Predictive Modeling for the Growth of <i>Salmonella</i> Enterica Serovar Typhimurium on Lettuce Washed with Combined Chlorine and Ultrasound During Storage

Abstract

This study developed predictive growth models of Salmonella enterica Serovar Typhimurium on lettuce washed with chlorine (100~300 ppm) and ultrasound (US, 37 kHz, 380 W) treatment and stored at different temperatures (10~25°C) using a polynomial equation. The primary model of specific growth rate (SGR) and lag time (LT) showed a good fit (R2≥0.92) with a Gompertz equation. A secondary model was obtained using a quadratic polynomial equation. The appropriateness of the secondary SGR and LT model was verified by coefficient of determination (R2=0.98~0.99 for internal validation, 0.97~0.98 for external validation), mean square error (MSE=- 0.0071~0.0057 for internal validation, -0.0118~0.0176 for external validation), bias factor (Bf=0.9918~1.0066 for internal validation, 0.9865~1.0205 for external validation), and accuracy factor (Af=0.9935~1.0082 for internal validation, 0.9799~1.0137 for external validation). The newly developed models for S. Typhimurium could be incorporated into a tertiary modeling program to predict the growth of S. Typhimurium as a function of combined chlorine and US during the storage. These new models may also be useful to predict potential S. Typhimurium growth on lettuce, which is important for food safety purposes during the overall supply chain of lettuce from farm to table. Finally, the models may offer reliable and useful information of growth kinetics for the quantification microbial risk assessment of S. Typhimurium on washed lettuce.