文献类型：期刊文献

英文题名：Characterization and Exploration of the Flavor Profiles of Green Teas from Different Leaf Maturity Stages of Camellia sinensis cv. Fudingdabai Using E-Nose, E-Tongue, and HS-GC-IMS Combined with Machine Learning

作者：Liu, Xiaohui Huang, Mingzheng Tang, Weiyuan Li, Yucai Li, Lun Xie, Jinyi Li, Xiangdong Dong, Fabao Wang, Maosheng

第一作者：刘晓辉

通信作者：Dong, FB[1];Wang, MS[1]

机构：[1]Guizhou Inst Technol, Coll Food & Pharmaceut Engn, Guiyang 550025, Peoples R China

第一机构：贵州理工学院食品药品制造工程学院

通信机构：corresponding author), Guizhou Inst Technol, Coll Food & Pharmaceut Engn, Guiyang 550025, Peoples R China.|贵州理工学院食品药品制造工程学院;贵州理工学院;

年份：2025

卷号：14

期号：16

外文期刊名：FOODS

收录：;Scopus(收录号：2-s2.0-105014442373);WOS:【SCI-EXPANDED(收录号:WOS:001559955600001)】；

基金：This research was funded by the Science and Technology Plan Project of Guizhou province, grant number QKHJC-ZK[2022] YB180 and QKHJC MS[2025] 196, the Young Scientists Fund of the National Natural Science Foundation of China, grant number 32001463, the Guizhou Institute of Technology High-Level Talent Scientific Research Startup Project, grant number XJGC20190924, the Science and Technology Plan Project of Guizhou province, grant number QKHZ[2023] YB064, Guizhou Fruit Wine Brewing Engineering Research Center, grant number [2022]050, and Guizhou Provincial Science and Technology Department, grant number KXJZ[2024]021. The APC was funded by the Science and Technology Plan Project of Guizhou province, grant number QKHJC-ZK[2022] YB180.

语种：英文

外文关键词：green tea; leaf tenderness; volatile compounds; HS-GC-IMS; rOAV; machine learning; Fudingdabai; KEGG pathway; flavor markers; tea grading

摘要：Understanding how leaf maturity affects the flavor attributes of green tea is crucial for optimizing harvest timing and processing strategies. This study comprehensively characterized the flavor profiles of Fudingdabai green teas at three distinct leaf maturity stages-single bud (FDQSG), one bud + one leaf (FDMJ1G), and one bud + two leaves (FDTC2G)-using a multimodal approach integrating electronic nose, electronic tongue, HS-GC-IMS, relative odor activity value (rOAV) evaluation, and machine learning algorithms. A total of 85 volatile compounds (VOCs) were identified, of which 41 had rOAV > 1. Notably, 2-methylbutanal, 2-ethyl-3,5-dimethylpyrazine, and linalool exhibited extremely high rOAVs (>1000). FDQSG was enriched with LOX (lipoxygenase)-derived fresh, grassy volatiles such as (Z)-3-hexen-1-ol and nonanal. FDMJ1G showed a pronounced accumulation of floral and fruity compounds, especially linalool (rOAV: 7400), while FDTC2G featured Maillard- and phenylalanine-derived volatiles like benzene acetaldehyde and 2,5-dimethylfuran, contributing to roasted and cocoa-like aromas. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed significant enrichment in butanoate metabolism and monoterpenoid biosynthesis. Random forest-SHAP analysis identified 20 key flavor markers, mostly VOCs, that effectively discriminated samples by tenderness grade. ROC-AUC validation further confirmed their diagnostic performance (accuracy >= 0.8). These findings provide a scientific basis for flavor-driven harvest management and the quality-oriented grading of Fudingdaibai green tea.