Determination of color values on tomato leaves stressed by the high temperature using hyperspectral imaging technique was studied in this paper .Hyperspectral images of sixty healthy and sixty unhealthy tomato leaves in the wavelengths of 380~1 023 nm were acquired by the hyperspectral imaging system .Simultaneously ,three color parameters (L* , a* and b* ) were measured by a colorimeter .Reflectance of all pixels in the region of interest (ROI) was extracted from the corrected hyperspec-tral image .Partial Least Squares (PLS) models were established based on different preprocessing methods .Successive Projec-tions Algorithm (SPA ) was identified to select effective wavelengths . Finally , Partial Least Squares-Discriminant Analysis (PLS-DA) models were built to classify different types of samples .The results showed that the determination coefficient (R2 ) were 0.818 ,0.109 and 0.896 in the prediction sets of PLS modes ;0.591 ,0.244 and 0.673 in the prediction sets of SPA-PLS models .The overall classification accuracy in the prediction sets of PLS-DA models were over 77 .50% .It demonstrated that it is feasible to measure color values on tomato leaves and identify different types of samples using hyperspectral imaging technique .%提出了利用可见／近红外高光谱成像技术检测高温障碍胁迫下番茄叶片色差的方法。首先采集380～1023 nm波段范围内60个高温障碍胁迫和60个健康番茄叶片的高光谱图像，同时获取全部叶片的色差值（L*，a*和b*），然后提取所有样本的高光谱图像中感兴趣区域（region of interest ，ROI）的光谱反射率值。基于不同预处理方法建立偏最小二乘（partial least squares ，PLS ）预测模型，再利用连续投影算法（suc-cessive projections algorithm ，SPA）提取特征波长并建立SPA-PLS预测模型。最后分别基于全波段和特征波段建立偏最小二乘-判别分析（partial least squares-discriminant analysis ，PLS-DA ）模型。结果显示，全波段中基于原始光谱信息建立的模型效果最好，3个色差值的预测集决定系数（determination coefficient ，R2）分别是0.818，0.109和0.896；基于特征波长建立的模型预测集 R2分别是0.591，0.244和0.673；所有模型预测集的总体识别率均大于77.50％。结果表明，可见／近红外高光谱成像技术检测番茄叶片色差值（L*和b*）和识别高温障碍样本是可行的。
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