Predictive Models for the Growth Kinetics of Listeria monocytogenes on White Cabbage

Abstract

Growth data of Listeria monocytogenes on white cabbage were obtained at several fixed thermal conditions (4, 10, 15, 20, 25 and 30C) and were then fitted into four different primary models: the modified Gompertz model, Logistic model, the Logistic model with delay and Baranyi model. Then, these four primary models were compared by Akaike's information and Sawa's Bayesian information criteria. Accordingly, the obtained growth parameters such as growth rate (GR) and lag time (LT) from each primary model were used to develop the secondary models and the performances were then evaluated by median relative error (MRE), mean absolute relative error (MARE) and %standard error of prediction (%SEP). The results indicated that Baranyi model described the growth of L. monocytogenes on white cabbage better than Logistic model, the modified Gompertz model and the Logistic model with delay. The square root models can be used to describe the growth parameters of L. monocytogenes on white cabbage estimated from the modified Gompertz, Logistic, Logistic with delay and Baranyi models, which showed better predictions for GR than LT. PRACTICAL APPLICATIONThe responses of Listeria monocytogenes on white cabbage at various temperatures were investigated and modeled using different models. The goodness-of-fit and predictive abilities of different models were evaluated and the primary model with best performance to describe the growth behavior was selected. The growth parameters derived from each primary model were used to develop the secondary model using the square root model. The validated models can be used to predict potential L. monocytogenes growth on white cabbage, which are very valuable for the food safety purpose during the whole food chain of white cabbage from farm to table. They could provide reliable and valuable growth kinetics information for the quantitative microbiological risk assessment of L. monocytogenes on white cabbage.

Predictive Modeling for Growth of Non‐ and Cold‐adapted Listeria monocytogenes on Fresh‐cut Cantaloupe at Different Storage Temperatures

“…The results obtained in this study were in agreement with the previous studies, which showed that the growth of L. monocytogenes on fresh produce was highly dependent on storage temperature, revealing that an increase in storage temperature accelerates the cell growth (Koseki and Isobe ; Sinigaglia and others ; Wang and others ). For example, Wang and others () showed that L. monocytogenes on white cabbage stored at higher temperatures had shorter times to reach the maximum population (5–8 log CFU/g). It was evident that L. monocytogenes can grow on fresh‐cut cantaloupe at refrigerated temperatures, but the growth was somewhat hampered and failed to develop into full growth curves.…”

Model discrimination for drying and rehydration kinetics of freeze‐dried tomatoes

“…In Equations (15)–(17), R 2 is the regression coefficient of determination, AIC is the Akaike information criterion (Akaike, ; Moxon et al, ), and L is the maximum log‐likelihood of the estimated model (Spiess & Neumeyer, ). The model with best performance will be defined by the higher and lower AICC and BIC values (J. Wang et al, ).…”

Growth Prediction of the Total Bacterial Count in Freshly Squeezed Strawberry Juice during Cold Storage Using Electronic Nose and Electronic Tongue

“…and Listeria monocytogenes [ 21 ] in pork were performed. This tool was also applied on beef [ 22 , 23 ], vegetables [ 24 , 25 , 26 ], fruit [ 27 ] and so on. At present, many studies have completed rapid, accurate and non-destructive fitting of colony growth models by using hyperspectral imaging (HSI), an electronic nose (E-nose), etc., according to the optical and odor characteristics caused by the growth of microorganisms.…”