CAJ | 학술논문

为对小麦硬度进行自动检测，采集不同硬度小麦品种的近红外高光谱图像，将光谱数据经过求导处理后，提取950~1645 nm 有效光谱区间数据，然后经过多元散射校正，建立偏最小二乘判别分析(PLS - DA)模型。采用120粒小麦对模型进行训练，剩余的90粒进行检验，总体上模型分类准确率为99.63%。表明，采用近红外高光谱成像技术对单籽粒小麦硬度进行分类是可行的。
위대소맥경도진행자동검측，채집불동경도소맥품충적근홍외고광보도상，장광보수거경과구도처리후，제취950~1645 nm 유효광보구간수거，연후경과다원산사교정，건립편최소이승판별분석(PLS - DA)모형。채용120립소맥대모형진행훈련，잉여적90립진행검험，총체상모형분류준학솔위99.63%。표명，채용근홍외고광보성상기술대단자립소맥경도진행분류시가행적。
As the hardness of wheat may greatly influence the milling process,it is necessary to activate automatic detection of wheat hardness. To prepare for the research,the near-infrared hyperspectral images of wheat with different hardness were collected. The data were processed by derivation,and those in spec-tral range between 950—1 645 nm effective were extracted,after multiplicative scatter correction,with which a partial least squares discriminant analysis model(PLS-DA) was built. During the experiment,120 wheat kernels were used to train the model,and the remaining 90 kernels were used to predict. Conse-quently,the accuracy rate of the model was 99. 63% . The results showed that it was feasible to classify the hardness of wheat kernel based on near-infrared hyperspectral imaging technology.