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, including the variability in extent and probability of microbial growth, are useful for estimating the risk of foodborne illness (i.e. Nauta, 2000). Risk assessment typically has to embrace all sources of variability. In this paper, a stochastic approach to evaluate growth of heat damaged Listeria monocytogenes cells influenced by different stresses (pH and presence of eugenol) was performed, using an individual-based approach of growth through OD measurements. Both the lag phase duration and the "work to be done" (h(0) parameter) were derived from the growth curves obtained. From results obtained histograms of the lag phase were generated and distributions were fitted. Histograms showed a shift to longer lag phases and an increase in variability with high stress levels. Using the distributions fitted, predictions of time to unacceptable growth (10(2) cfu/g) of L. monocytogenes were established by Monte Carlo simulation and they were compared with results from statistical methods. It was evidenced that both methods (Monte Carlo and regression analysis) gave a good indication of the probability of a certain level of growth other than the average. Tornado plots were obtained to establish a sensitivity analysis of the influence of the conditions tested (heat, pH, eugenol) applied to the microorganism and their combinations.Food Microbiology 06/2010; 27(4):468-75. DOI:10.1016/j.fm.2009.12.002 · 3.33 Impact Factor
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ABSTRACT: The presence of Escherichia coli in contaminated food products is commonly attributed to faecal contamination when they are improperly handled and/or when inactivation treatments fail. Adaptation of E. coli at low pH and a(w) levels can vary at different temperatures depending on the serotype, thus more detailed studies are needed. In this work, a screening to assess the growth of four pathogenic serotypes of E. coli (O55:H6; O59:H21; O158:H23 and O157:H7) was performed. Subsequently, boundary models were elaborated with the fastest serotype selected at different temperatures (8, 12 and 16 degrees C), and inoculum levels (2, 3 and 4log cfu/mL) as function of pH (7.00-5.00) and a(w) (0.999-0.960). Finally, the growth kinetics of E. coli was described in the conditions that allowed growth. Results obtained showed that the serotypes O157:H7 and O59:H21 did not grow at more stringent conditions (8 degrees C; pH 5.50), while the E. coli O158:H23 was the best adapted, resulting in faster growth. The logistic regression models presented a good adjustment to data observed since more than 96.7% of cases were correctly classified. The growth interface was shifted to more limited conditions as the inoculum size was higher. Detection times (t(d), h) and their variability were higher at low levels of the environmental factors studied. This work provides insight on the growth kinetics of E. coli at various environmental conditions.Food Microbiology 09/2010; 27(6):819-28. DOI:10.1016/j.fm.2010.04.016 · 3.33 Impact Factor