Tensor methods in data analysis of chromatography/mass spectroscopy-based plant metabolomics

Area of Science:

• Plant metabolomics
• Chemometrics
• Tensor analysis

Background:

• Plant metabolomics is crucial for plant science research.
• Chemometrics aids in analyzing plant metabolomic data.
• High-order chemometrics, specifically tensor modeling, offers advanced methods for complex, multi-way data.

Purpose of the Study:

• To systematically review tensor methods for plant metabolomic data analysis.
• To summarize the advantages, disadvantages, and recent advances in tensor models.
• To discuss the application of tensor methods in solving plant science problems.

Main Methods:

• Review of existing literature on tensor methods in plant metabolomics.
• Analysis of tensor model applications across various plant science domains.
• Synthesis of methodological advances and challenges in tensor analysis.

Main Results:

• Tensor methods provide powerful tools for analyzing complex, multi-way plant metabolomic data.
• Applications span plant gene mutation, phenotype, disease resistance, pharmacology, nutrition, and product quality.
• Tensor methods significantly advance automated and intelligent plant metabolomics analysis.

Conclusions:

• Tensor methods are revolutionizing plant science research by enabling sophisticated data analysis.
• This review is the first to systematically cover tensor analysis in plant metabolomics.
• Tensor modeling represents a significant paradigm shift in plant metabolomics research.