Processing can affect milk properties and alter the composition of milk metabolites, which has corresponding effects on milk flavor and quality. It is quite important to study the safe quality control of milk processing. Therefore, the purpose of this study was to identify metabolites at different steps of ultra‐high‐temperature‐sterilized (UHT) milk processing using gas chromatography–mass spectrometry (GC–MS). These steps included raw milk, pasteurized milk (80°C for 15 s), semi‐finished milk (after pasteurizing, it was homogenized at 75°C with pressure of 250 bar), UHT milk (at 140°C for 10 s), and finished milk (homogenized UHT milk). A total of 66 metabolites were identified across all samples, including 30 metabolites in the chloroform layers of the milk samples and 41 metabolites in the water layers; 5 metabolites were found in both layers. The metabolites were primarily fatty acids, amino acids, sugars, and organic acids. For example, pasteurized and ultra‐high‐temperature‐sterilized kinds of milk had lactose contents similar to those of raw milk, with increases in saturated fatty acids such as hexadecanoic acid and octadecanoic acid. Additionally, these findings indicated that these methods of processing can affect the contents of some components of milk. Therefore, from the perspective of milk's nutritional value and consumer health, the excessive heating of dairy products should be avoided and the milk heat treatment process should be standardized from the source.

The objective of the study was to elucidate the stearoyl-coenzyme A desaturase (SCD1)-dependent gene network of c9, t11-CLA biosynthesis in MAC-T cells from an energy metabolism perspective. The cells were divided into the CAY group (firstly incubated with CAY10566, a chemical inhibitor of SCD1, then incubated with trans-11-octadecenoic acid, (TVA)), the TVA group (only TVA), and the control group (without CAY, TVA). The c9, t11-CLA, and TVA contents were determined by gas chromatography. The mRNA levels of SCD1 and candidate genes were analyzed via real-time PCR. Tandem mass tag (TMT)-based quantitative proteomics, bioinformatic analysis, parallel reaction monitoring (PRM), and small RNA interference were used to explore genes involved in the SCD1-dependent c9, t11-CLA biosynthesis. The results showed that the SCD1 deficiency led by CAY10566 blocked the biosynthesis of c9, t11-CLA. In total, 60 SCD1-related proteins mainly involved in energy metabolism pathways were primarily screened by TMT-based quantitative proteomics analysis. Moreover, 17 proteins were validated using PRM analysis. Then, 11 genes were verified to have negative relationships with SCD1 after the small RNA interference analysis. Based on the above results, we concluded that genes involved in energy metabolism pathways have an impact on the SCD1-dependent molecular mechanism of c9, t11-CLA biosynthesis.