Green tea grades identification via Fourier transform near‐infrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform

Abstract

The extraction of near‐infrared (NIR) spectral discrimination information is important for the NIR spectral classification task. Some discriminant information extraction algorithms such as linear discriminant analysis (LDA), uncorrelated discriminant transform (UDT), and fuzzy uncorrelated discriminant transform (FUDT) use the sample mean to calculate the total scattering matrix. However, the calculation of the sample mean will be affected by abnormal samples, which will affect the extraction of discriminant information. To solve this problem, this article proposes an improved discriminant information extraction algorithm called weighted global fuzzy uncorrelated discriminant transform (WGFUDT). The algorithm uses the Euclidean distance between the training samples to weight the training samples and assigns a smaller weight to the abnormal data to reduce its effect on the sample mean. The algorithm was used for the grade identification of two green teas (Huangshan Maofeng tea and Mee tea). The results show that the classification accuracies of WGFUDT on Mee tea and Huangshan Maofeng tea are 97.22% and 99.07%, respectively. Compared with LDA, UDT and FUDT, WGFUDT can obtain more spectral discriminant information and has higher accuracy in grade identification of Huangshan Maofeng tea and Mee tea. Practical Applications Some traditional discriminant information extraction algorithms sometimes cannot extract enough discriminant information, which will affect the recognition rate of tea grades. WGFUDT can extract more discriminant information in the face of green tea spectral information and achieve higher accuracy. WGFUDT for green tea grade identification is fast and effective.

Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades

“…This is because the tea sample contains water, and the absorption bands corresponding to hydroxyl O–H stretching and O–H deformation in water affect the spectral analysis results, and the absorption peaks near 7250 is formed by the tensile vibration absorption of hydroxyl (O–H) and nitrogen-hydrogen (N–H) bond in tea polysaccharides and hydrogen containing compounds. The last absorption peaks near 8500 are related to the vibrational stretching of the carbon hydrogen (C–H) bond in tea polyphenols and protein [ 37 , 38 ]. Comparing Fig.…”

Several Feature Extraction Methods Combined with Near-Infrared Spectroscopy for Identifying the Geographical Origins of Milk

“…When collecting spectral data, due to the impacts of measurement errors and optical scattering, the raw spectral data were mixed with a large amount of noise. To improve the accuracy of the classification, the raw spectra must be preprocessed to remove the influence of noise and scattering on the spectral data [ 38 ].…”

Fast and nondestructive discrimination of quality level of Yongchuan Xiuya tea based on near infrared spectroscopy combined with artificial neural network algorithm

“…At present, many scholars have realized the content prediction of tea polyphenols, caffeine (Sahachairungrueng et al, 2022) and other components, the rapid assessment of the quality of fresh tea leaves (Wang et al, 2013) and the discrimination of tea varieties (Ren et al, 2013) by using NIRS and a variety of chemometrics methods. In addition, NIRS combined with a variety of chemometrics methods was used to achieve rapid detection of black tea quality grade (Ren et al, 2020a, b) and Huangshan Maofeng tea and Emei tea (He et al, 2022) with an accuracy rate of 97%. However, there was no report on the rapid discrimination of Yongchuan Xiuya tea with different quality levels.…”