title: Integrating multiple analytical platforms and chemometrics for comprehensive metabolic profiling: application to meat spoilage detection.
abstract: Untargeted metabolic profiling has become a common approach to attempt to understand biological systems. However, due to the large chemical diversity in the metabolites it is generally necessary to employ multiple analytical platforms so as to encompass a wide range of metabolites. Thus it is beneficial to find chemometrics approaches which can effectively integrate data generated from multiple platforms and ideally combine the strength of each platform and overcome their inherent weaknesses; most pertinent is with respect to limited chemistries. We have reported a few studies using untargeted metabolic profiling techniques to monitor the natural spoilage process in pork and also to detect specific metabolites associated with contaminations with the pathogen Salmonella typhimurium. One method used was to analyse the volatile organic compounds (VoCs) generated throughout the spoilage process while the other was to analyse the soluble small molecule metabolites (SMM) extracted from the microbial community, as well as from the surface of the spoiled/contaminated meat. In this study, we exploit multi-block principal component analysis (MB-PCA) and multi-block partial least squares (MB-PLS) to combine the VoCs and SMM data together and compare the results obtained by analysing each data set individually. We show that by combining the two data sets and applying appropriate chemometrics, a model with much better prediction and importantly with improved interpretability was obtained. The MB-PCA model was able to combine the strength of both platforms together and generated a model with high consistency with the biological expectations, despite its unsupervised nature. MB-PLS models also achieved the best over-all performance in modelling the spoilage progression and discriminating the naturally spoiled samples and the pathogen contaminated samples. Correlation analysis and Bayesian network analysis were also performed to elucidate which metabolites were correlated strongly in the two data sets and such information could add additional information in understanding the meat spoilage process.
authors.affiliation: School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. yun.xu-2@manchester.ac.uk
authors.forename: Yun
authors.initials: Y
authors.lastname: Xu
authors.forename: Elon
authors.initials: E
authors.lastname: Correa
authors.forename: Royston
authors.initials: R
authors.lastname: Goodacre
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D005591:Chemical Fractionation; D005504:Food Analysis / Q000379:methods*; D008401:Gas Chromatography-Mass Spectrometry; D015964:Gene Expression Regulation, Bacterial / Q000502:physiology; D008460:Meat / Q000382:microbiology / Q000592:standards*; D025341:Principal Component Analysis; D012486:Salmonella typhimurium / Q000378:metabolism*