摘要
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。
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 （BPANN）, 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 tofind 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 calibration 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 MSCANN 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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2010年第1期188-191,共4页
Spectroscopy and Spectral Analysis
基金
国家高技术研究发展计划项目("863计划")(2007AA10Z211)
国家科技支撑项目(2006BAD10A01)资助
