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Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum

INTRODUCTION: Polygonatum kingianum is a traditional medicinal plant, and processing has significantly impacts its quality. METHODS: Therefore, untargeted gas chromatography-mass spectrometry (GC-MS) and Fourier transform-near-infrared spectroscopy (FT-NIR) were used to analyze the 14 processing met...

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Autores principales: Xu, Yulin, Yang, Meiquan, Yang, Tianmei, Yang, Weize, Wang, Yuanzhong, Zhang, Jinyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200983/
https://www.ncbi.nlm.nih.gov/pubmed/37223798
http://dx.doi.org/10.3389/fpls.2023.1140691
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author Xu, Yulin
Yang, Meiquan
Yang, Tianmei
Yang, Weize
Wang, Yuanzhong
Zhang, Jinyu
author_facet Xu, Yulin
Yang, Meiquan
Yang, Tianmei
Yang, Weize
Wang, Yuanzhong
Zhang, Jinyu
author_sort Xu, Yulin
collection PubMed
description INTRODUCTION: Polygonatum kingianum is a traditional medicinal plant, and processing has significantly impacts its quality. METHODS: Therefore, untargeted gas chromatography-mass spectrometry (GC-MS) and Fourier transform-near-infrared spectroscopy (FT-NIR) were used to analyze the 14 processing methods commonly used in the Chinese market.It is dedicated to analyzing the causes of major volatile metabolite changes and identifying signature volatile components for each processing method. RESULTS: The untargeted GC-MS technique identified a total of 333 metabolites. The relative content accounted for sugars (43%), acids (20%), amino acids (18%), nucleotides (6%), and esters (3%). The multiple steaming and roasting samples contained more sugars, nucleotides, esters and flavonoids but fewer amino acids. The sugars are predominantly monosaccharides or small molecular sugars, mainly due to polysaccharides depolymerization. The heat treatment reduces the amino acid content significantly, and the multiple steaming and roasting methods are not conducive to accumulating amino acids. The multiple steaming and roasting samples showed significant differences, as seen from principal component analysis (PCA) and hierarchical cluster analysis (HCA) based on GC-MS and FT-NIR. The partial least squares discriminant analysis (PLS-DA) based on FT-NIR can achieve 96.43% identification rate for the processed samples. DISCUSSION: This study can provide some references and options for consumers, producers, and researchers.
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spelling pubmed-102009832023-05-23 Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum Xu, Yulin Yang, Meiquan Yang, Tianmei Yang, Weize Wang, Yuanzhong Zhang, Jinyu Front Plant Sci Plant Science INTRODUCTION: Polygonatum kingianum is a traditional medicinal plant, and processing has significantly impacts its quality. METHODS: Therefore, untargeted gas chromatography-mass spectrometry (GC-MS) and Fourier transform-near-infrared spectroscopy (FT-NIR) were used to analyze the 14 processing methods commonly used in the Chinese market.It is dedicated to analyzing the causes of major volatile metabolite changes and identifying signature volatile components for each processing method. RESULTS: The untargeted GC-MS technique identified a total of 333 metabolites. The relative content accounted for sugars (43%), acids (20%), amino acids (18%), nucleotides (6%), and esters (3%). The multiple steaming and roasting samples contained more sugars, nucleotides, esters and flavonoids but fewer amino acids. The sugars are predominantly monosaccharides or small molecular sugars, mainly due to polysaccharides depolymerization. The heat treatment reduces the amino acid content significantly, and the multiple steaming and roasting methods are not conducive to accumulating amino acids. The multiple steaming and roasting samples showed significant differences, as seen from principal component analysis (PCA) and hierarchical cluster analysis (HCA) based on GC-MS and FT-NIR. The partial least squares discriminant analysis (PLS-DA) based on FT-NIR can achieve 96.43% identification rate for the processed samples. DISCUSSION: This study can provide some references and options for consumers, producers, and researchers. Frontiers Media S.A. 2023-05-08 /pmc/articles/PMC10200983/ /pubmed/37223798 http://dx.doi.org/10.3389/fpls.2023.1140691 Text en Copyright © 2023 Xu, Yang, Yang, Yang, Wang and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Xu, Yulin
Yang, Meiquan
Yang, Tianmei
Yang, Weize
Wang, Yuanzhong
Zhang, Jinyu
Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum
title Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum
title_full Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum
title_fullStr Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum
title_full_unstemmed Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum
title_short Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum
title_sort untargeted gc-ms and ft-nir study of the effect of 14 processing methods on the volatile components of polygonatum kingianum
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200983/
https://www.ncbi.nlm.nih.gov/pubmed/37223798
http://dx.doi.org/10.3389/fpls.2023.1140691
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