基于高光谱图像技术的雪花梨品质无损检测

为探讨基于高光谱图像技术对雪花梨品质进行无损检测的可行性，研究了利用高光谱图像系统提取雪花梨中糖和水的光谱响应和形态特征参数，获取样品含糖量和含水率的敏感水分吸收光谱带，利用人工神经网络建立雪花梨含糖量和含水率预测模型及利用投影图像面积预测雪花梨鲜重。结果表明，基于高光谱图像技术对雪花梨品质进行无损检测是可行的。雪花梨含糖量预测值和实际值间相关系数R为0.996，误差平均值为0.5°Brix；含水率预测值和实际值间相关系数R为0.94，相对误差平均值为0.62%；鲜重预测值和实际值间相关系数R为0.93。

Non-destructive inspection of Chinese pear quality based on hyperspectral imaging technique

Non-destructive inspection of the interior and exterior quality of fruit has always been a research topic because many subjective assessing methods limited to the exterior measurements with poor repeatability and tedious procedures are still widely used. In this study, a hyperspectral-imaging technique was developed to realize a fast, accurate and objective grading of Chinese pears. The morphological features and spectral responses on sugar and water content can be extracted simultaneously. The feature wavelengths for water content prediction(462, 502, 592, 706 and 957 nm) and for sugar content prediction(500, 703, 816, 875 and 920 nm) were selected based on partial least squares analysis. Artificial Neural Network was engaged to establish the prediction model for the water and sugar contents. The results show that the ANN model could predict water and sugar contents of pear samples with correlation coefficient of 0.996 and 0.94, respectively. RMSEP was 4.24% for water content and 0.5°Brix for sugar content. For weight prediction, the correlation coefficient between predicted and real weight was 0.93.