水果表面农药污染的可见／近红外光谱识别法

以表面经过喷施不同浓度农药后的脐橙为研究对象，采用可见／近红外漫反射光谱技术定性检测脐橙农药污染的程度。采集脐橙350～1800nm范围的光谱。应用多元散射校正（MSC），标准正态变量（SNV）变换，一阶微分和二阶微分四种不同预处理方法，分别在430～1000nm、10001800nm和430～1800nm三个光谱范围内建立识别脐橙污染程度的偏最小二乘法（PLS）数学模型。比较分析得出试验结果：波谱范围取430～1000nm，采用一阶微分的预处理方法时应用PLS校正方法的结果最优，其预测值和真实值之间的相关系数和预测均方根误差分别为0．9830和0．1482。研究结果袁明。可见／近红外漫反射光谱技术可以定性检测脐橙的农药污染程度。

A Vis/NIR Spectrum Recognition Method of Pesticide Contamination on Fruit Surface

The Vis/NIR technology was proposed to qualitatively detect the extent of pesticide contamination on the surface of navel oranges, which were sprayed with different concentration of pesticide contamination. The spectral region from 350 to 1 800 nm was measured. And the spectrum was divided into three regions, which were 430-100 nm, 1 000-1 800 nm, 430- 1 800 nm, respectively. Four methods of spectrum data preprocessing were used, which were multiplicative scattering correction （MSC）, standard normal variate （SNV）, first derivative （FD） and second derivative（SD）. The Vis/NIR and partial least square regression（PLS） were used to establish the models by comparing several preprocessing procedures and wavelength ranges. The results show that the optimal models could be obtained in the range of 430- 1 000 nm by the spectral data preprocessing of the first derivative. And the correlation coefficients of the prediction and root mean squared error of pre- diction （RMSEP） are 0. 983 0 and 0. 148 2, respectively. This study results show that the Vis/NIR technology can be used to qualitatively detect the extent of pesticide contamination of navel orange.