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Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology
Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explor...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606731/ https://www.ncbi.nlm.nih.gov/pubmed/37893607 http://dx.doi.org/10.3390/foods12203714 |
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author | Wen, Hui Yang, Tianmei Yang, Weize Yang, Meiquan Wang, Yuanzhong Zhang, Jinyu |
author_facet | Wen, Hui Yang, Tianmei Yang, Weize Yang, Meiquan Wang, Yuanzhong Zhang, Jinyu |
author_sort | Wen, Hui |
collection | PubMed |
description | Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography–mass spectrometry (GC–MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC–MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC–MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena. |
format | Online Article Text |
id | pubmed-10606731 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106067312023-10-28 Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology Wen, Hui Yang, Tianmei Yang, Weize Yang, Meiquan Wang, Yuanzhong Zhang, Jinyu Foods Article Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography–mass spectrometry (GC–MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC–MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC–MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena. MDPI 2023-10-10 /pmc/articles/PMC10606731/ /pubmed/37893607 http://dx.doi.org/10.3390/foods12203714 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wen, Hui Yang, Tianmei Yang, Weize Yang, Meiquan Wang, Yuanzhong Zhang, Jinyu Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology |
title | Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology |
title_full | Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology |
title_fullStr | Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology |
title_full_unstemmed | Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology |
title_short | Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC–MS and Multi-Spectral Fusion Technology |
title_sort | comparison of metabolites and species classification of thirteen zingiberaceae spices based on gc–ms and multi-spectral fusion technology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10606731/ https://www.ncbi.nlm.nih.gov/pubmed/37893607 http://dx.doi.org/10.3390/foods12203714 |
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