VOC-based metabolic profiling for food spoilage detection with the application to detecting Salmonella typhimurium-contaminated pork.

In this study, we investigated the feasibility of using a novel volatile organic compound (VOC)-based metabolic profiling approach with a newly devised chemometrics methodology which combined rapid multivariate analysis on total ion currents with in-depth peak deconvolution on selected regions to characterise the spoilage progress of pork. We also tested if such approach possessed enough discriminatory information to differentiate natural spoiled pork from pork contaminated with Salmonella typhimurium, a food poisoning pathogen commonly recovered from pork products. Spoilage was monitored in this study over a 72-h period at 0-, 24-, 48- and 72-h time points after the artificial contamination with the salmonellae. At each time point, the VOCs from six individual pork chops were collected for spoiled vs. contaminated meat. Analysis of the VOCs was performed by gas chromatography/mass spectrometry (GC/MS). The data generated by GC/MS analysis were initially subjected to multivariate analysis using principal component analysis (PCA) and multi-block PCA. The loading plots were then used to identify regions in the chromatograms which appeared important to the separation shown in the PCA/multi-block PCA scores plot. Peak deconvolution was then performed only on those regions using a modified hierarchical multivariate curve resolution procedure for curve resolution to generate a concentration profiles matrix C and the corresponding pure spectra matrix S. Following this, the pure mass spectra (S) of the peaks in those region were exported to NIST 02 mass library for chemical identification. A clear separation between the two types of samples was observed from the PCA models, and after deconvolution and univariate analysis using N-way ANOVA, a total of 16 significant metabolites were identified which showed difference between natural spoiled pork and those contaminated with S. typhimurium.