Metabolomic profiling of food matrices: Preliminary identification of potential markers of microbial contamination. Issue 10 (4th September 2020)

• Record Type: Journal Article
• Authors: Carraturo, Federica; Libralato, Giovanni; Esposito, Rodolfo; Galdiero, Emilia; Aliberti, Francesco; Amoresano, Angela; Fontanarosa, Carolina; Trifuoggi, Marco; Guida, Marco
• Is Part Of: Journal of food science. Volume 85:Issue 10(2020)
• Page Start: 3467
• Page End: 3477
• Publication Date: 2020-09-04
• Subjects: fingerprinting -- GC/MS -- meat -- metabolomics -- salmonella -- VOC; Food -- Periodicals; Food -- Research -- Periodicals; Food -- Periodicals; Research -- Periodicals; Levensmiddelen; Voeding; 664
• DOI: 10.1111/1750-3841.15418
• Languages: English
• ISSNs: 0022-1147

Abstract

Abstract: The research aimed to generate an early warning system highlighting in real‐time bacterial contamination of meat matrices and providing information which could support companies in accepting or rejecting batches. Current microorganisms' detection methods rely on techniques (plate counting), which provide retrospective values for microbial contamination. The purpose of this research was to evaluate the ability of the headspace solid‐phase microextraction (HS‐SPME) and gas chromatography‐mass spectrometry (GC/MS) methodologies to detect volatile organic carbons (VOCs), which may be associated to a peculiar microbiological contamination of food. The disposal of fast headspace gas chromatography‐mass spectrometry (HS‐SPME‐GC/MS) able to accurately and rapidly (30 min per sample) detect pathogens in raw meat could replace the traditional and time‐consuming (3 to 4 days) standardized microbiological analysis required by regulations. Experiments focused on qualitative and quantitative evaluations of VOCs produced by Salmonella Typhimurium, Campylobacter jejuni, and Staphylococcus aureus in different types of raw meat (beef, pork, chicken). HS‐SPME‐GC/MS allowed to use smaller sample volumes compared to traditional methods with no sample processing and the potentiality for its application on various food matrices for the detection of a wide variety of pathogens. Data analysis showed the identification of unique VOCs' profiles being possible markers of meat contamination due to their association to specific pathogens. The identification of VOCs markers in association to selected bacterial pathogens and their metabolites could support the rapid determination of specific meat samples contamination. Further research is required to outline‐specific metabolic profiles for each microorganism responsible of meat contamination and prevent false positives.