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
Abstract
1. Introduction
2. Materials and Methods
2.1. Tea Samples and Chemical Reagents
2.2. Electronic Tongue Sensory Analysis
2.3. Electronic Nose Sensory Analysis
2.4. HS-GC-IMS Analytical Method
2.5. Qualitative and Quantitative Analysis
2.6. Feature Selection and Extraction of Key Flavor Attributes in Green Teas of Different Leaf Maturity Stages
2.7. Data Analysis
3. Results and Discussion
3.1. Electronic Tongue Analysis
3.2. Electronic Nose Analysis
3.3. HS-GC-IMS Analysis
3.3.1. Qualitative and Quantitative VOC Profiling
3.3.2. Analysis of VOCs Variation Among FDG of Different Tenderness
3.3.3. Multivariate Statistical Analysis
3.3.4. rOAV Analysis
3.3.5. KEGG Functional Annotation and Enrichment of VOCs
3.4. Identification of Key Flavor Markers of Green Teas of Different Tenderness Based on Six Machine Learning Models
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
FDG | Fudingdabai green tea |
FDQSG | Fuding Que She |
FDMJ1G | Fuding Maojian |
FDTC2G | Fuding Taicha |
ML | machine learning |
RF | random forest |
SVM | support vector machine |
KNN | k-nearest neighbor |
GNB | Gaussian naïve Bayes |
LR | logistic regression |
DT | decision tree |
VOCs | volatile organic compounds |
ROC | receiver operating characteristic |
SHAP | Shapley Additive Explanations |
AUC | the area under the receiver operating characteristic curve |
rOAV | relative odor activity value |
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NO. | Volatile Compounds | Odor Type | Chemical Classes | OT (μg/L) | rOAV | ||
---|---|---|---|---|---|---|---|
FDQSG | FDMJ1G | FDTC2G | |||||
1 | Linalool | floral | Terpenes | 0.22 | 3.8 × 103 | 7.4 × 103 | 6.3 × 103 |
2 | trans-linalool oxide | floral | Terpenes | 60 | 4.2 | 3.5 | 4.0 |
3 | Nonanal-M | aldehydic | Aldehydes | 1.1 | 9.1 × 102 | 8.1 × 102 | 8.6 × 102 |
4 | Nonanal-D | aldehydic | Aldehydes | 1.1 | 2.1 × 102 | 1.7 × 102 | 2.0 × 102 |
5 | (E)-2-octenal | fatty | Aldehydes | 3.0 | 35 | 16 | 21 |
6 | Octanal-M | aldehydic | Aldehydes | 0.587 | 389 | 343 | 422 |
7 | Heptanal-M | green | Aldehydes | 2.80 | 135 | 166 | 171 |
8 | Heptanal-D | green | Aldehydes | 2.80 | 736 | 514 | 423 |
9 | (E)-hept-2-enal | green | Aldehydes | 13 | 5.6 | 0.95 | 0.89 |
10 | Octanal-D | aldehydic | Aldehydes | 0.587 | 52.7 | 28.6 | 44.8 |
11 | Pentanal | fermented | Aldehydes | 12 | 66 | 45 | 25 |
12 | 2-methylbutanal | cocoa | Aldehydes | 1.0 | 1.1 × 103 | 1.3 × 103 | 1.4 × 103 |
13 | 3-methylbutanal | aldehydic | Aldehydes | 1.1 | 5.8 × 102 | 7.3 × 102 | 6.9 × 102 |
14 | Butanal | chocolate | Aldehydes | 2.0 | 69 | 94 | 45 |
15 | Hexanal | green | Aldehydes | 2.4 | 1.7 × 102 | 71 | 41 |
16 | (E)-2-hexenal | green | Aldehydes | 88.7 | 5.96 | 3.43 | 2.62 |
