CAJ | 학술논문

采用多元散射校正(MSC)预处理方法对冬小麦叶片反射光谱进行预处理,有效地减小物理因素对光谱的影响,之后用非线性迭代偏最小二乘法(NIPALs)提取经MSC处理后的反射光谱的主成分,主成分个数由交叉证实法(Cross Validation)确定,将提取的主成分作为人工神经网络(ANN)的输入,建立人工神经网络分析模型(MSC-ANN),用冬小麦叶片的反射光谱来预测冬小麦叶片叶绿素含量.校准集的化学值与预测值的相关系数r达到0.960 4,预测标准偏差SD为0.187,相对标准偏差RSD为5.18%.检验集的化学值与预测值的相关系数r达到0.960 0,预测标准偏差SD为0.145,相对标准偏差RSD为4.21%.结果表明,MSC-ANN方法能在较大程度上消除了野外物理因素的影响,使用具有代表性的光谱数据点建立模型,能够建立准确的冬小麦叶绿素含量预测模型,可代替经典分析方法,满足冬小麦叶片叶绿素快速分析的需要.
채용다원산사교정(MSC)예처리방법대동소맥협편반사광보진행예처리,유효지감소물리인소대광보적영향,지후용비선성질대편최소이승법(NIPALs)제취경MSC처리후적반사광보적주성분,주성분개수유교차증실법(Cross Validation)학정,장제취적주성분작위인공신경망락(ANN)적수입,건립인공신경망락분석모형(MSC-ANN),용동소맥협편적반사광보래예측동소맥협편협록소함량.교준집적화학치여예측치적상관계수r체도0.960 4,예측표준편차SD위0.187,상대표준편차RSD위5.18%.검험집적화학치여예측치적상관계수r체도0.960 0,예측표준편차SD위0.145,상대표준편차RSD위4.21%.결과표명,MSC-ANN방법능재교대정도상소제료야외물리인소적영향,사용구유대표성적광보수거점건립모형,능구건립준학적동소맥협록소함량예측모형,가대체경전분석방법,만족동소맥협편협록소쾌속분석적수요.
Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat The correlation coefficient (r) of calibratipn set was 0. 960 4, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0. 9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4. 21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.