ArticleFood chemistry: X2024
Monitoring quality changes in green tea during storage: A hyperspectral imaging method.
Article in Food chemistry: X, 2024. 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.
- 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
- Rapid assessment of green tea storage quality using copper-amino acid nanozyme sensor arrays.Food chemistry: X · 2026Article
- Investigation and Rapid Determination of Storage-Induced Multidimensional Quality Deterioration of Wenyujin Rhizoma Concisum.Foods (Basel, Switzerland) · 2026Article
- A Model for Identifying the Fermentation Degree of Tieguanyin Oolong Tea Based on RGB Image and Hyperspectral Data.Foods (Basel, Switzerland) · 2026Article
- Non-Destructive Sensing of Tea Pigments in Black Tea Rolling Process.Foods (Basel, Switzerland) · 2025Article
- Rapid detection of synthetic pigments in black tea using hyperspectral imaging technology and machine learning.Food chemistry: X · 2025Article
- Traceability of Rizhao green tea origin based on multispectral data fusion strategy and chemometrics.Food chemistry: X · 2025Article
- High-throughput quantification of aflatoxin in moldy peanuts using hyperspectral imaging and CNN: Comparative evaluation of machine learning algorithms and deep learning models.Current research in food science · 2025Article
- Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The quality of green tea deteriorates the longer it is stored. However, there is a lack of accurate and rapid methods for determining the storage period of tea. In this study, hyperspectral imaging (HSI) was used to determine the storage period of green teas stored at 4 °C (set 1) and 25 °C (set 2), and to quantify and visualize the main chemical components (e.g. catechins). In this study, three prediction algorithms were compared, in which partial least squares discriminant analysis outperformed the other models in qualitative discrimination, with 98% and 96% correct discrimination for two sets, respectively. Moreover, quantitative models for ester catechins, simple catechins, and total catechins were developed with Rp > 0.90 and RPD > 1.0, indicating that the models were reliable. Further, a more intuitive visualization of catechin content was achieved. In conclusion, the HSI provides a rapid, non-destructive method to determine the freshness of stored green tea.
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