title: Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.
abstract: This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China; Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida, United States of America.
authors.forename: Chuanqi
authors.initials: C
authors.lastname: Xie
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Li
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
authors.forename: Yongni
authors.initials: Y
authors.lastname: Shao
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China; Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Hangzhou, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D002138:Calibration; D028241:Camellia sinensis / Q000737:chemistry*; D003124:Colorimetry / Q000379:methods*; D003890:Desiccation; D007259:Infrared Rays; D008962:Models, Theoretical; D018515:Plant Leaves / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared; D013057:Spectrum Analysis; D013472:Sunlight; D013662:Tea / Q000737:chemistry
chemical_list: D013662:Tea