土壤养分的近红外漫反射光谱预测模型分析
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摘要
针对土壤养分近红外漫反射光谱数据分析的预测问题,分别利用主成分回归和偏最小二乘回归的方法建立土壤样品的近红外漫反射光谱全氮含量的数学模型,比较模型的预测精度。研究结果表明,采用主成分回归法建模预测结果的均方根误差RMSEP为0.040;偏最小二乘回归法建模的RMSEP为0.034,通过模型验证得到的全氮含量预测值与实际值相关性分析得到主成分回归法决定系数R~2=0.873 1,偏最小二乘回归法R~2=0.903 5,表明偏最小二乘回归法所建模型预测精度优于主成分回归法。该研究为提高近红外光谱法土壤养分检测精度提供了依据。
For the prediction problem in data analysis on near infrared diffuse reflection spectroscopy of soil nutrient,in this paper,the principal component regression and partial least squares regression were used to establish the mathematical models of the near infrared spectra of soil samples with different total nitrogen contents,and the prediction accuracy of the models were compared.The results show that the RMSEP is 0.040 by principal component regression and 0.034 by partial least squares regression respectively,with determination coefficient R~2=0.873 1 by principal component regression and R~2=0.903 5 by partial least squares regression through correlation analysis between the predicted value and the actual value of the total nitrogen content by means of model validation,which indicates the prediction accuracy of modeling by partial least squares regression is superior to that by principal component regression.The research results provides the basis for improving the detection accuracy of soil nutrients by near-infrared spectroscopy.
For the prediction problem in data analysis on near infrared diffuse reflection spectroscopy of soil nutrient,in this paper,the principal component regression and partial least squares regression were used to establish the mathematical models of the near infrared spectra of soil samples with different total nitrogen contents,and the prediction accuracy of the models were compared.The results show that the RMSEP is 0.040 by principal component regression and 0.034 by partial least squares regression respectively,with determination coefficient R~2=0.873 1 by principal component regression and R~2=0.903 5 by partial least squares regression through correlation analysis between the predicted value and the actual value of the total nitrogen content by means of model validation,which indicates the prediction accuracy of modeling by partial least squares regression is superior to that by principal component regression.The research results provides the basis for improving the detection accuracy of soil nutrients by near-infrared spectroscopy.
引文
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[9]郑咏梅,张铁强,张军,等.平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究[J].光谱学与光谱分析,2004,24(12):1546-1548.
[10]李硕,汪善勤,张美琴.基于可见-近红外光谱比较主成分回归、偏最小二乘回归和反向传播神经网络对土壤氮的预测研究[J].光学学报,2012,32(8):289-293.
[11]褚小立,许育鹏,陆婉珍.偏最小二乘方法在光谱定性分析中的应用研究[J].现代仪器,2007(5):13-15.
[2]Morra M J,Hall M H,Freeborn L L.Carbon and nitrogen analysis of soil fractions using near infrared reflectance spectroscopy[J].Soil Science Society of America Journal,1991(55):288-291.
[3]Schimann H,Joffre R,Roggy J C,et al.Evaluation of the recovery of microbial functions during soil restoration using near infrared spectroscopy[J].Applied Soil Ecology,2007,37:223-232.
[4]Chang C W,Laird D A,Mausbach M J,et al.Near infrared reflectance spectroscopy:principal components regression analyses of soil properties[J].Soil Science Society of American Journal,2001,65(2):480-490.
[5]Kusumo B H,Hedley M J,Hedley C B,et al.Measuring carbon dynamics infield soils using soil spectral reflectance:prediction of maize root density,soil organic carbon and nitrogen content[J].Plant and Soil,2011,338(1-2):233-245.
[6]李伟,张书慧,张倩,等.近红外光谱法快速测定土壤碱解氮、速效氮和速效钾含量[J].农业工程学报,2007,23(1):55.
[7]蔡剑华,王先春,胡惟文.基于EMD的土壤有机质含量近红外光谱检测[J].农业机械学报,2010,41(9):182-186.
[8]隋世江,叶鑫,隽英华.近红外光谱技术在土壤成分检测中的研究进展[J].农业科技与装备,2012(1):14-19.
[9]郑咏梅,张铁强,张军,等.平滑、导数、基线校正对近红外光谱PLS定量分析的影响研究[J].光谱学与光谱分析,2004,24(12):1546-1548.
[10]李硕,汪善勤,张美琴.基于可见-近红外光谱比较主成分回归、偏最小二乘回归和反向传播神经网络对土壤氮的预测研究[J].光学学报,2012,32(8):289-293.
[11]褚小立,许育鹏,陆婉珍.偏最小二乘方法在光谱定性分析中的应用研究[J].现代仪器,2007(5):13-15.