17 | methyl-5-hepten-2-one | citrus | Ketones | 68 | 8.5 | 8.8 | 7.6 |
18 | 2-Octanone | earthy | Ketones | 50.2 | 2.74 | 0.71 | 0.92 |
19 | 3-hydroxybutan-2-one | buttery | Ketones | 14 | 9.7 | 9.5 | 7.0 |
20 | 2-Propanone | solvent | Ketones | 832 | 4.28 | 5.33 | 4.53 |
21 | 1-penten-3-one | spicy | Ketones | 23 | 5.9 | 1.4 | 1.1 |
22 | 3-Pentanone | ethereal | Ketones | 40 | 3.0 | 1.9 | 1.5 |
23 | n-Hexanol-M | herbal | Alcohols | 5.6 | 42.3 | 46.1 | 39.4 |
24 | (Z)-3-hexen-1-ol | green | Alcohols | 3.9 | 67 | 59 | 62 |
25 | n-Hexanol-D | herbal | Alcohols | 5.6 | 7.6 | 7.7 | 6.5 |
26 | 1.8-Cineole | herbal | Alcohols | 1.10 | 11.7 | 11.4 | 12.5 |
27 | oct-1-en-3-ol | earthy | Alcohols | 1.5 | 33 | 22 | 17 |
28 | pentan-1-ol-M | fermented | Alcohols | 150.2 | 2.30 | 2.40 | 2.41 |
29 | pentan-1-ol-D | fermented | Alcohols | 150.2 | 1.56 | 1.66 | 1.66 |
30 | 2-Methylbutanol acetate | fruity | Esters | 5.00 | 25.9 | 2.58 | 1.83 |
31 | Propyl isovalerate | fruity | Esters | 8.70 | 1.61 | 1.21 | 0.96 |
32 | Ethyl Acetate | ethereal | Esters | 5.00 | 54.5 | 95.9 | 92.3 |
33 | methyl acetate | ethereal | Esters | 1500 | 1.080 | 1.070 | 1.430 |
34 | 2-pentyl furan | fruity | Furans | 5.80 | 19.0 | 13.8 | 11.6 |
35 | 2-n-Butylfuran | spicy | Furans | 5.0 | 5.8 | 3.4 | 3.0 |
36 | Methyl Salicylate | minty | Aromatics | 40 | 3.8 | 5.3 | 4.1 |
37 | benzene acetaldehyde | green | Aromatics | 6.3 | 9.2 | 7.6 | 14 |
38 | 3-methylthiopropanal | vegetable | Sulfur-containing Compounds | 0.45 | 8.5 | 9.3 | 5.9 |
39 | Ethylsulfide | coffee, meat | Sulfur-containing Compounds | 4.8 | 3.6 | 3.7 | 5.2 |
40 | 2-Ethyl-3,5-dimethylpyrazine | nutty | Pyrazines | 0.040 | 1.42 × 103 | 1.44 × 103 | 1.35 × 103 |
41 | 2-acetyl-1-pyrroline | popcorn | Other Heterocyclics | 0.12 | 57 | 66 | 1.6 × 102 |
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Liu, X.; Huang, M.; Tang, W.; Li, Y.; Li, L.; Xie, J.; Li, X.; Dong, F.; Wang, M. 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. Foods 2025, 14, 2861. https://doi.org/10.3390/foods14162861
Liu X, Huang M, Tang W, Li Y, Li L, Xie J, Li X, Dong F, Wang M. 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. Foods. 2025; 14(16):2861. https://doi.org/10.3390/foods14162861
Chicago/Turabian StyleLiu, Xiaohui, Mingzheng Huang, Weiyuan Tang, Yucai Li, Lun Li, Jinyi Xie, Xiangdong Li, Fabao Dong, and Maosheng Wang. 2025. "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" Foods 14, no. 16: 2861. https://doi.org/10.3390/foods14162861
APA StyleLiu, X., Huang, M., Tang, W., Li, Y., Li, L., Xie, J., Li, X., Dong, F., & Wang, M. (2025). 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. Foods, 14(16), 2861. https://doi.org/10.3390/foods14162861