Identification and sorting of impurities in tea using spectral vision

• Published in: Food science & technology Vol. 205; p. 116519
• Main Authors: Guo, Yuchen, Yu, Ziru, Yu, Xiangyang, Wang, Xiaoqing, Cai, Yefan, Hong, Weibin, Cui, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Hyperspectral imaging; Impurity identification; Machine learning; Spectral vision; Tea; Impurity identification; Tea; Spectral vision; Machine learning; Hyperspectral imaging
• ISSN: 0023-6438
• DOI: 10.1016/j.lwt.2024.116519

Removing impurities from Puer tea is an important step in its processing. However, existing methods are unable to accurately and efficiently sort impurities from Puer tea. This study proposes a novel method based on spectral imaging technology to identify impurities in Puer tea. Hyperspectral images of tea and impurities were acquired in the 400–1000 nm wavelengths. The spectra of each category of samples were extracted from the hyperspectral images and comprehensively analysed and processed, mainly including outlier detection and elimination, spectral preprocessing, and characteristic wavelengths selection. Support vector machine (SVM) models are built based on full-spectrum data and characteristic wavelengths data to achieve pixel-level classification of hyperspectral images. The results showed that the best model based on full spectra achieved an accuracy of 97.8% on the test dataset. The best models based on characteristic wavelengths selected by successive projections algorithm and genetic algorithm achieved accuracies of 95.9% and 94.9%, respectively. Finally, these pixel classification models were applied to the test samples to test the effectiveness of the models in identifying impurities. The results indicate that the three selected models can effectively identify impurities and even perform well on identifying unfamiliar categories of impurities. •Using hyperspectral imaging techniques to identify impurities in Puer tea.•Characteristic wavelengths were extracted before modeling.•Pixel classification models based on SVM were built (the best accuracy is 97.8%).•Sample's category determined by labels distribution of all pixels in its area.•Capable of accurately identifying and locating various impurities in Puer tea.