绿阿拉比卡咖啡挥发性有机物(VOCs)指纹图谱:HS-GC-IMS对比GC × GC- ms

IF 3.1 Q2 FOOD SCIENCE & TECHNOLOGY
International Journal of Food Science Pub Date : 2025-08-22 eCollection Date: 2025-01-01 DOI:10.1155/ijfo/1302823
Matteo Bordiga, Vincenzo Disca, Marcello Manfredi, Elettra Barberis, Francesca Carrà, Luciano Navarini, Valentina Lonzarich, Marco Arlorio
{"title":"绿阿拉比卡咖啡挥发性有机物(VOCs)指纹图谱:HS-GC-IMS对比GC × GC- ms","authors":"Matteo Bordiga, Vincenzo Disca, Marcello Manfredi, Elettra Barberis, Francesca Carrà, Luciano Navarini, Valentina Lonzarich, Marco Arlorio","doi":"10.1155/ijfo/1302823","DOIUrl":null,"url":null,"abstract":"<p><p>This study compared two nontargeted analytical techniques-headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS)-to fingerprint the volatile organic compounds (VOCs) of green <i>Coffea arabica</i> beans from Ethiopia, Brazil, Nicaragua, and Guatemala. HS-GC-IMS enabled rapid differentiation of samples, detecting VOC signal regions that effectively clustered samples by origin with minimal preparation. GC × GC-MS offered higher chemical resolution, identifying 98 compounds, including methoxypyrazines, aldehydes, and alcohols, which significantly contributed to interorigin variability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the capacity of both methods to distinguish geographical origins, with hierarchical clustering highlighting region-specific VOC patterns. HS-GC-IMS proved efficient for high-throughput screening, while GC × GC-MS provided molecular insights into potential aroma precursors. Together, these platforms offer a complementary approach to green coffee authentication and quality control.</p>","PeriodicalId":14125,"journal":{"name":"International Journal of Food Science","volume":"2025 ","pages":"1302823"},"PeriodicalIF":3.1000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396915/pdf/","citationCount":"0","resultStr":"{\"title\":\"Fingerprinting of Green Arabica Coffee Volatile Organic Compounds (VOCs): HS-GC-IMS Versus GC × GC-MS.\",\"authors\":\"Matteo Bordiga, Vincenzo Disca, Marcello Manfredi, Elettra Barberis, Francesca Carrà, Luciano Navarini, Valentina Lonzarich, Marco Arlorio\",\"doi\":\"10.1155/ijfo/1302823\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study compared two nontargeted analytical techniques-headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS)-to fingerprint the volatile organic compounds (VOCs) of green <i>Coffea arabica</i> beans from Ethiopia, Brazil, Nicaragua, and Guatemala. HS-GC-IMS enabled rapid differentiation of samples, detecting VOC signal regions that effectively clustered samples by origin with minimal preparation. GC × GC-MS offered higher chemical resolution, identifying 98 compounds, including methoxypyrazines, aldehydes, and alcohols, which significantly contributed to interorigin variability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the capacity of both methods to distinguish geographical origins, with hierarchical clustering highlighting region-specific VOC patterns. HS-GC-IMS proved efficient for high-throughput screening, while GC × GC-MS provided molecular insights into potential aroma precursors. Together, these platforms offer a complementary approach to green coffee authentication and quality control.</p>\",\"PeriodicalId\":14125,\"journal\":{\"name\":\"International Journal of Food Science\",\"volume\":\"2025 \",\"pages\":\"1302823\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12396915/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Food Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/ijfo/1302823\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Food Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/ijfo/1302823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

本研究比较了顶空气相色谱-离子迁移谱法(HS-GC-IMS)和综合二维气相色谱-质谱法(GC × GC- ms)两种非靶向分析技术对来自埃塞俄比亚、巴西、尼加拉瓜和危地马拉的阿拉比卡绿咖啡豆挥发性有机化合物(VOCs)的指纹图谱。HS-GC-IMS能够快速区分样品,检测VOC信号区域,通过最少的准备有效地按来源聚类样品。GC × GC- ms提供了更高的化学分辨率,鉴定出98种化合物,包括甲氧基吡嗪、醛类和醇类,这些化合物对物种间变异性有重要贡献。主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)证实了这两种方法区分地理来源的能力,分层聚类突出了区域特定的VOC模式。HS-GC-IMS被证明是高效的高通量筛选,而GC × GC- ms则提供了潜在香气前体的分子见解。总之,这些平台为绿咖啡认证和质量控制提供了一种互补的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fingerprinting of Green Arabica Coffee Volatile Organic Compounds (VOCs): HS-GC-IMS Versus GC × GC-MS.

Fingerprinting of Green Arabica Coffee Volatile Organic Compounds (VOCs): HS-GC-IMS Versus GC × GC-MS.

Fingerprinting of Green Arabica Coffee Volatile Organic Compounds (VOCs): HS-GC-IMS Versus GC × GC-MS.

Fingerprinting of Green Arabica Coffee Volatile Organic Compounds (VOCs): HS-GC-IMS Versus GC × GC-MS.

This study compared two nontargeted analytical techniques-headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS)-to fingerprint the volatile organic compounds (VOCs) of green Coffea arabica beans from Ethiopia, Brazil, Nicaragua, and Guatemala. HS-GC-IMS enabled rapid differentiation of samples, detecting VOC signal regions that effectively clustered samples by origin with minimal preparation. GC × GC-MS offered higher chemical resolution, identifying 98 compounds, including methoxypyrazines, aldehydes, and alcohols, which significantly contributed to interorigin variability. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) confirmed the capacity of both methods to distinguish geographical origins, with hierarchical clustering highlighting region-specific VOC patterns. HS-GC-IMS proved efficient for high-throughput screening, while GC × GC-MS provided molecular insights into potential aroma precursors. Together, these platforms offer a complementary approach to green coffee authentication and quality control.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Food Science
International Journal of Food Science Agricultural and Biological Sciences-Food Science
CiteScore
6.20
自引率
2.50%
发文量
105
审稿时长
11 weeks
期刊介绍: International Journal of Food Science is a peer-reviewed, Open Access journal that publishes research and review articles in all areas of food science. As a multidisciplinary journal, articles discussing all aspects of food science will be considered, including, but not limited to: enhancing shelf life, food deterioration, food engineering, food handling, food processing, food quality, food safety, microbiology, and nutritional research. The journal aims to provide a valuable resource for food scientists, food producers, food retailers, nutritionists, the public health sector, and relevant governmental and non-governmental agencies.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信