Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging.

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. A total of 90 samples of eight tea leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). The results showed that 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 (RMSEP) of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation (RPD) of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION: The results suggested that hyperspectral imaging technique coupled with chemometrics was a promising tool for 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. This article is protected by copyright. All rights reserved.