Xie Chuanqi, Li Xiaoli, Shao Yongni, He Yong
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.
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
PLoS One. 2014 Dec 29;9(12):e113422. doi: 10.1371/journal.pone.0113422. eCollection 2014.
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 Δaand 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.
本研究探讨了利用高光谱成像技术对茶叶在不同干燥阶段的颜色成分（ΔL*、Δa和Δb）进行无损测量并分类的可行性。在380 - 1030 nm光谱区域采集了五个干燥阶段茶叶的高光谱图像。用色度计测量这三个颜色特征。根据偏最小二乘回归（PLSR）模型的预测结果，应用不同的预处理算法来选择最佳算法。分别采用竞争性自适应重加权采样（CARS）和连续投影算法（SPA）来识别有效波长。分别建立了不同的模型（最小二乘支持向量机[LS - SVM]、PLSR、主成分回归[PCR]和多元线性回归[MLR]）来预测这三个颜色成分。SPA - LS - SVM模型表现出色，对于ΔL的相关系数（rp）为0.929，对于Δa为0.849，对于Δb*为0.917。构建了LS - SVM模型用于不同茶叶的分类。在校准集中正确分类率（CCR）范围为89.29%至100%，在预测集中范围为71.43%至100%。校准集的总分类结果为96.43%，预测集为85.71%。结果表明，高光谱成像技术可作为一种客观无损的方法来确定不同干燥阶段茶叶的颜色特征并进行分类。
