Multi-omics and machine learning reveal the mechanisms underlying cultivar-driven flavor differentiation in fermented ciba chili

Ciba chili quality is strongly influenced by raw material characteristics, but the cultivar-driven mechanisms underlying quality differentiation remain unclear. Ciba chili made from Inner Yellow New Generation (ICP), Lantern (LCP), and Devil's (DCP) peppers was comprehensively investigated using physicochemical analysis, GC-IMS, UHPLC-MS/MS untargeted metabolomics, 16S rRNA sequencing, and machine learning. Cultivar specificity profoundly shaped fermentation results. Volatile profiling revealed late-stage acid and ester enrichment in LCP, higher aldehyde retention alongside earlier ester formation in ICP, and an enrichment of alcohols, ketones, furans, and pyrazines in DCP. Furthermore, untargeted metabolomics showed that LCP, ICP, and DCP were enriched in organic oxygen-containing compounds, organic acids and organoheterocyclic compounds, and lipids and benzenoids, respectively. A random forest model utilizing these metabolites showed superior classification performance, and SHAP analysis further identified 20 core discriminatory metabolites contributing to cultivar classification. KEGG analysis traced flavor differentiation to phenylpropanoid metabolism, lipid oxidation, purine cofactor metabolism, and l -glutamine-mediated nitrogen metabolism. Cultivar-dependent bacterial succession was observed: DCP was dominated by Lactiplantibacillus , LCP by Weissella , and ICP showed delayed, less coordinated Weissella dominance. Crucially, correlation analysis revealed bacteria metabolite networks: LCP exhibited strong negative associations, ICP showed a balanced network linking acidification, nitrogen metabolism, and flavor precursors, while DCP displayed pronounced coupling associated with complex aromas and pungency. This study establishes an integrated multi-omics and machine learning framework to elucidate how chili cultivar drives quality differentiation, providing theoretical support for cultivar selection and flavor oriented industrial fermentation.

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Publication Details

Journal
Food Chemistry X
Published
2026-09-17
DOI
https://doi.org/10.1016/j.fochx.2026.104449
Primary Topic
Fermentation and Sensory Analysis
Type
article
Field-Weighted Citation Impact
0.00

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Multi-omics and machine learning reveal the mechanisms underlying cultivar-driven flavor differentiation in fermented ciba chili

Liang Zhang, Defu Xu, Huachang Wu, Zihao Liu et al.
Food Chemistry X
Fermentation and Sensory Analysis
article

Multi-omics and machine learning reveal the mechanisms underlying cultivar-driven flavor differentiation in fermented ciba chili

Liang Zhang, Defu Xu, Huachang Wu, Zihao Liu, Ju Guan, Bo Zu, Zhaohui Huang, Tianyang Wang, Changbo Gao, Xiaoming Chen, Yiling Xiong
article en

Abstract

Ciba chili quality is strongly influenced by raw material characteristics, but the cultivar-driven mechanisms underlying quality differentiation remain unclear. Ciba chili made from Inner Yellow New Generation (ICP), Lantern (LCP), and Devil's (DCP) peppers was comprehensively investigated using physicochemical analysis, GC-IMS, UHPLC-MS/MS untargeted metabolomics, 16S rRNA sequencing, and machine learning. Cultivar specificity profoundly shaped fermentation results. Volatile profiling revealed late-stage acid and ester enrichment in LCP, higher aldehyde retention alongside earlier ester formation in ICP, and an enrichment of alcohols, ketones, furans, and pyrazines in DCP. Furthermore, untargeted metabolomics showed that LCP, ICP, and DCP were enriched in organic oxygen-containing compounds, organic acids and organoheterocyclic compounds, and lipids and benzenoids, respectively. A random forest model utilizing these metabolites showed superior classification performance, and SHAP analysis further identified 20 core discriminatory metabolites contributing to cultivar classification. KEGG analysis traced flavor differentiation to phenylpropanoid metabolism, lipid oxidation, purine cofactor metabolism, and l -glutamine-mediated nitrogen metabolism. Cultivar-dependent bacterial succession was observed: DCP was dominated by Lactiplantibacillus , LCP by Weissella , and ICP showed delayed, less coordinated Weissella dominance. Crucially, correlation analysis revealed bacteria metabolite networks: LCP exhibited strong negative associations, ICP showed a balanced network linking acidification, nitrogen metabolism, and flavor precursors, while DCP displayed pronounced coupling associated with complex aromas and pungency. This study establishes an integrated multi-omics and machine learning framework to elucidate how chili cultivar drives quality differentiation, providing theoretical support for cultivar selection and flavor oriented industrial fermentation.

Food Chemistry XVol. 39
Southwest University of Science and Technology (CN), Shanghai Liangyou (China) (CN), Sichuan Tourism University (CN), Chengdu University (CN), Sichuan University of Science and Engineering (CN)
Department of Science and Technology of Sichuan Province
Zero hunger
Openalex Percentile: Top 15%
Fermentation and Sensory Analysis
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