【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil mo...【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0—20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S^0(122个样本)、训练集S^1(60个样本)、验证集S^2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S^1和S^2样本集的9个梯度SM(0%—32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R^2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关�展开更多
A novel OSC-KPCA based pattern analysis method was proposed to improve the clustering and predictive performance of the metabolomics.The strong nonlinear pattern recognition power and the predominance in dealing with ...A novel OSC-KPCA based pattern analysis method was proposed to improve the clustering and predictive performance of the metabolomics.The strong nonlinear pattern recognition power and the predominance in dealing with small high-dimensional data of the Kernel Principal Component Analysis(KPCA)were used here to analyze four genotypes of the important model plant—Arabidopsis thaliana.In order to improve the performance of PR(Pattern Recognition),the Orthogonal Signal Correction(OSC)method was used to filter the original data firstly to eliminate the interference of noise.The PR results showed that the OSC-KPCA based PR method could reveal the underlying relationship between genotypes and metabolites successfully.The paternal genotypes(Co10 and C24)and the two F1 progeny C24×Co10 and Co10×C24 could be 100% correctly discriminated.More importantly the predictability was also as high as 100%.展开更多
文摘【目的】快速、准确地监测土壤有机质对于精准农业的发展具有重要意义。可见光-近红外(visible and near-infrared,Vis-NIR)光谱技术在土壤属性估算、数字化土壤制图等方面应用较为广泛,然而,在田间进行光谱测量,易受土壤含水量(soil moisture,SM)、温度、土壤表面状况等因素的影响,导致光谱信息中包含大量干扰信息,其中,SM变化是影响光谱观测结果最为显著的因素之一。此研究的目的是探讨OSC算法消除其影响,提升Vis-NIR光谱定量估算土壤有机质(soil organic matter,SOM)的精度。【方法】以江汉平原公安县和潜江市为研究区域,采集217份耕层(0—20 cm)土壤样本,进行风干、研磨、过筛等处理,采用重铬酸钾-外加热法测定SOM;将总体样本划分为3个互不重叠的样本集:建模集S^0(122个样本)、训练集S^1(60个样本)、验证集S^2(35个样本);设计SM梯度试验(梯度间隔为4%),在实验室内获取S^1和S^2样本集的9个梯度SM(0%—32%)的土壤光谱数据;分析SM对土壤Vis-NIR光谱反射率的影响,采用外部参数正交化算法(external parameter orthogonalization,EPO)、正交信号校正算法(orthogonal signal correction,OSC)消除SM对土壤光谱的干扰;利用主成分分析(principal component analysis,PCA)的前两个主成分得分和光谱相关系数两种方法检验消除SM干扰前、后的效果;基于偏最小二乘回归(partial least squares regression,PLSR)方法建立EPO和OSC处理前、后的SOM估算模型,利用决定系数(coefficient of determination,R^2)、均方根误差(root mean square error,RMSE)和RPD(the ratio of prediction to deviation)3个指标比较PLSR、EPO-PLSR、OSC-PLSR模型的性能。【结果】土壤Vis-NIR光谱受SM的影响十分明显,随着SM的增加,土壤光谱反射率呈非线性降低趋势。OSC处理前的湿土光谱数据主成分得分散点相对分散,与干土光谱数据主成分得分空间的位置不重叠,不同SM梯度之间的光谱相关�
文摘A novel OSC-KPCA based pattern analysis method was proposed to improve the clustering and predictive performance of the metabolomics.The strong nonlinear pattern recognition power and the predominance in dealing with small high-dimensional data of the Kernel Principal Component Analysis(KPCA)were used here to analyze four genotypes of the important model plant—Arabidopsis thaliana.In order to improve the performance of PR(Pattern Recognition),the Orthogonal Signal Correction(OSC)method was used to filter the original data firstly to eliminate the interference of noise.The PR results showed that the OSC-KPCA based PR method could reveal the underlying relationship between genotypes and metabolites successfully.The paternal genotypes(Co10 and C24)and the two F1 progeny C24×Co10 and Co10×C24 could be 100% correctly discriminated.More importantly the predictability was also as high as 100%.
