A conversation between hyphenated spectroscopic techniques and phytometabolites from medicinal plants. Issue 11 (30th September 2021)

• Record Type: Journal Article
• Authors: Puri, Shivani; Sahal, Dinkar; Sharma, Upendra
• Other Names: Misra Biswapriya guestEditor.; Volmer Dietrich A. guestEditor.
• Is Part Of: Analytical Science Advances. Volume 2:Issue 11/12(2021)
• Page Start: 579
• Page End: 593
• Publication Date: 2021-09-30
• Subjects: herbal medicine -- hyphenated techniques -- multivariate data analysis -- phytometabolite profiling -- plant metabolomics; Chemistry, Analytic -- Periodicals; Science -- Periodicals; 543
• DOI: 10.1002/ansa.202100021
• Languages: English
• ISSNs: 2628-5452

Abstract

Abstract: Medicinal plant metabolomics has emerged as a goldmine for the natural product chemists. It provides a pool of bioactive phytoconstituents leading to accelerated novel discoveries and the elucidation of a variety of biosynthetic pathways. Further, it also acts as an innovative tool for herbal medicine's scientific validation and quality assurance. This review highlights different strategies and analytical techniques employed in the practice of metabolomics. Further, it also discusses several other applications and advantages of metabolomics in the area of natural product chemistry. Additional examples of integrating metabolomics with multivariate data analysis techniques for some Indian medicinal plants are also reviewed. Recent technical advances in mass spectrometry‐based hyphenated techniques, nuclear magnetic resonance‐based techniques, and comprehensive hyphenated technologies for phytometabolite profiling studies have also been reviewed. Mass Spectral Imaging (MSI) has been presented as a highly promising method for high precision in situ spatiotemporal monitoring of phytometabolites. We conclude by introducing GNPS (Global Natural Products Social Molecular Networking) as an emerging platform to make social networks of related molecules, to explore data and to annotate more metabolites, and expand the networks to novel "predictive" metabolites that can be validated.