A black tea quality testing method for scale production using CV and NIRS with TCN for spectral feature extraction

To rigorously assess black tea quality in large-scale production, this study introduces a multi-modal fusion approach integrating computer vision (CV) with Near-Infrared Spectroscopy (NIRS). CV technology is first applied to evaluate the tea's appearance quality, while NIRS quantifies key chemical components, including tea polyphenols (TP), free amino acids (FAA), and caffeine (CAF). Additionally, different methods are employed to extract potential quality features from NIR spectra. The information are then fused, and a classifier is utilized to accurately identify tea quality. Results show that the Temporal Convolutional Network (TCN) fused model achieves a 98.2 % accuracy rate, surpassing both the Convolutional Neural Network (CNN) fused model and traditional methods. This study demonstrates that TCNs effectively extract spectral features and that data fusion significantly enhances tea quality testing, offering valuable insights for production optimization. •Fusing potential spectral quality features extracted by TCN with appearance quality and taste factors to test tea quality.•TCN outperforms CNNs in extracting potential features from NIR spectra.•The constructed fused model achieves a high accuracy of 98.2 %.