Feature‑Optimized Electronic‑Nose Classification of Fermented and Non‑Fermented Teas
DOI:
https://doi.org/10.48048/tis.2026.11396Keywords:
E-nose, Feature selection, Fermented tea, Non-fermented tea, LDA, Tea classification, Silhouette scoreAbstract
Fermentation plays a crucial role in establishing the extraordinary variety of flavors, fragrances, colors, nutritional attributes, and health advantages associated with teas. Considering the substantial disparities in quality and consumer preferences, it is essential to differentiate between fermented (yellow, red, and black) and non-fermented (green) teas for quality assurance and market viability. This study proposes a method to enhance tea classification performance using an electronic nose (e-nose) system. Five feature extraction methods (max, mean, median, gradient, and standard deviation) were applied to capture informative signals from the e-nose data. A silhouette score-based feature selection technique was then used to guide the determination of the optimal combination of these features. The best performance was achieved by combining the mean, median, and gradient features with a linear discriminant analysis (LDA) model, reaching a testing accuracy of 0.85, precision of 0.86, recall of 0.86, and an AUC of 0.97. The confusion matrix indicated perfect classification for green tea, with only minor misclassifications between red and black teas. To validate the e-nose results, gas chromatography-mass spectrometry (GC-MS) was employed. It identified key marker compounds for differentiating tea types. For instance, (1R)-4,7,7-trimethylbicyclo[2.2.1]heptan-2-one was found exclusively in yellow, red, and black teas, while nickel tetracarbonyl was unique to green tea. Overall, the study highlights the benefits of using feature selection to enhance e-nose classification performance and supports its use as a reliable, non-invasive tool for distinguishing fermented and non-fermented teas.
HIGHLIGHTS
- E-nose and LDA used to classify fermented vs non-fermented teas.
- Feature selection guided by silhouette score improves classification.
- GC-MS confirms e-nose findings in distinguishing tea fermentation types.
GRAPHICAL ABSTRACT
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