ArticleFoods (Basel, Switzerland)2021
A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose.
Article in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Leakage-Free Benchmarking of Electronic Noses for Beef Freshness: A Signal-Richness Criterion for Model Selection.Foods (Basel, Switzerland) · 2026Article
- Multi-Granularity Domain Adversarial Learning for Cross-Domain Tea Classification Using Electronic Nose Signals.Foods (Basel, Switzerland) · 2026Article
- Characterization of Aroma-Active Compounds in Five Dry-Cured Hams Based on Electronic Nose and GC-MS-Olfactometry Combined with Odor Description, Intensity, and Hedonic Assessment.Foods (Basel, Switzerland) · 2025Article
- A Gas Sensors Detection System for Real-Time Monitoring of Changes in Volatile Organic Compounds during Oolong Tea Processing.Foods (Basel, Switzerland) · 2024Article
- Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market.Foods (Basel, Switzerland) · 2023Article
- Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data.Nutrients · 2023Article
- Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases.Microorganisms · 2023Review
- Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis.Foods (Basel, Switzerland) · 2023Article
- Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Review
- A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.Foods (Basel, Switzerland) · 2022Article
- A Transfer Learning Framework with a One-Dimensional Deep Subdomain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions.Sensors (Basel, Switzerland) · 2022Article
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.