Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples

Abstract

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken–beef, duck–beef, and pork–beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

μ‐PESI‐based MS profiling combined with untargeted metabolomics analysis for rapid identification of red wine geographical origin

“…Typically, all the mass spectra for each μ‐PESI analysis run were averaged into one mass spectrum. The MALDIquant package 23 of the R programming language (Vienna, Austria) was then applied to preprocess the mass spectral data, 24 including variance stabilization (method = ‘sqrt’), alignment (halfWindowSize = 30, SNR = 2.5, tolerance = 0.00005, warpingMethod = ‘lowess’), peak detection (halfWindowSize = 30, SNR = 2.5) and peak binning (tolerance = 0.00005). The preprocessed data were then extracted into an Excel file.…”

“…The 200‐permutation test was applied to evaluate overfitting 26 . The characteristic ions were selected by the V + S ‐plots with VIP ≥ 2.5, | p (corr)| ≥ 0.5 and | p | ≥ 0.05 in the PLS‐DA 24 . The characteristic ions were confirmed by checking their presence in the raw mass spectra with a reliable signal‐to‐noise ratio (S/N > 4).…”

“…26 The characteristic ions were selected by the V + S-plots with VIP ≥ 2.5, |p(corr)| ≥ 0.5 and |p| ≥ 0.05 in the PLS-DA. 24 The characteristic ions were confirmed by checking their presence in the raw mass spectra with a reliable signal-to-noise ratio (S/N > 4). Moreover, a box plot 27 and a clustering heatmap (MetaboAnalyst 5.0, Montreal, Canada) 28 was applied to illustrate the relative abundances of the characteristic ions in the samples.…”

Research Progress of Electronic Nose and Near-Infrared Spectroscopy in Meat Adulteration Detection

“…Nowadays, there are many varieties of meat, and the phenomenon of low-priced meat being used as high-priced meat is becoming more and more common. Typical cases of intentional adulteration are inter-species meat mixes, designed to deceive consumers by substituting cheaper meat for more expensive meat, such as beef adulterated with pork [81], with pork, duck, and other low-priced meat pretending to be beef, lamb, donkey meat, and other high-priced meat, with some spices added, so that consumers cannot identify the real attributes of the meat. This is the main means of adulteration in the market.…”