Non-Destructive Detection of Tea Polyphenols in Fu Brick Tea Based on Hyperspectral Imaging and Improved PKO-SVR Method

Junyao Gong, Gang Chen, Yuezhao Deng, Cheng Li and Kui Fang ()
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Junyao Gong: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
Gang Chen: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
Yuezhao Deng: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
Cheng Li: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
Kui Fang: College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China

Agriculture, 2024, vol. 14, issue 10, 1-23

Abstract: Tea polyphenols (TPs) are a critical indicator for evaluating the quality of tea leaves and are esteemed for their beneficial effects. The non-destructive detection of this component is essential for enhancing precise control in tea production and improving product quality. This study developed an enhanced PKO-SVR (support vector regression based on the Pied Kingfisher Optimization Algorithm) model for rapidly and accurately detecting tea polyphenol content in Fu brick tea using hyperspectral reflectance data. During this experiment, chemical analysis determined the tea polyphenol content, while hyperspectral imaging captured the spectral data. Data preprocessing techniques were applied to reduce noise interference and improve the prediction model. Additionally, several other models, including K-nearest neighbor (KNN) regression, neural network regression (BP), support vector regression based on the sparrow algorithm (SSA-SVR), and support vector regression based on particle swarm optimization (PSO-SVR), were established for comparison. The experiment results demonstrated that the improved PKO-SVR model excelled in predicting the polyphenol content of Fu brick tea (R 2 = 0.9152, RMSE = 0.5876, RPD = 3.4345 for the test set) and also exhibited a faster convergence rate. Therefore, the hyperspectral data combined with the PKO-SVR algorithm presented in this study proved effective for evaluating Fu brick tea’s polyphenol content.

Keywords: PKO-SVR model; hyperspectral image technology; fast; non-destructive detection; Fu brick tea; tea polyphenols (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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