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 (R2 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.
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 (R2 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.