Evaluation of data transformations and validation of a model for the effect of temperature on bacterial growth

Abstract

The temperature of chilled foods is an important variable for controlling microbial growth in a production and distribution chain. Therefore, it is essential to model growth as a function of temperature in order to predict the number of organisms as a function of temperature and time. This article deals with the correct variance-stabilizing transformation of the growth parameters A (asymptotic level), mu (specific growth rate), and lambda (lag time). This is of importance for the regression analysis of the data. A previously gathered data set and model for the effect of temperature on the growth of Lactobacillus plantarum (M. H. Zwietering, J. T. de Koos, B. E. Hasenack, J. C. de Wit, and K. van 't Riet, Appl. Environ. Microbiol. 57:1094-1101, 1991) is extended with new data. With the total data set (original and new data), a variance-stabilizing transformation is selected in order to determine which transformation should precede fitting. No transformation for the asymptote data, a square root for the growth rate, and a logarithmic transformation for the lag time were found to be appropriate. After these transformations, no significant correlation was found between the variance and the magnitude of the variable. Model corrections were made and model parameters were estimated by using the original data. With the new data, the models were validated by comparing the lack of fit of the models with the measurement error, using an F test. The predictions of the models for mu and lambda were adequate. The model for A showed a systematic deviation, and therefore a new model for A is proposed.