应用近红外光谱技术对稻谷脂肪含量进行检测。采集了90个稻谷样本的漫反射近红外光谱，运用Kennard-Stone法选取校正集及预测集样本。对比研究了归一化、一阶导、二阶导、一阶导+归一化等四种预处理方法对模型性能的影响，确定一阶导为最佳预处理方法。运用竞争性自适应重加权采样技术筛选出与稻谷脂肪含量检测相关的特征波长，再用多元线性回归对特征波长进行优选，最终得到30个特征波长。其中最典型的特征波长为1343 nm、1489 nm和1583 nm，反映了稻谷脂肪中大量存在的-CH和-OH基团。所建立的基于近红外光谱分析技术的稻谷脂肪含量检测模型具的决定系数为0.9589，定标标准差RMSEC为0.2236，相对偏差为5.53%。

Near Infrared (NIR) spectrum was used to detect the fat content in rice. NIR spectra of 90 rice samples were measured. Kennard-Stone method was used to select the calibration set and prediction set samples. The effects of different pretreatment (normalize, first derivative and second derivative methods) have been compared for the accuracy of the models. The best pretreatment method is the first derivative. The competitive adaptive reweighted sampling was applied to screening the key wavelengths associated with the sample properties. Finally, thirty key wavelengths are selected by Multiple Linear Regression further. The most typical key wavelengths are 1343 nm, 1489 nm and 1583 nm which related to the groups of -CH and -OH in rice fat. The detection model of fat content of rice based on near infrared spectroscopy has higher precision whose coefficient of determination, root mean square error of calibration and relative deviation are 0.9589, 0.2236 and 5.53%.