Development and Validation of a Predictive Model for the Growth of Salmonella enterica in Chicken Meat. Issue 4 (17th July 2014)

• Record Type: Journal Article
• Authors: Zhou, Kang; Zhong, Kaicheng; Long, Chao; Han, Xinfeng; Liu, Shuliang
• Is Part Of: Journal of food safety. Volume 34:Issue 4(2014:Oct.)
• Page Start: 326
• Page End: 332
• Publication Date: 2014-07-17
• Subjects: Food adulteration and inspection -- Periodicals; Food contamination -- Periodicals; Food -- Analysis -- Periodicals; Food -- Microbiology -- Periodicals; Pathogenic bacteria -- Periodicals; Food handling -- Periodicals; Food preservatives -- Periodicals; 664
• DOI: 10.1111/jfs.12131
• Languages: English
• ISSNs: 0149-6085

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <sec id="jfs12131-sec-0001" sec-type="section"> <p>The growth of <italic>S</italic><italic>almonella</italic> in chicken meat at different temperatures and water activities was studied. The DMFit software (Institute of Food Reseach, Norwich, UK), based on the Baranyi model, was used to fit growth curves and obtain growth parameters. When temperature or water activity increased, the maximum growth rate increased and the lag time decreased. The developed secondary model was validated by published reports and 422 data from ComBase, which suggest that the model could be used to safely predict the growth of <italic>S</italic><italic>almonella</italic> in chicken. In this study, the correlation between growth parameters and growth conditions was also investigated using unified and separated models. These models were validated using a different <italic>S</italic><italic>almonella</italic> strain, isolated from chicken meat, and also with independent data from literatures and ComBase. The values of accuracy and bias factor are 0.99, 1.22 for unified model, and 0.98 and 1.08 for separated model, which suggested the predictions of the models were in a safe and acceptable range. The evaluation suggested robustness of the models and the statistical results of the models show goodness‐of‐fit.</p> </sec> <sec id="jfs12131-sec-0002" sec-type="section"> <title>Practical Applications</title> <p>There is no significant difference between unified and separated models, so the data tested in laboratory media could be easily used in real food after the mathematical process using unified model. This is important in predictive microbiology, as it can be readily used to control the potential hazard in food and maintain food safety.</p> </sec> </abstract>