Development and Validation of a Predictive Model for Listeria monocytogenes Scott A as a Function of Temperature, pH, and Commercial Mixture ofPotassium Lactate and Sodium Diacetate

Abstract

The objective of this study was to develop and validate secondary models that can predict growth parameters of L. monocytogenes Scott A as a function of concentrations (0-3%) of a commercial potassium lactate (PL) and sodium diacetate (SDA) mixture, pH (5.5-7.0), and temperature (4-37 o C). A total of 120 growth curves were fitted to the Baranyi primary model that directly estimates lag time (LT) and specific growth rate (SGR). The effects of the variables on L. monocytogenes Scott A growth kinetics were modeled by response surface analysis using quadratic and cubic polynomial models of the natural logarithm transformation of both LT and SGR. Model performance was evaluated with dependent data and independent data using the prediction bias (B f ) and accuracy factors (A f ) as well as the acceptable prediction zone method [percentage of relative errors (%RE)]. Comparison of predicted versus observed values of SGR indicated that the cubic model fits better than the quadratic model, particularly at 4 and 10 o C. The B f and A f for independent SGR were 1.00 and 1.08 for the cubic model and 1.08 and 1.16 for the quadratic model, respectively. For cubic and quadratic models, the %REs for the independent SGR data were 92.6 and 85.7, respectively. Both quadratic and cubic polynomial models for SGR and LT provided acceptable predictions of L. monocytogenes Scott A growth in the matrix of conditions described in the present study. Model performance can be more accurately evaluated with B f and A f and % RE together.Predictive growth modeling of L. monocytogenes has received a lot of attention [2,9,16] because of listeriosis outbreaks, predominantly associated with ready-to-eat (RTE) food. If models can be developed to give reliable predictions, considerable savings can be made in costs associated with laboratory challenge testing of food products. Furthermore, these models can be utilized by the food industry and risk assessors to control the safety and quality of food and to quantify the effects of environmental factors on the behavior of the pathogen.An important step after developing a model is to evaluate the performance of the model by comparing its predictions against observed data. Performance evaluation can be carried out on the basis of the data used in model development to determine if the model sufficiently describes the experimental data (internal validation) [24]. External validation uses new data that were obtained from growth data reported in the literature. However, the problem with literature data is that the comparisons are often confounded by more than one experimental variable being different than the data used in model development. In addition, independent data that were not used in model development but were inside model boundaries (interpolation) can be used for internal validation [17]. The adequacy of the model to predict data should be assessed both graphically using plots of prediction errors as well as by using mathematical and/or statistical indices that quantify prediction bias and accuracy ...