[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122251-en":3,"doc-seo-122251-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122251,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC–MS-based metabolomics and machine learning","Colored onions are popular for their distinctive aroma, rich phytochemical content, and diverse biological activities, yet comprehensive profiling of phytochemical composition and volatile metabolites remains limited. This study evaluates total phenols, flavonoids, anthocyanins, carotenoids, and antioxidant activities across three colored onion bulb types. Headspace solid-phase microextraction coupled with GC–MS identifies volatile metabolites, while multivariate statistics, SelectKBest and LASSO feature selection, and machine-learning models classify metabolite profiles. Random forest provides the strongest classification performance and SHAP supports key biomarker metabolites.","OPEN ACCESS  \nEDITED BY  \nTushar Dhanani,  \nFlorida Agricultural and Mechanical University, United States  \nREVIEWED BY  \nYanhe Li,  \nNorth Carolina Agricultural and Technical State University, United States Azazahemad A. Kureshi,  \nPharmanza Herbal Private Limited, India Siddanagouda Shivanagoudra,  \nTexas A&M University, United States  \n*CORRESPONDENCE  \nWengang Jin  \n [jinwengang@nwafu.edu.cn](jinwengang@nwafu.edu.cn)  \nA. M. Abd El-Aty  \n [abdelaty44@hotmail.com](abdelaty44@hotmail.com)[ ](abdelaty44@hotmail.com)RECEIVED 24 February 2025 ACCEPTED 02 April 2025 PUBLISHED 22 April 2025  \nCITATION  \nCheng K, Xiao J, He J, Yang R, Pei J, Jin W and Abd El-Aty AM (2025) Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/ GC–MS-based metabolomics and machine learning.  \nFront. Nutr. 12:1582576 .  \ndoi: 10.3389/fnut.2025.1582576  \nCOPYRIGHT  \n© 2025 Cheng, Xiao, He, Yang, Pei, Jin and Abd El-Aty. 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.  \nTYPE Original Research PUBLISHED 22 April 2025  \nDOI 10.3389/fnut.2025.1582576  \nUnraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC– MS-based metabolomics and machine learning  \nKaiqi Cheng 1,2, Jingzhe Xiao 2, Jingyuan He 2, Rongguang Yang 2, Jinjin Pei 1,2, Wengang Jin 1,2* and A. M. Abd El-Aty3,4*  \n1Qinba State Key Laboratory of Biological Resource and Ecological Environment (Incubation), Collaborative Innovation Center of Bio-Resource in Qinba Mountain Area, Shaanxi University of Technology, Hanzhong, China, 2 Key Laboratory of Bio-Resources of Shaanxi Province, School of Bioscience and Engineering, Shaanxi University of Technology, Hanzhong, China, 3 Department of Pharmacology, Faculty of Veterinary Medicine, Cairo University, Giza, Egypt, 4 Department of Medical Pharmacology, Medical Faculty, Ataturk University, Erzurum, Türkiye  \nIntroduction: Colored onions are favored by consumers due to their distinctive aroma, rich phytochemical content, and diverse biological activities. However, comprehensive analyses of their phytochemical profiles and volatile metabolites remain limited.  \nMethods: In this study, total phenols, flavonoids, anthocyanins, carotenoids, and antioxidant activities of three colored onion bulbs were evaluated. Volatile metabolites were identified using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) . Multivariate statistical analyses, feature selection techniques (SelectKBest, LASSO), and machine learning models were applied to further analyze and classify the metabolite profiles.  \nResults: Significant differences in phytochemical composition and antioxidant activities were observed among the three onion types. A total of 243 volatile metabolites were detected, with sulfur compounds accounting for 51-64%, followed by organic acids and their derivatives (4-19%) . Multivariate analysis revealed distinct volatile profiles, and 19 key metabolites were identified as biomarkers. Additionally, 33 and 38 feature metabolites were selected by SelectKBestand LASSO, respectively. The 38 features selected by LASSO enabled clear differentiation of onion types via PCA, UMAP, and k-means clustering. Among the four machine learning models tested, the random forest model achieved the highest classification accuracy (1 .00) . SHAP analysis further confirmed 20 metabolites as potential key markers.  \nConclusion: The findings suggest that the combination of HS-SPME/GC-MSand machine learning, particularly the random forest algorithm, is a powerful approa","cbCaiaUPY2yQXlEL","https://ap.wps.com/l/cbCaiaUPY2yQXlEL","pdf",2430544,1,13,"English","en",105,"# Introduction\n## Onion color diversity and bioactive compounds\n## Volatile odor compounds and prior HS-SPME/GC–MS studies\n## Metabolomics as an emerging approach\n# Methods\n## Phytochemical and antioxidant assays\n## HS-SPME/GC–MS volatile metabolite identification\n## Multivariate statistics and machine learning workflows\n# Results\n## Differences in phytochemical composition and antioxidant activity\n## Volatile metabolite profiling and biomarker discovery\n## Feature selection and classification using PCA/UMAP/k-means\n## Machine learning model performance and SHAP interpretation\n# Conclusion","[{\"question\":\"What analytical approach was used to identify volatile metabolites in pigmented onion bulbs?\",\"answer\":\"Volatile metabolites were identified using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC–MS).\"},{\"question\":\"How were phytochemical components and antioxidant activities assessed in the study?\",\"answer\":\"The study evaluated total phenols, flavonoids, anthocyanins, carotenoids, and antioxidant activities across three colored onion bulb types.\"},{\"question\":\"Which machine learning model achieved the best classification accuracy?\",\"answer\":\"Among the tested models, the random forest model achieved the highest classification accuracy, reported as 1.00, and SHAP analysis supported key metabolite markers.\"}]","Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC–MS-based metabolomics and machine learning | 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