Abstract: In this study, considering the current market demand for quantitative analysis of adulterated samples, beef samples adulterated with different proportions of pork were analyzed by high-resolution mass spectrometry (HRMS), and the acquired data were evaluated by principal component analysis (PCA), partial least squares-discrimination analysis (PLS-DA), and orthogonal partial least squares-discrimination analysis (OPLS-DA). The results showed that the OPLS-DA model exhibited a better discriminative ability compared with the other methods. By sorting the S-plot values, 25 porcine-derived peptides and 25 bovine-derived peptides were selected. The linear correlation coefficients (R2) for 10 bovine-derived peptides and 6 porcine-derived peptides were above 0.99. Confirmatory experiments showed that the selected peptides could be used to predict the contents of adulterants in real samples. This study has established a method for quantifying the adulteration of beef steak and provided a new idea for screening of significantly differential peptides useful for the quantification of adulteration of meat products.

Key words: multivariate statistical analysis; orthogonal partial least squares-discrimination analysis; significantly differential peptides; quantitative analysis