Some examples of, and some problems with, the use of nonlinear logistic regression in predictive food microbiology.
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.
Ratkowsky
D A
DA
School of Agricultural Science, University of Tasmania, Hobart, Australia. D.Ratkowsky@utas.edu.au
D004926:Escherichia coli / Q000254:growth & development*; D005516:Food Microbiology; D006863:Hydrogen-Ion Concentration; D008089:Listeria monocytogenes / Q000254:growth & development*; D016015:Logistic Models; D008954:Models, Biological; D012044:Regression Analysis; D013696:Temperature; D014867:Water
D014867:Water