为了实现小麦品质(干物质、重量)的快速无损检测,对35个小麦品种样品进行了近红外系统扫描,获取光谱信息,并进行高斯滤波平滑(GFS)、归一化(N)和基线校正(BC)预处理.采用偏最小二乘(PLS)算法分别建立光谱信息与干物质和重量参考值之间的定量关系.采用回归系数法(RC)和连续投影算法(SPA)两种方法在900～1700nm范围内选择最优波长对全波段PLS模型进行优化.基于选择的最优波长,分别建立PLS和MLR预测模型.结果 表明,基于RC法从RAW光谱中筛选出的20个最佳波长构建的RC-RAW-PLS模型对干物质有较好的预测性能,rp为0.93,RMSEP为0.03％.基于SPA法从RAW光谱中选取的12个最优波长建立的SPA-RAW-MLR模型对重量有较好的预测性能,rp为0.89,RMSEP为0.32 g.综上所述,近红外光谱结合PLS和MLR算法可分别用于小麦籽粒干物质和重量的快速预测.“,”In order to realize simultaneous and rapid nondestructive detection of wheat quality (dry matter,weight),35 wheat varieties samples were subjected to near-infrared (NIR) system scanning,and the spectral information were acquired and pretreated by three methods including Gaussian filtering smoothing (GFS),normalize (N) and baseline correction (BC),respectively.Partial least squares (PLS) algorithm was adopted to build a quantitative relationship between spectra and reference value of dry matter and weight,respectively.Two methods such as regression coefficients (RC) and successive projections algorithm (SPA) were applied to select optimal wavelengths from the full 900～1700 nm range for PLS model optimization.Based on the selected optimal wavelengths,PLS and multiple linear regression (MLR) prediction models were built respectively.The results indicated that the RC-RAW-PLS models based on 20 optimal wavelengths selected from RAW spectra by RC method had better performance in dry matter prediction,with rp of 0.93 and RMSEP of 0.03％.The SPA-RAW-MLR model built with 12 optimal wavelengths selected from RAW spectra by SPA method had better performance in weight prediction,with rp of 0.89 and RMSEP of 0.32 g.In conclusion,NIR spectroscopy combined with PLS and MLR algorithm could be used for rapid prediction of dry matter and weight in wheat grain.