A model for the combined effects of temperature and salt concentration on growth rate of food spoilage molds.

We modeled mold growth on a solid culture medium at various temperatures and NaCl concentrations by using five common food spoilage molds (Penicillium roqueforti, Trichoderma harzianum, Paecilomyces variotii, Aspergillus niger, and Emericella nidulans). For the description of the growth rate (expressed as the increase in colony diameter per unit of time) as a function of temperature and NaCl concentration, a six-parameter model has been developed. The model combines either the Rosso-type or the Ratkowsky-type temperature dependence with the NaCl concentration dependence derived from the relationship between the growth rate and square root of (1 - water activity), as proposed by Gibson and coworkers (A. M. Gibson, J. Baranyi, J. I. Pitt, M. J. Eyles, and T. A. Roberts, Int. J. Food Microbiol. 23:419-431, 1994). The model will be of use to food microbiologists whose aim is to predict the likelihood of fungal spoilage.

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

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.

Modeling of bacterial growth with shifts in temperature.

The temperature of chilled foods is an important variable for the shelf life of a product in a production and distribution chain. To predict the number of organisms as a function of temperature and time, it is essential to model the growth as a function of temperature. The temperature is often not constant in various stages of distribution. The objective of this research was to determine the effect of shifts in temperature. The suitability and usefulness of several models to describe the growth of Lactobacillus plantarum with fluctuating temperatures was evaluated. It can be assumed that temperature shifts within the lag phase can be handled by adding relative parts of the lag time to be completed and that temperature shifts within the exponential phase result in no lag phase. With these assumptions, the kinetic behavior of temperature shift experiments was reasonably well predicted, and this hypothesis was accepted statistically in 73% of the cases. Only shifts of temperature around the minimum temperature for growth showed very large deviations from the model prediction. The best results were obtained with the assumption that a temperature shift (within the lag phase as well as within the exponential phase) results in an additional lag phase. This hypothesis was accepted statistically in 93% of the cases. The length of the additional lag phase is one-fourth of the lag time normally found at the temperature after the shift.