Foodborne Pathogen Prevalence and Biomarker Identification for Microbial Contamination in Mutton Meat.

Simple Summary: This study analyzed microbial contamination in mutton meat and during its slaughter process at four retail sites in Coimbatore, focusing on the total microbial load and prevalence of specific pathogens. Samples from mutton meat, cutting boards, hand swabs, knives, weighing balances, and water were collected. Mutton-washed water and mutton meat exhibited the highest microbial loads, particularly in terms of total plate count and coliforms. E. coli and Staphylococcus species were common, with automated identification revealing that most pathogens were of Staphylococcus origin. Salmonella was detected in 57% of the mutton samples using an automated identification system. Gas chromatography and mass spectrometry analysis of goat meat inoculated with pathogens identified distinct volatile and metabolite profiles, providing potential biomarkers for contamination. Multivariate statistical analysis further differentiated the volatile and metabolite profiles. These findings underscore the importance of cross-contamination during meat handling and suggest using volatile compounds for pathogen detection. Microbial contamination and the prevalence of foodborne pathogens in mutton meat and during its slaughtering process were investigated through microbial source tracking and automated pathogen identification techniques. Samples from mutton meat, cutting boards, hand swabs, knives, weighing balances, and water sources were collected from four different retail sites in Coimbatore. Total plate count (TPC), yeast and mold count (YMC), coliforms, E. coli, Pseudomonas aeruginosa, Salmonella, and Staphylococcus were examined across 91 samples. The highest microbial loads were found in the mutton-washed water, mutton meat, and cutting board samples. The automated pathogen identification system identified Staphylococcus species as the predominant contaminant and also revealed a 57% prevalence of Salmonella. Further analysis of goat meat inoculated with specific pathogens showed distinct volatile and metabolite profiles, identified using gas chromatography-mass spectrometry (GC-MS). Multivariate statistical analyses, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and sparse partial least squares discriminant analysis (sPLS-DA), identified potential biomarkers for pathogen contamination. The results highlight the significance of cross-contamination in the slaughtering process and suggest the use of volatile compounds as potential biomarkers for pathogen detection. [ABSTRACT FROM AUTHOR]