小麦品质近红外检测系统的设计与试验研究

为实现快速无损地检测小麦品质设计了基于光栅技术的近红外检测系统，测试了该系统的准确性、重复性和稳定性，选取MPA光谱仪为参比仪器，分别采集56份小麦样品的光谱，建立偏最小二乘回归模型并验证。该系统的四个模型的决定系数R2分别为92.38%，93.48%，93.16%，94.44%，交叉验证标准差RESECV为0.405，0.374，0.383，0.346，相对分析误差RPD为3.62，3.39，3.82，4.24;预测集验证模型的R2为96.97%，94.22%，96.62%，96.34%，预测标准差RMSEP为0.221，0.305，0.233，0.243。MPA光谱仪的建模结果R2 为95.99%，RESECV 为0.293，RDP为5;预测集验证模型的R2为98.31%，RMSEP为0.165。实验表明：小麦品质近红外检测系统所得模型具有良好的预测性，稳定性和重复性;所得光谱波长与吸光度具有重现性;其模型对平均光谱的预测效果优于单张光谱;该系统工作稳定，性能优良，可应用于小麦品质质量检测。

Research on Development and Experiment of NIR Wheat Quality Quick Detection System

In order to detect wheat quality rapidly and nondestructively, NIR wheat quality quick detection system was developed on the base of grating technology. To test accuracy, repeatability and stability of this self-made system, Bruker MPA spectroscopy was selected as target analyzer and 56 wheat samples were analyzed by building and validating PLS calibration models. In the 4 models of the self-made system, the coefficient of determination R2 is 92.38%, 93.48%, 93.16% and 94.44%; root mean square error of cross validation RMSECV=0.405, 0.374, 0.383, 0.346; ratio of performance to standard deviate RPD=3.62, 3.39, 3.82, 4.24, respectively. And evaluating indicators of validating results in the 4 models are as follows: R2=96.97%, 94.22%, 96.62% and 96.34%; Root mean square error of prediction RMSEP=0.221, 0.305, 0.233 and 0.243 respectively. The model of MPA spectroscopy gave an R2 of 95.99%, a RMSECV of 0.293, RPD of 5 and validation results are R2 of 98.31%, RMSEP of 0.165, respectively. The results show that the models of self-made instrument have good prediction performance, stability and repeatability, and wavelength and absorbance of the obtained spectra have a good repeatability. The prediction effect of single spectrum is not ideal, but it can be improved by using average spectrum of repeated acquisition. NIR wheat quality quick detection system can detect wheat quality with good performance.