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Geographical origin traceability of green tea based on terahertz time-domain spectroscopy coupled with an optimized
Lihua Hao1, Jiatao Li2, Zhiwei Hou3
1Henan Institute of Product Quality Supervision and Inspection Technology, Zhengzhou 450000, China.
Summary
Terahertz time-domain spectroscopy (THz-TDS) combined with machine learning effectively traced the origin of Xinyang Maojian green tea. This method enhances geographical indication (GI) protection for valuable teas.
Area of Science:
- Agricultural Science
- Spectroscopy
- Data Science
Background:
- Geographical origin traceability is crucial for maintaining the market value and Geographical Indication (GI) status of premium green teas.
- Xinyang Maojian green tea serves as a model for exploring advanced traceability techniques.
Purpose of the Study:
- To investigate the feasibility of using terahertz time-domain spectroscopy (THz-TDS) combined with machine learning for discriminating the geographical origin of Xinyang Maojian green tea.
- To compare various spectral preprocessing strategies, feature selection algorithms, and classifiers for optimal origin discrimination.
Main Methods:
- Terahertz time-domain spectroscopy (THz-TDS) was employed to collect spectral data from Xinyang Maojian green tea samples.
- Multiple spectral preprocessing techniques, four feature selection algorithms (including Recursive Feature Elimination - RFE), and various classifiers (including Support Vector Machine - SVM) were evaluated.
- SHAP (SHapley Additive exPlanations) analysis was used to interpret the model's classification decisions.
Main Results:
- The combination of raw spectral data and SVM classification (Raw + SVM) performed best among full-frequency models.
- Recursive Feature Elimination (RFE) combined with SVM (RFE-SVM) proved to be the most effective feature selection strategy, identifying 110 informative frequencies.
- The final Raw + RFE-SVM model achieved high accuracy (0.8833) and Macro F1-score (0.8819) on an independent test set.
- SHAP analysis identified specific low-frequency bands (e.g., 0.2167 THz) as potential spectral indicators for origin classification.
Conclusions:
- THz-TDS coupled with RFE-based feature selection and SVM classification demonstrates significant potential for the geographical origin traceability of Xinyang Maojian green tea.
- This approach can aid in safeguarding the authenticity and market value of GI-protected agricultural products.
- Specific spectral features in the terahertz range can serve as reliable indicators for tea origin discrimination.
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