Pseudo-targeted metabolomics combined with a non-parametric discriminant model to distinguish commercial milk processed at different temperatures

Thermal processing is essential for liquid milk quality and safety. However, relatively broad standard regulations and inefficient supervision methods encourage enterprises to use excessive heating to maximize commercial profits. Therefore, a pseudo-targeted metabolomics approach based on ultra performance liquid chromatography quadrupole time-of flight mass spectrometry (UPLC-QTOF MS) with multivariate statistical analysis was used to discover biomarkers that can distinguish among pasteurized (29 Pa, 72 °C, 75 °C, and 85 °C), extended shelf life (9 ESL, 121 °C), and ultra-high temperature sterilization (20 UHT, 138 °C) milk. Finally, 10 key metabolites were characterized, including peptides, nicotinamide, N6-methyladenosin, and 2-hydroxycapric acid. The diagnostic performance of these candidate biomarkers was evaluated in 55 validation samples using receiver operating characteristic curve analysis and a non-parametric discrimination model, with 96.4% classification accuracy. These results demonstrated that the pseudo-targeted metabolomics method is reliable and provides effective biomarkers for evaluating the nutritional value of milk subjected to different thermal treatments.