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

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

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,...

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Main Authors: Gayathri Muthusamy, Subburamu Karthikeyan, Veeranan Arun Giridhari, Ahmad R. Alhimaidi, Dananjeyan Balachandar, Aiman A. Ammari, Vaikuntavasan Paranidharan, Thirunavukkarasu Maruthamuthu
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Biology
Subjects:
microbial analysis
meat metabolite
meat volatile
automated detection
food safety
source tracking
Online Access:https://www.mdpi.com/2079-7737/13/12/1054
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Summary: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, <i>E. coli</i>, <i>Pseudomonas aeruginosa</i>, <i>Salmonella</i>, and <i>Staphylococcus</i> 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 <i>Staphylococcus</i> species as the predominant contaminant and also revealed a 57% prevalence of <i>Salmonella</i>. 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.
ISSN:2079-7737

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