Chemometric applications in metabolomic studies using chromatography-mass spectrometry

Metabolomic studies generate large and exceptionally complex datasets. The chemical diversity that exists within the metabolome presents an immense analytical challenge. Chemometric methods are frequently used to analyze such data because these approaches offer an efficient route to meaningful interpretation of results. The techniques used in recent research fall into three general categories: statistical methods, machine learning models, and custom solutions. There are drawbacks and strengths to every approach, and the right choice varies study to study, depending on the experimental design and hypothesis. It is common for researchers to employ multiple methods by building a pipeline of data analysis steps to analyze their spectra. These pipelines are designed to parse the data in a systematic fashion to best answer the question at hand. This review covers advancements in chemometric techniques applied to metabolomics studies in the last five years. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Metabolomic studies generate complex datasets, and chemometric methods are commonly used for analysis. Techniques can be categorized into statistical methods, machine learning models, and custom solutions, with researchers choosing the right approach based on experimental design and hypothesis. Multiple methods are often used in a pipeline for systematic data analysis.