ArticleJournal of analytical methods in chemistry2019
Tracing Geographical Origins of Teas Based on FT-NIR Spectroscopy: Introduction of Model Updating and Imbalanced Data Handling Approaches.
Article in Journal of analytical methods in chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Geographical Origin Traceability of Tea (Foods (Basel, Switzerland) · 2026Review
- Highly efficient adsorptive removal of the carcinogen aflatoxin BEnvironmental science and pollution research international · 2025Article
- Rapid discrimination and quantification of chemotypes in Perillae folium using FT-NIR spectroscopy and GC-MS combined with chemometrics.Food chemistry: X · 2024Article
- Characterization of Mineral Composition and Nutritional Value ofPlants (Basel, Switzerland) · 2023Article
- Rapid detection of adulteration in powder of ginger (Food chemistry: X · 2022Article
- Article
- Comparative Determination of Phenolic Compounds inPlants (Basel, Switzerland) · 2022Article
- Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.Frontiers in nutrition · 2021Review
- Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy.Journal of analytical methods in chemistry · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work presents a reliable approach to trace teas' geographical origins despite changes in teas caused by different harvest years. A total of 1447 tea samples collected from various areas in 2014 (660 samples) and 2015 (787 samples) were detected by FT-NIR. Seven classifiers trained on the 2014 dataset all succeeded to trace origins of samples collected in 2014; however, they all failed to predict origins for the 2015 samples due to different data distributions and imbalanced dataset. Three outlier detection based undersampling approaches-one-class SVM (OC-SVM), isolation forest and elliptic envelope-were then proposed; as a result, the highest macro average recall (MAR) for the 2015 dataset was improved from 56.86% to 73.95% (by SVM). A model updating approach was also applied, and the prediction MAR was significantly improved with increase in the updating rate. The best MAR (90.31%) was first achieved by the OC-SVM combined SVM classifier at a 50% rate.
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