Advancing tea detection with artificial intelligence: Strategies, progress, and future prospects

Xu, Qilin; Zhou, Yifeng; Wu, Linlin

doi:10.1016/j.tifs.2024.104731

Trends in Food Science & Technology

2024

DOI: 10.1016/j.tifs.2024.104731

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Advancing tea detection with artificial intelligence: Strategies, progress, and future prospects

Qilin Xu,

Yifeng Zhou,

Linlin Wu

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2024

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LCLN-CA: A Survival Regression Analysis-Based Prediction Method for Catechin Content in Yunnan Sun-Dried Tea

Li,

Wang,

Wang

et al. 2024

Horticulturae

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Catechins are pivotal determinants of tea quality, with soil environmental factors playing a crucial role in the synthesis and accumulation of these compounds. To investigate the impact of changes in tea garden soil environments on the catechin content in sun-dried tea, this study measured the catechin content in soil samples and corresponding tea leaves from Nanhua, Yunnan, China. By integrating the variations in catechin content with those of 17 soil factors and employing COX regression factor analysis, it was found that pH, organic matter (OM), fluoride, arsenic (As), and chromium (Cr) were significantly correlated with catechin content (p < 0.05). Further, using the LASSO regression for variable selection, a model named LCLN-CA was constructed with four variables including pH, OM, fluoride, and As. The LCLN-CA model demonstrated high fitting accuracy with AUC values of 0.674, 0.784, and 0.749 for catechin content intervals of CA ≤ 10%, 10% < CA ≤ 20%, and 20% < CA ≤ 30% in the training set, respectively. The validation set showed AUC values of 0.630, 0.756, and 0.723, respectively, indicating a well-calibrated curve. Based on the LCLN-CA model and the DynNom framework, a visual prediction system for catechin content in Yunnan sun-dried tea was developed. External validation with a test dataset achieved an Accuracy of 0.870. This study explored the relationship between soil-related factors and variations in catechin content, paving a new way for the prediction of catechin content in tea and enhancing the practical application value of artificial intelligence technology in agricultural production.

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LCLN-CA: A Survival Regression Analysis-Based Prediction Method for Catechin Content in Yunnan Sun-Dried Tea

Li,

Wang,

Wang

et al. 2024

Horticulturae

View full text Add to dashboard Cite

show abstract

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Advancing tea detection with artificial intelligence: Strategies, progress, and future prospects

Cited by 1 publication

References 153 publications

LCLN-CA: A Survival Regression Analysis-Based Prediction Method for Catechin Content in Yunnan Sun-Dried Tea

LCLN-CA: A Survival Regression Analysis-Based Prediction Method for Catechin Content in Yunnan Sun-Dried Tea

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