Simultaneous construction of PCR-DGGE-based predictive models ofListeria monocytogenesandVibrio parahaemolyticuson cooked shrimps

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The aim of this study was to simultaneously construct PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps at 4 and 10°C. Calibration curves were established to correlate peak density of DGGE bands with microbial counts. Microbial counts derived from PCR-DGGE and plate methods were fitted by Baranyi model to obtain molecular and traditional predictive models. For L. monocytogenes, growing at 4 and 10°C, molecular predictive models were constructed. It showed good evaluations of correlation coefficients (R2 > 0·92), bias factors (Bf) and accuracy factors (Af) (1·0 ≤ BfAf ≤ 1·1). Moreover, no significant difference was found between molecular and traditional predictive models when analysed on lag phase (λ), maximum growth rate (μmax) and growth data (P > 0·05). But for V. parahaemolyticus, inactivated at 4 and 10°C, molecular models show significant difference when compared with traditional models. Taken together, these results suggest that PCR-DGGE based on DNA can be used to construct growth models, but it is inappropriate for inactivation models yet. This is the first report of developing PCR-DGGE to simultaneously construct multiple molecular models.Significance and Impact of the StudyIt has been known for a long time that microbial predictive models based on traditional plate methods are time-consuming and labour-intensive. Denaturing gradient gel electrophoresis (DGGE) has been widely used as a semiquantitative method to describe complex microbial community. In our study, we developed DGGE to quantify bacterial counts and simultaneously established two molecular predictive models to describe the growth and survival of two bacteria (Listeria monocytogenes and Vibrio parahaemolyticus) at 4 and 10°C. We demonstrated that PCR-DGGE could be used to construct growth models. This work provides a new approach to construct molecular predictive models and thereby facilitates predictive microbiology and QMRA (Quantitative Microbial Risk Assessment).

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