Analysis of a molecular predictive mode for the growth of Staphylococcus aureus in pork

Abstract

ABSTRACTThe objective of this study was to develop a molecular predictive model from quantitative real-time polymerase chain reaction methods to describe the growth of S. aureus strains in artificially contaminated pork in storage dependent of a constant temperature (7–30°C). This model acquired by quantitative real-time polymerase chain reaction methods was compared to a conventional predictive model using data. This study used three of the main growth models to fit the growth equation. The results proved that Modified Gompertz, Logistic, and Richards models were adequate for describing the growth curves. These models had the very low rate of the growth of S. aureus in pork during a lag phase. The growth rate increased with temperature, and the lag time decreased. Lag phases were apparent in all models, and those samples stored at low temperatures had longer lag phases. There was no significant difference in the molecular and conventional predictive models for any of the growth curves. However, the use of a molecular predictive model could save more time and labor to construct more precise models of certain pathogens. In conclusion, the molecular predictive model could provide an effective method to lessen the risk of S. aureus of pork.