Literature DB >> 30071382

Metabolic discrimination of pine resins using multiple analytical platforms.

Luis Francisco Salomé-Abarca1, Jorik van der Pas1, Hye Kyong Kim1, Gerda A van Uffelen2, Peter G L Klinkhamer3, Young Hae Choi4.   

Abstract

Resins are one of the first sites of interaction between plants and biotic and abiotic factors. Despite their evident morphological and chemical differentiation from other plant organs, the detailed correlation between resins and biological or environmental factors is not yet clear. In this study, 1H nuclear magnetic resonance (NMR), gas chromatography coupled with mass spectrometry (GC-MS) and high-performance thin-layer chromatography (HPTLC)-based profiling techniques were applied to the metabolic characterisation of plant resins of different species and season of collection, using samples from five different species that were collected during early and late spring. The 1H NMR analysis confirmed the main metabolic groups in the resins to be terpenoids and further GC-MS analysis revealed a notable chemical variation between the species and collection periods. Abies grandis displayed a significant differentiation from the other species, showing a higher number of monoterpenes. The HPTLC-based profiling method hyphenated with multivariate data analysis (MVDA) also showed a clear separation confirming the GC-MS terpenoidal profiling results. Additionally, the unknown compounds were obtained by preparative TLC for identification. Based on the results of the three analytical platforms, it was concluded that the major difference in chemical composition of pine species was between species rather than the collection period. Nonetheless, the chemical profiles of resins from different species and collection periods can be well discriminated and correlated to mono- and sesquiterpenes in the case of species and diterpenes for the collection periods.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  GC-MS; HPTLC; Metabolic variation; NMR; Pine resin; Terpenoids

Mesh:

Substances:

Year:  2018        PMID: 30071382     DOI: 10.1016/j.phytochem.2018.07.011

Source DB:  PubMed          Journal:  Phytochemistry        ISSN: 0031-9422            Impact factor:   4.072


  6 in total

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6.  HPTLC-Based Chemical Profiling: An Approach to Monitor Plant Metabolic Expansion Caused by Fungal Endophytes.

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  6 in total

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