Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2
1. Department of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China
2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
3. Taizhou Institute of Zhejiang University, Taizhou 317700, China
4. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
Abstract：The effects of different varieties of tea are different because of their different organic chemical components. Therefore, it is essential to find a technical method that can accurately and quickly identify tea varieties. Near-infrared (NIR) spectroscopy is a nondestructive detection technology correctly identifying tea varieties. Due to noise signals in the NIR spectra of tea samples collected by the NIR spectrometer, a fuzzy linear discriminant QR analysis method was proposed to accurately identify the NIR spectra of tea samples containing noise signals. After the dimensionality of NIR spectra was compressed by principal component analysis (PCA), it was reduced using fuzzy linear discriminant analysis (FLDA). The discriminant vector matrix was constructed from the eigenvectors obtained by FLDA. The discriminant vector matrix was decomposed by QR decomposition to obtain a new discriminant vector matrix. Then, the K-nearest neighbor (KNN) algorithm was used for classification, which has the advantage of high accuracy. Four kinds of tea samples, namely Yuexi Cuilan, Lu'an Guapian, Shiji Maofeng and Huangshan Maofeng, were taken as the experimental samples. There were 65 tea samples in each category, and the total number of tea samples was 260. Firstly, the NIR spectral data of tea samples were collected by the Fourier NIR spectrometer Antaris Ⅱ. Secondly, the obtained NIR spectral data of tea were preprocessed, and the scattering effect of spectral data was reduced through multiple scattering correction. Thirdly, the dimensionality of NIR data is 1 557, so PCA was used to reduce the dimensionality of the spectra to 7. Then, fuzzy linear discriminant QR analysis was performed to extract the identification information from the compressed NIR spectra, and the dimensionality of the data was further reduced to 3 dimensions. Finally, KNN was used to classify tea samples and achieved the accurate classification of tea varieties. Furthermore, the experimental results were compared including three algorithms, which are PCA combined with KNN, PCA and linear discriminant analysis (LDA) combined with KNN, PCA and fuzzy linear discriminant QR analysis combined with KNN. Under the weight index m=2 and K=1, the final classification accuracies of the three algorithms were 83.89%, 87.78% and 98.33%, respectively. The experimental results showed that fuzzy linear discriminant QR analysis provided a method for the identification of NIR spectra of tea, and its effect was better than PCA and LDA.
Key words：Fuzzy linear discriminant analysis; Principal component analysis; Near-infrared spectroscopy; K-nearest neighbor algorithm
胡彩平，何成遇，孔丽微，朱优优，武 斌，周浩祥，孙 俊. 模糊线性判别QR分析的茶叶近红外光谱鉴别分析[J]. 光谱学与光谱分析, 2023, 43(12): 3802-3805.
HU Cai-ping, HE Cheng-yu, KONG Li-wei, ZHU You-you, WU Bin, ZHOU Hao-xiang, SUN Jun. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805.
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