Cargando…

Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers

The present study was conducted to clarify the differences in the multi-element, volatile organic compound, fatty acid, and metabolite fingerprints between black and white sesame seeds. A total of 53 chemical elements, 32 volatile flavor compounds, 40 fatty acids, and 283 metabolites were identified...

Descripción completa

Detalles Bibliográficos
Autores principales: Mi, Si, Wang, Yuhang, Zhang, Xiangnan, Sang, Yaxin, Wang, Xianghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315883/
https://www.ncbi.nlm.nih.gov/pubmed/35885285
http://dx.doi.org/10.3390/foods11142042
_version_ 1784754671721644032
author Mi, Si
Wang, Yuhang
Zhang, Xiangnan
Sang, Yaxin
Wang, Xianghong
author_facet Mi, Si
Wang, Yuhang
Zhang, Xiangnan
Sang, Yaxin
Wang, Xianghong
author_sort Mi, Si
collection PubMed
description The present study was conducted to clarify the differences in the multi-element, volatile organic compound, fatty acid, and metabolite fingerprints between black and white sesame seeds. A total of 53 chemical elements, 32 volatile flavor compounds, 40 fatty acids, and 283 metabolites were identified and evaluated in the two groups of sesame seeds. Univariate and multivariate statistics indicated a distinct separation between the two groups of sesame seeds. A panel of 16 chemical elements, 3 volatile compounds, 8 individual fatty acids, and 54 metabolites with p value < 0.05 and variable importance in projection score > 1 were selected as the most important discriminants for the two types of sesame seeds. Overall, these data reveal the influence of genotype on the chemical composition of sesame seeds. Our findings also demonstrate that the hybrid model of instrumental analysis and chemometrics is feasible for the discrimination of black and white sesame seeds.
format Online
Article
Text
id pubmed-9315883
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-93158832022-07-27 Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers Mi, Si Wang, Yuhang Zhang, Xiangnan Sang, Yaxin Wang, Xianghong Foods Article The present study was conducted to clarify the differences in the multi-element, volatile organic compound, fatty acid, and metabolite fingerprints between black and white sesame seeds. A total of 53 chemical elements, 32 volatile flavor compounds, 40 fatty acids, and 283 metabolites were identified and evaluated in the two groups of sesame seeds. Univariate and multivariate statistics indicated a distinct separation between the two groups of sesame seeds. A panel of 16 chemical elements, 3 volatile compounds, 8 individual fatty acids, and 54 metabolites with p value < 0.05 and variable importance in projection score > 1 were selected as the most important discriminants for the two types of sesame seeds. Overall, these data reveal the influence of genotype on the chemical composition of sesame seeds. Our findings also demonstrate that the hybrid model of instrumental analysis and chemometrics is feasible for the discrimination of black and white sesame seeds. MDPI 2022-07-11 /pmc/articles/PMC9315883/ /pubmed/35885285 http://dx.doi.org/10.3390/foods11142042 Text en © 2022 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
Mi, Si
Wang, Yuhang
Zhang, Xiangnan
Sang, Yaxin
Wang, Xianghong
Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers
title Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers
title_full Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers
title_fullStr Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers
title_full_unstemmed Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers
title_short Discrimination of Black and White Sesame Seeds Based on Targeted and Non-Targeted Platforms with Chemometrics: From Profiling towards Identification of Chemical Markers
title_sort discrimination of black and white sesame seeds based on targeted and non-targeted platforms with chemometrics: from profiling towards identification of chemical markers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315883/
https://www.ncbi.nlm.nih.gov/pubmed/35885285
http://dx.doi.org/10.3390/foods11142042
work_keys_str_mv AT misi discriminationofblackandwhitesesameseedsbasedontargetedandnontargetedplatformswithchemometricsfromprofilingtowardsidentificationofchemicalmarkers
AT wangyuhang discriminationofblackandwhitesesameseedsbasedontargetedandnontargetedplatformswithchemometricsfromprofilingtowardsidentificationofchemicalmarkers
AT zhangxiangnan discriminationofblackandwhitesesameseedsbasedontargetedandnontargetedplatformswithchemometricsfromprofilingtowardsidentificationofchemicalmarkers
AT sangyaxin discriminationofblackandwhitesesameseedsbasedontargetedandnontargetedplatformswithchemometricsfromprofilingtowardsidentificationofchemicalmarkers
AT wangxianghong discriminationofblackandwhitesesameseedsbasedontargetedandnontargetedplatformswithchemometricsfromprofilingtowardsidentificationofchemicalmarkers