Premium
GC‐MS Based Metabolomics for Rapid Simultaneous Detection of Escherichia coli O157:H7, Salmonella Typhimurium, Salmonella Muenchen, and Salmonella Hartford in Ground Beef and Chicken
Author(s) -
CevallosCevallos Juan Manuel,
Danyluk Michelle D.,
ReyesDeCorcuera José I.
Publication year - 2011
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1750-3841.2011.02132.x
Subject(s) - salmonella , escherichia coli , metabolite , microbiology and biotechnology , chemistry , bacteria , tryptic soy broth , metabolome , enterobacteriaceae , food science , biology , biochemistry , genetics , gene
A metabolomic‐based method for rapid detection of Escherichia coli O157:H7, Salmonella Hartford, Salmonella Typhimurium, and Salmonella Muenchen in nonselective media was developed. All pathogenic bacteria were grown in tryptic soy broth (TSB) at 37 °C followed by metabolite quantification at 2‐h intervals for 24 h. Results were compared with the metabolite profiles similarly obtained with E. coli K12, Pseudomonas aeruginosa , Staphylococcus aureus , Saccharomyces cereviseae , and Aspergillus oryzae grown individually or as a cocktail under the same conditions. Principal component analysis (PCAS) discriminated pathogenic microorganisms grown in TSB. Metabolites responsible of PCAS classification were dextrose, cadaverine, the aminoacids L‐histidine, glycine, and L‐tyrosine, as well as the volatiles 1‐octanol, 1‐propanol, 1butanol, 2‐ethyl‐1‐hexanol, and 2,5‐dimethyl‐pyrazine. Partial least square (PLS) models based on the overall metabolite profile of each bacteria were able to detect the presence of Escherichia coli O157:H7 and Salmonella spp. at levels of approximately 7 ± 2 CFU/25 g of ground beef and chicken within 18 h. Practical Application: Rapid detection of foodborne pathogens is critical to reduce food‐related disease outbreaks and product recalls. This research proposes an alternative approach to rapidly detect 2 major pathogens based on detecting changes in the metabolite profile during incubation in nonselective culture media. Although not directly transferable to an industrial environment, this proof of concept research provides evidence of potential application.