Application of machine learning in LC-MS-based non-targeted analysis

Abstract

Non-targeted analysis, a cornerstone in fields such as metabolomics, environmental science, and food safety, allows for the comprehensive screening of constituents in a sample without preconceived target compounds. The vast and intricate data generated by these analyses, however, often present challenges for traditional data processing methods in effectively extracting valuable insights. Machine learning, as a robust tool for data processing and pattern recognition, has increasingly garnered the attention of researchers for its application in non-targeted analysis. This review synthesizes the latest advancements in the application of machine learning to non-targeted analysis. Furthermore, the discussion covers key steps such as data acquisition, data preprocessing, feature extraction, and data analysis and interpretation. It highlights the challenges that machine learning faces in these critical stages and proposes future research directions. By reviewing the most recent research findings, this review aims to provide practical guidance on the selection and optimization of machine learning methods for researchers in non-targeted analysis, thereby fostering further development and application in this domain.