Abstract: With the improvement of living standards, people’s demands for food quality and safety have increased, driving the urgent need for cutting-edge analytical methodologies in food science research. In this context, the integration of liquid chromatography high resolution mass spectrometry (LC-HRMS) with machine learning (ML) has established a sophisticated framework for foodomics. This powerful technological combination is gradually becoming a new direction for food science research and is widely being applied in food authenticity identification, safety assessment, and quality control. This review provides a critical overview of recent advances in the application of LC-HRMS combined with ML in food metabolomics, proteomics, lipidomics, and multi-omics approaches. Furthermore, we discuss the future development of the combination of LC-HRMS and ML, aiming to provide a theoretical basis and technical guidance for the intelligent development of rapid and comprehensive food testing.

Key words: food; liquid chromatography high resolution mass spectrometry; machine learning; foodomics