In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this ...In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.展开更多
【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系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R^2_(pre)、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R^2_(pre)、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。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
高光谱遥感是监测土壤盐渍化的重要手段之一,但野外光谱反射率易受土壤水分的影响,导致盐分监测精度难以保证。为有效消除水分因素,提高土壤含盐量反演精度,该研究以银川平原盐渍化土壤为研究对象,以野外土壤光谱反射率(reflectance,Ref...高光谱遥感是监测土壤盐渍化的重要手段之一,但野外光谱反射率易受土壤水分的影响,导致盐分监测精度难以保证。为有效消除水分因素,提高土壤含盐量反演精度,该研究以银川平原盐渍化土壤为研究对象,以野外土壤光谱反射率(reflectance,Ref)和实测土壤含盐量为数据源,分析不同含水率的土壤光谱特征,将反射率经过一阶微分(first derivative of reflectance,FDR)、正交信号校正(orthogonal signal correction,OSC)和一阶微分-正交信号校正(first derivative of reflectance-orthogonal signal correction,FDR-OSC)变换,分析各光谱数据与含盐量、含水率的相关性,确定最佳“除水”方法,然后基于支持向量机(support vector machine,SVM)建立土壤含盐量反演模型。结果表明:1)含水率与土壤光谱反射率呈反比,光谱在1430、1950、2200 nm附近存在吸收带,1950 nm附近为最主要吸收波段,且存在向长波漂移的现象。2)光谱数据与含水率相关性由强到弱的顺序为:Ref、OSC、FDR、FDR-OSC;与含盐量相关性由强到弱的顺序为:FDR-OSC、FDR、OSC、Ref。3)基于FDR-OSC“除水”的SVM含盐量模型决定系数R_(c)^(2)、R_(p)^(2)和相对分析误差(relative prediction deviation,RPD)分别达到0.952、0.960和5.04,具有极强的拟合和反演能力。研究结果可为银川平原及同类地区土壤含盐量的精准监测提供科学依据。展开更多
基金Project supported by the Zhejiang Province Key Technologies R & DProgram (No. 021103549)the National Key Technologies R & DProgram (No. 2001BA701A45), China
文摘In near-infrared (NIR) analysis of plant extracts, excessive background often exists in near-infrared spectra. The detection of active constituents is difficult because of excessive background, and correction of this problem remains difficult. In this work, the orthogonal signal correction (OSC) method was used to correct excessive background. The method was also compared with several classical background correction methods, such as offset correction, multiplicative scatter correction (MSC), standard normal variate (SNV) transformation, de-trending (DT), first derivative, second derivative and wavelet methods. A simulated dataset and a real NIR spectral dataset were used to test the efficiency of different background correction methods. The results showed that OSC is the only effective method for correcting excessive background.
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系数变化较大;OSC处理后的湿土光谱数据主成分得分空间的位置基本与干土光谱数据相重合,各样本光谱数据之间相似性很高,不同SM梯度之间的光谱相关系数变化较小。9个SM梯度的EPO-PLSR模型的验证平均R^2_(pre)、RPD分别为0.69、1.7。9个SM梯度的OSC-PLSR模型的验证平均R^2_(pre)、RPD分别为0.72、1.89,校正后的OSC-PLSR模型受SM的较小,有效提升SOM估算模型的精度和鲁棒性。【结论】OSC能够消除SM变化对土壤Vis-NIR光谱的影响,可为将来田间原位实时监测SOM信息提供一定的理论支撑。
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%.
文摘针对选择性催化还原(selective catalytic reduction,SCR)脱硝系统反应过程复杂,在工况变化时存在非线性、大惯性和强干扰性的特点,难以建立准确的出口NO_x排放浓度模型。利用核偏最小二乘法具有解决变量众多且存在严重相关的非线性工业过程建模的优点,首先引入正交信号校正(orthogonal signal correction,OSC)对相空间重构后的样本进行预处理,剔除与建模无关的信息;然后利用组合核偏最小二乘法(combination kernel partial least squares,CKPLS)具有较好的学习能力和泛化能力的特点,提出OSC-CKPLS方法提高模型性能;最后采用滑动窗口更新,并反馈补偿修正模型。对2个标准数据集进行仿真,分别验证CKPLS、OSC和OSC-CKPLS能够提高模型性能;并对SCR脱硝系统现场数据验证了本文方法的有效性。
文摘高光谱遥感是监测土壤盐渍化的重要手段之一,但野外光谱反射率易受土壤水分的影响,导致盐分监测精度难以保证。为有效消除水分因素,提高土壤含盐量反演精度,该研究以银川平原盐渍化土壤为研究对象,以野外土壤光谱反射率(reflectance,Ref)和实测土壤含盐量为数据源,分析不同含水率的土壤光谱特征,将反射率经过一阶微分(first derivative of reflectance,FDR)、正交信号校正(orthogonal signal correction,OSC)和一阶微分-正交信号校正(first derivative of reflectance-orthogonal signal correction,FDR-OSC)变换,分析各光谱数据与含盐量、含水率的相关性,确定最佳“除水”方法,然后基于支持向量机(support vector machine,SVM)建立土壤含盐量反演模型。结果表明:1)含水率与土壤光谱反射率呈反比,光谱在1430、1950、2200 nm附近存在吸收带,1950 nm附近为最主要吸收波段,且存在向长波漂移的现象。2)光谱数据与含水率相关性由强到弱的顺序为:Ref、OSC、FDR、FDR-OSC;与含盐量相关性由强到弱的顺序为:FDR-OSC、FDR、OSC、Ref。3)基于FDR-OSC“除水”的SVM含盐量模型决定系数R_(c)^(2)、R_(p)^(2)和相对分析误差(relative prediction deviation,RPD)分别达到0.952、0.960和5.04,具有极强的拟合和反演能力。研究结果可为银川平原及同类地区土壤含盐量的精准监测提供科学依据。
