用色散扫描型仪器采集大麦样品的近红外光谱， 扫描出的光谱携带了大量样品化学值信息， 采用正交信号校正(OSC)预处理方法对这些原始光谱进行处理， 剔除噪声等不相关因子以后建立偏最小二乘(PLS)近红外光谱分析模型(OSC-PLS)， 预测大麦蛋白质的含量， 并与传统PLS建模方法进行对比。 基于OSC-PLS算法的蛋白质含量近红外光谱分析模型的测定系数R2为0.901， 检验集的化学值与模型预测值的相关系数r达到0.971 7， 分析模型的预测标准偏差SD为0.545 0， 相对标准偏差RSD为4.2%。 结果表明， OSC-PLS回归方法能在较大程度上消除无关因素的影响， 在简化模型的同时提高了模型的可解释性， 能够建立准确的大麦蛋白质含量近红外预测模型， 可代替经典分析方法， 满足农产品快速分析的需要。

Spectra of barley containing vast information were obtained with the dispersion spectrograph. The contents of protein in barley were determined by dispersive near infrared（NIR）spectroscopy. Pretreatment method of orthogonal signal correction （OSC） was used to reject uncorrelated variables in the original spectra before building the partial least squares NIR method（OSC-PLS）. The results were compared with the regular PLS model. With the OSC-PLS method, the determination coefficient R2 was 0.901. The correlation coefficient of validation set was 0.971 7. The standard deviation（SD）and relative standard deviation（RSD）were 0.545 0 and 4.2% respectively. Applying OSC-PLS resulted in removal of non-correlated variation in spectra and reduced model’s complexity with preserved ability and improved interpretative ability of variation in spectra. It means that the OSC-PLS is a fungible model to predict the contents of protein in barley veraciously to meet the demand of fast analysis of agricultural products.