ArticleFoods (Basel, Switzerland)2024
Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible-Near-Infrared Spectroscopy and Image-Processing Technologies.
Article in Foods (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Advances in Solid-State Fermentation Technology for Oilseed Meal: Strain Selection, Fermentation Strategies, and High-Value Applications.Foods (Basel, Switzerland) · 2026Review
- Digital Sensing for Comprehensive Tea Quality Evaluation: From Dry Tea to Tea Infusion and Infused Leaves.Comprehensive reviews in food science and food safety · 2026Review
- Computer Vision from Tea Cultivation to Quality Evaluation.Foods (Basel, Switzerland) · 2026Review
- Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database.BMC medical informatics and decision making · 2026Article
- Machine learning-assisted Raman spectroscopy for non-destructive analysis of crude palm oil quality.NPJ science of food · 2026Article
- A Model for Identifying the Fermentation Degree of Tieguanyin Oolong Tea Based on RGB Image and Hyperspectral Data.Foods (Basel, Switzerland) · 2026Article
- Green-Making Stage Recognition of Tieguanyin Tea Based on Improved MobileNet V3.Sensors (Basel, Switzerland) · 2026Article
- Intelligent Discrimination of Grain Aging Using Volatile Organic Compound Fingerprints and Machine Learning: A Comprehensive Review.Foods (Basel, Switzerland) · 2026Review
- Deep learning in food quality and safety management: A review of architectures, applications, and future directions.Current research in food science · 2026Review
- Edge Computing-Enabled Smart Agriculture: Technical Architectures, Practical Evolution, and Bottleneck Breakthroughs.Sensors (Basel, Switzerland) · 2025Review
- Application of Image Computing in Non-Destructive Detection of Chinese Cuisine.Foods (Basel, Switzerland) · 2025Review
- Deep Learning-Enhanced Spectroscopic Technologies for Food Quality Assessment: Convergence and Emerging Frontiers.Foods (Basel, Switzerland) · 2025Review
- Terahertz Spectroscopy for Food Quality Assessment: A Comprehensive Review.Foods (Basel, Switzerland) · 2025Review
- The Fermentation Degree Prediction Model for Tieguanyin Oolong Tea Based on Visual and Sensing Technologies.Foods (Basel, Switzerland) · 2025Article
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10 authors.
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Abstract
During the fermentation process of Oolong tea, significant changes occur in both its external characteristics and its internal components. This study aims to determine the fermentation degree of Oolong tea using visible-near-infrared spectroscopy (vis-VIS-NIR) and image processing. The preprocessed vis-VIS-NIR spectral data are fused with image features after sequential projection algorithm (SPA) feature selection. Subsequently, traditional machine learning and deep learning classification models are compared, with the support vector machine (SVM) and convolutional neural network (CNN) models yielding the highest prediction rates among traditional machine learning models and deep learning models with 97.14% and 95.15% in the prediction set, respectively. The results indicate that VIS-NIR combined with image processing possesses the capability for rapid non-destructive online determination of the fermentation degree of Oolong tea. Additionally, the predictive rate of traditional machine learning models exceeds that of deep learning models in this study. This study provides a theoretical basis for the fermentation of Oolong tea.
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