Journal
INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY
Volume 73, Issue 2-3, Pages 119-125Publisher
ELSEVIER
DOI: 10.1016/S0168-1605(01)00643-2
Keywords
predictive modelling; nonlinear logistic regression; binomial error; growth/no growth; Listeria monocytogenes; Escherichia coli
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A new technique, nonlinear logistic regression, is described for modelling binomially distributed data, i.e., presence/absence data where growth is either observed or not observed, for applications in predictive food microbiology. Some examples of the successful use of this technique are presented, where the controlling factors are temperature, water activity, pH and the concentration of lactic acid, a weakly dissociating organic acid. Generally speaking, good-fitting models were obtained, as evidenced using various performance measures and goodness-of-fit statistics. As may be expected with a new statistical technique, some problems were encountered with the implementation of the modelling approach and these are discussed. (C) 2002 Elsevier Science B.V All rights reserved.
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