为了了解高光谱图像中光谱提取区域对果品糖度检测模型精度的影响，本文以“华优”猕猴桃为对象，分别提取了10×10、20×20和30×30（像素×像素）的正方形光谱区域以及样品掩膜图像的平均光谱，对平均光谱进行平滑去噪+标准正态变量变换预处理，用处理后的全光谱建立了预测猕猴桃糖度的偏最小二乘、最小二乘支持向量机、极限学习机和误差反向传播网络模型，分析了光谱提取区域对猕猴桃糖度检测精度的影响规律。结果表明，光谱提取面积的增加能够提升最小二乘支持向量机、极限学习机和误差反向传播网络模型的预测性能。基于猕猴桃掩膜图像的平均光谱所建立的最小二乘支持向量机模型具有最好的预测性能，其预测相关系数为0.97，预测均方根误差为0.86oBrix，相对预测误差为4.06。研究说明在高光谱图像中选择合适的光谱提取区域有助于提高模型的预测精度。

The sugar content of “Huayou” kiwifruits was estimated by hyperspectral imaging. The mean reflectance spectra of square regions measuring 10 × 10, 20 × 20, and 30 × 30 (pixels × pixels) and mask images were extracted. Four sugar content prediction models, i.e., partial least squares (PLS), least-square support vector machine (LSSVM), extreme learning machine (ELM), and error back-propagation network (BP), were established using the processed full spectra, and the effect of spectral extraction regions on the prediction accuracy of sugar content was analyzed. The mean spectra were preprocessed by Savitzky-Golay smoothing combined with standard normal variate transformation. The results showed that the prediction performance of LSSVM, ELM, and BP improved with increasing size of the region of interest. The LSSVM based on mean spectra extracted by mask images exhibited the highest prediction performance; the correlation coefficient of prediction was 0.97, the root mean square error of prediction was 0.86 °Brix, and the relative prediction error was 4.06. The results of this study indicate that selecting appropriate spectral extraction regions in hyperspectral imaging is helpful to improve the prediction accuracy of the established models.

国家科技支撑计划资助项目（2015BAD19B03）