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Untargeted Metabolomics Approach for the Differentiation between Panax vietnamensis var. vietnamensis and Panax vietnamensis var. fuscidiscus
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Panax vietnamensis var. vietnamensis (PVV) and Panax vietnamensis var. fuscidiscus (PVF) both belong to Panax vietnamensis species and are chemically and morphologically similar, making it hard to distinguish for the consumer. Herein, 42 PVF and 12 PVV samples were collected in Quang Nam and Lai Chau Province, respectively, and subsequently characterized by ITSr-DNA sequence data to verify their origins. Next, untargeted metabolomics combined with multivariate statistical analysis was developed to differentiate PVV and PVF. The metabolic profiles of PVV and PVF were found to be distinct and classified well using Partial Least-Squares Discriminant Analysis (PLS-DA) in the training set. Among them, seven ginsenosides were of high abundance in PVV, while six were of high abundance in PVF. Next, the test set was used to validate 13 putative differential markers found in the training set, illustrating a complete match with the expression patterns of these ginsenosides in the training set. Finally, PLS-DA and linear Support Vector Machine models both indicated distinct ginsenoside profiles of PVV and PVF without misclassification in the test set. Conclusively, the developed untargeted metabolomics approach might serve as a powerful tool for the authentication of PVV and PVF at the metabolome level.
Title: Untargeted Metabolomics Approach for the Differentiation between Panax vietnamensis var. vietnamensis and Panax vietnamensis var. fuscidiscus
Description:
Panax vietnamensis var.
vietnamensis (PVV) and Panax vietnamensis var.
fuscidiscus (PVF) both belong to Panax vietnamensis species and are chemically and morphologically similar, making it hard to distinguish for the consumer.
Herein, 42 PVF and 12 PVV samples were collected in Quang Nam and Lai Chau Province, respectively, and subsequently characterized by ITSr-DNA sequence data to verify their origins.
Next, untargeted metabolomics combined with multivariate statistical analysis was developed to differentiate PVV and PVF.
The metabolic profiles of PVV and PVF were found to be distinct and classified well using Partial Least-Squares Discriminant Analysis (PLS-DA) in the training set.
Among them, seven ginsenosides were of high abundance in PVV, while six were of high abundance in PVF.
Next, the test set was used to validate 13 putative differential markers found in the training set, illustrating a complete match with the expression patterns of these ginsenosides in the training set.
Finally, PLS-DA and linear Support Vector Machine models both indicated distinct ginsenoside profiles of PVV and PVF without misclassification in the test set.
Conclusively, the developed untargeted metabolomics approach might serve as a powerful tool for the authentication of PVV and PVF at the metabolome level.
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