Chemometrics advances on the challenges of the gas chromatography-mass spectrometry metabolomics data: a review

摘要

Metabolomics refers to the comprehensive and quantitative analysis of biological small molecules and aims to gather as much metabolic information as possible from a biological system. Accurate quantification of all metabolites in complex samples is a challenging issue and requires mathematical methods to analyze complex and very big data. Chemometrics methods have become crucial and dedicated tools for extracting valuable information from complex data. Additionally, gas chromatography-mass spectrometry (GC-MS) is a key tool with great potential in many analytical fields and has been demonstrated to be capable of facing many important challenges related to metabolomics research. This review presents an overview of the major advances in data pre-processing methods, metabolite identification and statistical data analysis methods for analyzing metabolomics data collected using GC-MS tool. Moreover, current study provides new insights into important chemometrics methods such as baseline correction, noise reduction, alignment, multivariate curve resolution, metabolite identification, multi-way calibration, unsupervised and supervised pattern recognition techniques to address metabolomics problems. For the sake of clarity, each of these topics will be discussed with different examples from the literature.