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Interspecific and Environmental Influence on the Foliar Metabolomes of Mitragyna Species Through Recursive OPLSDA Modeling

I tiakina i:
I whakaputaina i:Plants vol. 14, no. 17 (2025), p. 2721-2737
Kaituhi matua: Andriyas Tushar
Ētahi atu kaituhi: Leksungnoen Nisa, Uthairatsamee Suwimon, Ngernsaengsaruay Chatchai, Sanyogita, Andriyas
I whakaputaina:
MDPI AG
Ngā marau:
Thailand
Africa
Mass spectrometry
Interspecific
Gas chromatography
Datasets
Modelling
Nuclear magnetic resonance > NMR
Metabolomics
Scientific imaging
Metabolites
Metabolism
Discriminant analysis
Chromatography
Vanillic acid
Chemical composition
Recursive methods
Squalene
Classification
Mass spectroscopy
Environmental factors
Mitella diversifolia
Urunga tuihono:Citation/Abstract
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Whakarāpopotonga:Understanding interspecific and environmental influences on secondary metabolite profiles can be critical in plant metabolomics. This study used a hierarchical orthogonal projections to latent structure discriminant analysis (OPLS-DA) to classify the foliar metabolomes of four naturally growing Mitragyna species in Thailand, M. speciosa, M. diversifolia, M. hirsuta, and M. rotundifolia. Using a recursive binary classification, interspecific and environmental influences were determined in multiple class separations, while identifying key metabolites driving these distinctions. Gas chromatography–mass spectrometry (GC-MS) annotated 409 metabolites, and through a progressive class differentiation using hierarchical OPLS-DA, M. speciosa exhibited a metabolome distinct from the other three species. However, the metabolomes of M. hirsuta and M. rotundifolia had a lot of overlap, while M. diversifolia displayed regional metabolic variation, emphasizing the role of environmental factors in shaping its chemical composition. Key metabolites, such as mitragynine, isorhynchophylline, squalene, and vanillic acid, among others, were identified as major discriminators across the hierarchical splits. Unlike conventional OPLS-DA, which struggles with multiclass datasets, the recursive approach identified class structures that were biologically relevant, without the need for manual pairwise modeling. The results aligned with prior morphological and genetic studies, validating the method’s robustness in capturing interspecific and environmental differences, which can be used in high-dimensional multiclass plant metabolomics.
ISSN:2223-7747
DOI:10.3390/plants14172721
Puna:Agriculture Science Database