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Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging
Author(s) -
Wang YuJie,
Li LuQing,
Shen ShanShan,
Liu Ying,
Ning JingMing,
Zhang ZhengZhu
Publication year - 2020
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.10393
Subject(s) - hyperspectral imaging , partial least squares regression , chemometrics , multispectral image , postharvest , mathematics , linear regression , coefficient of determination , horticulture , chemistry , statistics , computer science , biology , chromatography , artificial intelligence
BACKGROUND The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required. RESULTS In this study, the potential of hyperspectral imaging in the range of 328–1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. Ninety samples of eight tea‐leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using a full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by a successive projections algorithm (SPA) and competitive adaptive reweighted sampling. The results showed that the optimal SPA–MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R 2 p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION The results suggested that the hyperspectral imaging technique coupled with chemometrics was a promising tool for the rapid and nondestructive measurement of tea‐leaf quality, and had the potential to develop multispectral imaging systems for future online detection of tea‐leaf quality. © 2020 Society of Chemical Industry