土壤养分的近红外漫反射光谱预测模型分析

Analysis of Prediction Model for Soil Nutrients by Near Infrared Diffuse Reflectance Spectroscopy

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 is0.040by principal component regression and0.034by partial least squares regression respectively,with determination coefficient R2=0.8731by principal component regression and R2=0.9035by 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.