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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 LactobaciUlus 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.

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), F. (specific growth rate), and A (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 LactobaciUus plantarum (M. H. Zwietering, J. T. de

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 ͌(1 ؊ water activity), as proposed by Gibson and coworkers (A. M.

Improvement of the accuracy of predictive models for proteolytic Clostridium botulinum stability of current and future process cheese compositions was studied. The results of a central composite design were analyzed the effects of pH (5.45 to 5.9), sodium chloride (1.1 to 2.7%, wt/wt), combinations of citrates and phosphates as emulsifying salts (1.5 to 2%, wt/wt) and temperature (15 to 30°C). Supplemental data enabled assessment of the differences in lactate in moisture (1.0 to 2.6%) originating from the cheese raw materials, variations in moisture (50 to 69%) and the percentage of total fat (0.1 t0 41 %). The time to a 100-fold increase in cell numbers, t100, was modeled using the SAS-LIFEREG procedure, which can compensate for interval-censored and no-growth values. Two quadratic response models were derived to predict the growth of C. botulinum. The Central Composite Model uses significant (P < 0.01) estimates of the combined effects of pH, total salts (NaCl plus emulsifying salts) in moisture (SIM), citrates as a percentage of total salts (CTS), and temperature. The Extended Total Model uses an additional parameter, lactate in moisture (LACM). The role of fat content was insignificant. Moisture content, which is frequently used, appears to be an unreliable predictor of botulinum stability when fat dry basis (FDB) varies. Both models are capable of predicting the observed stability of compositions derived from the literature. They can complement the historic fully quadratic model of Tanaka, they can be used to assess process cheese safety in relation to distribution and/or storage conditions, and they can accelerate product design, minimizing the use of time-consuming product-challenge tests.