Xiangdong Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Xiangdong Li has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Fabao Dong
College of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China
2 shared publications - Jinyi Xie
College of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China
2 shared publications - Lun Li
College of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China
2 shared publications - Maosheng Wang
College of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China
2 shared publications - Mingzheng Huang
College of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China
2 shared publications - Weiyuan Tang
College of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang 550025, China
2 shared publications
Latest Publications
- Open Access
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
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.
Open AccessCharacterization 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
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.
- Open Access
- 28 July 2025
- Published by MDPI AG in Preprints.org
Characterize and Explore the Flavor Profiles of Green Teas from Different Tenderness Levels of Camellia sinensis cv. Fudingdabai by E-Nose, E-Tongue and HS-GC-IMS Combined with Machine Learning
Understanding how leaf tenderness influences flavor attributes in green tea is crucial for optimizing harvest timing and processing strategies. This study comprehensively characterized the flavor profiles of Fudingdabai green teas at three tenderness levels—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. FDQSG was dominated by fresh, grassy, and herbal volatiles such as (Z)-3-hexen-1-ol and nonanal, suggesting active LOX pathways. FDMJ1G showed a metabolic surge in floral compounds, especially linalool (rOAV > 7400), along with minty and fruity notes. FDTC2G featured roasted and cocoa-like aromas due to enhanced Maillard reactions and phenylalanine metabolism. KEGG 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.
- 28 July 2025
- Published by MDPI AG in Preprints.org
Open AccessCharacterize and Explore the Flavor Profiles of Green Teas from Different Tenderness Levels of Camellia sinensis cv. Fudingdabai by E-Nose, E-Tongue and HS-GC-IMS Combined with Machine Learning
Understanding how leaf tenderness influences flavor attributes in green tea is crucial for optimizing harvest timing and processing strategies. This study comprehensively characterized the flavor profiles of Fudingdabai green teas at three tenderness levels—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. FDQSG was dominated by fresh, grassy, and herbal volatiles such as (Z)-3-hexen-1-ol and nonanal, suggesting active LOX pathways. FDMJ1G showed a metabolic surge in floral compounds, especially linalool (rOAV > 7400), along with minty and fruity notes. FDTC2G featured roasted and cocoa-like aromas due to enhanced Maillard reactions and phenylalanine metabolism. KEGG 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.