abstract: | This study identified limitations of the log-logistic model to evaluate microbial inactivation kinetics by high-pressure processing (HPP) including the need to assign a numerical value to “approximate” the undefined expression log10 t?=?0 and the misinterpretation of its parameters due to a derivation flaw. Peer-reviewed HPP microbial inactivation data were adjusted to a sigmoidal equation (SIG), the original “vitalistic” log-logistic models (VIT-1, VIT-6), and two functions that did not follow the original derivation procedure (LOG-1, LOG-6). Their goodness of fit was determined utilizing the coefficient of determination (R 2 ) and Akaike information criteria (AIC). The shape of the survival curve greatly influenced the performance of log-logistic models. VIT and LOG models performed equally when the kinetic curve showed a sigmoidal shape, and the numerical values of their parameter estimates were identical regardless of the log10 (t?=?0) approximation. Conversely, most concave curves yielded inaccurate parameter estimates for all models. LOG-1 and VIT-1 performed best when log10 t?=?0 was ?1 or ?2, whereas LOG-6 and VIT-6 yielded best results for values of ?3 to ?9. SIG ranked last for most datasets but occasionally performed best (Akaike weight factor wAICi ?=?0.40–1.00) when microbial survival counts showed clear sigmoidal shapes. VIT models consistently displayed R 2 ?≥?0.98, and their parameters can be interpreted within a “biological” context using the corrected derivation shown for LOG models. However, concave curves are more frequently observed for HPP microbial inactivation, and fitting the experimental data to log-logistic models deems unnecessary. |
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