What question did this study set out to answer?

The study aims to enhance tea classification across different domains using E-nose signals and a novel deep learning approach.

April 17, 2026Open Access

Multi-Granularity Domain Adversarial Learning for Cross-Domain Tea Classification Using Electronic Nose Signals

Key Points

The study aims to enhance tea classification across different domains using E-nose signals and a novel deep learning approach.
Developed MGDA-Net with CNN and self-attention branches for feature extraction.
Employed a branch-level adversarial alignment strategy to reduce discrepancies across domains.
Utilized a three-stage training process involving source pretraining, adversarial alignment, and target fine-tuning.
MGDA-Net achieved mean accuracies of 99.31% and 99.38% for oolong and jasmine tea classifications respectively.
Demonstrated consistent performance even with limited label data, maintaining above 87% accuracy.
Outperformed all baseline methods in comparative experiments.

Abstract

Rapid and reliable tea classification is valuable for routine product screening, yet conventional sensory or physicochemical methods are subjective or time-consuming. Electronic nose (E-nose) sensing provides a fast alternative, but performance often degrades under domain shifts caused by different tea types, commercial categories, or acquisition conditions. This study proposes MGDA-Net, a multi-granularity domain adversarial network for cross-domain tea classification using E-nose time-series signals. MGDA-Net learns local temporal dynamics via a CNN branch and global contextual dependencies via a self-attention branch, and fuses them through an adaptive gating module. A branch-level adversarial alignment strategy is introduced to reduce source–target discrepancy at both local and global feature levels. A three-stage training procedure, consisting of source pretraining, adversarial alignment, and target fine-tuning, enables knowledge transfer from a labeled green tea source-domain to two target tasks. Experiments on oolong tea commercial-category classification (6 classes) and jasmine tea retail price-level classification (8 classes) show that MGDA-Net achieves mean accuracies of 99.31 ± 0.69% and 99.38 ± 0.51% over 10 independent runs, substantially outperforming all compared baseline methods. Ablation studies, feature-space analyses, and label-efficiency experiments further confirm the contribution of each component and show that MGDA-Net maintains mean accuracies above 87% when only 40% of the target-domain labels are used for fine-tuning. These findings suggest that MGDA-Net is a promising approach for cross-domain tea classification using E-nose data.

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