Modeling the Growth Rate of Listeria Monocytogenes Using Absorbance Measurements and Calibration Curves
ABSTRACT The influence of environmental conditions (temperature and pH) on the relationship between growth data expressed by absorbance (ABS) and data transformed to cell count (CC) measurements was studied, using calibration curves for predicting Listeria monocytogenes growth rate. With this aim, 19 calibration curves at different stress conditions were performed. A shift in the calibration curves was observed for the most stringent conditions, which affected cell viability. Subsequently, a Baranyi model was fitted to ABS and CC data to obtain growth rate (GRABS and GRCC) and a linear regression was performed. Absorbance was found to be a reliable technique for measuring microbial growth, as a strong relationship between GRABS and GRCC (R2= 0.9717) was observed. Furthermore, 2 different response surface models were developed to link GRABS and GRCC data with temperature, citric acid, and ascorbic acid. The goodness of fit of both ABS and CC models to the data was observed (RMSE = 0.0223 and 0.0221; SEP [%]= 29 and 25, respectively). Mathematical validation was carried out by calculating bias and accuracy factors, providing reasonably acceptable values for both absorbance and cell count models (Bf= 1.11 and 1.09, Af= 1.44 and 1.41, respectively). Predictions for GRCC were compared to data taken from Growth Predictor software at different temperatures and pH. Response surface model predictions showed that a suitable combination of preservative factors can inhibit L. monocytogenes growth. These results highlight accurate predictions of growth parameters of L. monocytogenes.
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ABSTRACT: Stochastic models can be useful to predict the risk of foodborne illness. The presence of Bacillus cereus in liquid egg can pose a serious hazard to the food industry, since a mild heat treatment cannot guarantee its complete inactivation. However, most of the information available in the scientific literature is deterministic, including growth of B. cereus. In this paper, a stochastic approach to evaluate growth of B. cereus cells influenced by different stresses (presence of nisin and lysozyme separately or in combination) was performed, using an individual-based approach of growth through OD measurements. Lag phase duration was derived from the growth curves obtained. From results obtained, histograms of the lag phase were generated and distributions were fitted. Normal and Weibull distributions were ranked as the bestfit distributions in experiments performed at 25 °C. At 16 °C, lag values (obtained in presence of combinations of both antimicrobials) were also fitted by a Gamma distribution. Predictions were compared with growth curves obtained in liquid egg exposed to mild heat, nisin and/or lysozyme to assess their validity.Food Microbiology 04/2011; 28(2):305-10. · 3.41 Impact Factor