ArticleScientific reports2022
Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Visualization of Moisture Distribution in Stacked Tea Leaves on Process Flow Line Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2025Article
- Evaluation of Withering Quality of Black Tea Based on Multi-Information Fusion Strategy.Foods (Basel, Switzerland) · 2025Article
- Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.Foods (Basel, Switzerland) · 2024Article
- Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology.Foods (Basel, Switzerland) · 2023Review
- NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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Abstract
Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line = 0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (R
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