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Searching for New Mathematical Growth Model Approaches for Listeria monocytogenes
Author(s) -
Valero A.,
Hervás C.,
GarcíaGimeno R.M.,
Zurera G.
Publication year - 2006
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1750-3841.2006.00208.x
Subject(s) - generalization , mean squared error , gompertz function , goodness of fit , mathematics , sigmoid function , citric acid , ascorbic acid , function (biology) , listeria monocytogenes , mathematical model , biological system , computer science , statistics , artificial neural network , artificial intelligence , chemistry , food science , biology , mathematical analysis , genetics , evolutionary biology , bacteria
ABSTRACT:  Different secondary modeling approaches for the estimation of Listeria monocytogenes growth rate as a function of temperature (4 to 30 °C), citric acid (0% to 0.4% w/v), and ascorbic acid (0% to 0.4% w/v) are presented. Response surface (RS) and square‐root (SR) models are proposed together with different artificial neural networks (ANN) based on product functions units (PU), sigmoidal functions units (SU), and a novel approach based on the use of hybrid functions units (PSU), which results from a combination of PU and SU. In this study, a significantly better goodness‐of‐fit was obtained in the case of the ANN models presented, reflected by the lower SEP values obtained (< 24.23 for both training and generalization datasets). Among these models, the SU model provided the best generalization capacity, displaying lower RMSE and SEP values, with fewer parameters compared to the PU and PSU models. The bias factor (B f ) and accuracy factor (A f ) of the mathematical validation dataset were above 1 in all cases, providing fail‐safe predictions. The balance between generalization properties and the ease of use is the main consideration when applying secondary modeling approaches to achieve accurate predictions about the behavior of microorganisms.

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