Modelling the growth kinetics of Listeria monocytogenes in pasta salads at different storage temperatures and packaging conditions

The aim of this study was to model Listeria monocytogenes growth kinetics in ready to eat full meal pasta salads, containing fresh and cooked ingredients. With this aim, laboratory prepared salads, representing two formulations of commercial pasta salads, were spiked with L. monocytogenes and tested under categorised packaging and storage temperature conditions. L. monocytogenes enumeration results collected in 15 different laboratory prepared salad datasets were analysed with primary and secondary models. The models showing the best fit to describe L. monocytogenes growth kinetics in the laboratory prepared salads were then validated within commercial pasta salads. Baranyi no-lag was the best primary model fitting datasets collected at 12 degrees C, whereas the exponential model gave the best results for datasets collected at 4 degrees C. The maximum microbial specific growth rate (mu(max)) mean values obtained at 4 and 12 degrees C for salads packaged under air packaging conditions were 0.008 +/- 0.003 and 0.036 +/- 0.006 log(10) (cfu/g) h(-1), respectively. At the same temperatures, the mmax mean values obtained under modified atmosphere were 0.005 +/- 0.005 and 0.026 +/- 0.005 log(10) (cfu/g) h(-1), respectively. The Gamma secondary model predicted the growth kinetics of L. monocytogenes at both temperatures and packaging conditions and the mmax at the optimum temperature and the optimum pH for Listeria growth (mu(opt)) estimated by the model corresponded to 0.247 +/- 0.009 log(10) (cfu/g) h(-1). Baranyi model without lag phase was used to generate growth kinetics under different scenarios. In the comparison of the predicted log(10) concentrations respect to the observed ones the residues rarely exceeded 1 Log(10) cfu/g. The selected models can be applied to describe the growth kinetics of L. monocytogenes in similar types of pasta salads with comparable pH, shelf life and storage conditions. (C) 2018 Elsevier Ltd. All rights reserved.