基于可见-近红外光谱识别氧乐果污染的脐橙

该文对喷施过不同浓度氧乐果农药的脐橙样品采集可见-近红外光谱进行识别。采用多元散射校正（MSC）、标准正交变量变换（SNV）、一阶导数（FD）和二阶导数（SD）4种不同光谱预处理方法时，分别选取430～1 000、1 000～1 800和430～1 800 nm 3种波谱范围建立偏最小二乘法（PLS）农药污染预测模型。比较实验结果表明：波谱范围取430～1 000 nm，采用一阶导数的预处理方法时应用建立的PLS预测模型最优，其验证组脐橙表面氧乐果污染程度的实际类别与预测类别的相关系数Rpred为0.9817，预测样本均方根误差RMSEP是0.1564。

Recognition of navel orange contaminated by omethoate based on Vis-NIR spectroscopy

The experiment acquired Vis-NIR of navel orange surface, which were sprayed with different concentration of omethoate pesticide. The prediction models of partial least squares (PLS) pesticide contamination were established using the four different spectral pretreatment methods of multiplicative scatter correction (MSC), standard orthogonal variable transformation (SNV), first derivative (FD) and second derivative (SD) by selecting the spectral range 430-1000 nm, 1 000-1 800 nm and 430-1 800 nm respectively. Comparing the results of experiment, it shows that the optimal model is obtained in the range of 430-1 000 nm and by the spectral data preprocessing method of FD. In the validation set, the correlation coefficient Rpred between the actual category and the predicted category of omethoate contamination degree on navel orange surface is 0.9817 and the root mean squared error of prediction samples (RMSEP) is 0.1564.