利用高光谱空间散射曲线的3个洛伦兹拟合参数对苹果的品质(硬度、 可溶性固溶物含量)进行同时检测。 采用偏最小二乘, 逐步多元线性回归和BP神经网络3种方法, 对归一化处理和未归一化处理的3个洛伦兹参数组合分别建立苹果品质的预测模型。 结果表明： 采用偏最小二乘法对未归一化处理参数的组合建立硬度的预测模型其预测结果最好, 校正组相关系数Rc=0.93, 校正标准差SEC=0.56, 验证组相关系数Rv=0.84, 验证标准差SEV=0.94。 采用偏最小二乘法对归一化处理参数的组合建立可溶性固形物的预测模型其预测结果最好, Rc=0.95, SEC=0.29, Rv=0.83, SEV=0.63。 研究结果表明： 利用高光谱空间散射曲线的多拟合参数组合可以同时检测苹果的多品质参数。

The research discussed the prediction method of apple’s internal quality such as firmness and soluble solids content with the combination of parameters getting from hyperspectral fitting scattering curve. The research compared different molding methods using the combination of the three Lorentzian fitting parameters with partial least squares (PLS), stepwise multiple linear regression (SMLR) and neural network (NN). The normalized combination parameters and original combination parameters were used to establish prediction models, respectively. The partial least squares (PLS) prediction models using the combination of three original parameters gave a better results with the correlation of calibration Rc=0.93, the standard error of calibration SEC=0.56, the correlation of validation Rv=0.84, and the standard error of validation SEV=0.94 for firmness of apples. The partial least squares (PLS) prediction models using combination of normalized parameters also gave a good results with Rc=0.95, and the standard error of calibration SEC=0.29, the correlation of validation Rv=0.83, and the standard error of validation SEV=0.63 for soluble solids content of apples. The research showed that using hyperspectral scattering curve can detect apple quality attributes at the same time.