Modelling the cross‐contamination of Listeria monocytogenes in pork during bowl chopping. (26th November 2017)

• Record Type: Journal Article
• Authors: Jiang, Ronghua; Wang, Xiang; Wang, Wen; Liu, Yangtai; Du, Jianping; Cui, Yang; Zhang, Chunyan; Dong, Qingli
• Is Part Of: International journal of food science & technology. Volume 53:Number 3(2018)
• Page Start: 837
• Page End: 846
• Publication Date: 2017-11-26
• Subjects: Chop -- cross‐contamination -- Listeria monocytogenes -- model -- pork; Food industry and trade -- Periodicals; 664
• DOI: 10.1111/ijfs.13660
• Languages: English
• ISSNs: 0950-5423

Abstract

Summary: This study has developed a predictive model for the cross‐contamination of pork by Listeria monocytogenes during bowl chopping. The transfer rates of L. monocytogenes were measured in sixteen chopping scenarios based on practical work. Meanwhile, contaminated bowl chopper was cleaned with either a dry rag (DR), warm water (WW) or 70% ethanol + water (EW), respectively. It was showed that significant differences ( P < 0.05) were observed among the three cleaning methods on the reduction of L. monocytogenes, the greatest log reduction being achieved by EW. Moreover, the model introduced by a previous study, predicting cross‐contamination of L. monocytogenes during meat slicing, was improved and validated in this study. Verification results showed that the improved model was acceptable for predicting L. monocytogenes cross‐contamination during pork chopping with coefficients of determination ( R 2 > 0.82), accuracy factors ( A f < 1.44), bias factors ( B f < 1.42), and root mean square error ( RMSE < 0.99). Furthermore, the modified model might provide an effective tool for assessing the risk of the cross‐contamination of meat products. Abstract : Schematic diagram of this study. The bowl chopper was used to chop raw meat, and sixteen different scenarios were set during chopping. The cross‐contamination model of Listeria monocytogenes was developed for chopping, and results of simulation were output from the model.