近红外光谱技术结合RCA和SPA方法检测土壤总氮研究  

Measurement of Soil Total Nitrogen Using Near Infrared Spectroscopy Combined with RCA and SPA

作　　者：方孝荣[1] 章海亮[2,3] 黄凌霞[4] 何勇[2] 

机构地区：[1]金华职业技术学院,浙江金华321017 [2]浙江大学生物系统工程与食品科学学院,浙江杭州310058 [3]华东交通大学轨道学院,江西南昌330013 [4]浙江大学动物科学学院,浙江杭州310058

出　　处：《光谱学与光谱分析》2015年第5期1248-1252,共5页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61134011);浙江省科技计划项目(2011C22070);浙江省蚕桑产业科技创新团队项目(2011R50028)资助

摘　　要：基于近红外光谱技术结合连续投影算法和回归系数分析对检测土壤总氮含量进行研究。采集农田土壤样本近红外光谱数据,土壤样本数量共394个。由于原始光谱数据量大,在500～2 500nm光谱波长范围基础上,为简化模型,在原始光谱基础上采用连续投影算法和回归系数分析提取特征变量,以两种变量选择方法提取的特征变量作为输入,分别采用偏最小二乘回归（PLS）、多元线性回归（MLR）和最小二乘支持向量机（LS-SVM）建模方法建立总氮预测模型,共建立了9个预测模型,最优预测集的决定系数为0.81,剩余预测偏差RPD为2.26。研究表明,基于连续投影算法和回归系数分析选择的特征波长可以应用于近红外光谱检测土壤总氮含量,同时可以大大简化模型,适合开发便携式土壤养分检测仪。Visible near infrared spectra technology was adopted to detect soil total nitrogen content.394 soil samples were collected from Wencheng,Zhejiang province to be used for calibration model（n=263）and independent prediction set（n=131）.Raw spectra and wavelength-reduced spectra with five different pretreatment methods（SG smoothing,SNV,MSC,1st-D and2nd-D）were compared to determine the optimal wavelength range and pretreatment method for analysis.The results with 5different pretreatment methods were not improved compared to that both of full spectra PLS model and wavelength reduction spectra model.Spectral variable selection is an important strategy in spectrum modeling analysis,because it tends to parsimonious data representation and can lead to multivariate models with better performance.In order to simply calibration models,the wavelength variables selected by two different variable selection methods（i.e.regression coefficient analysis（RCA）and successive projections algorithm（SPA）were proposed to be the inputs of calibration methods of PLS,MLR and LS-SVM models separately.These calibration models were also compared to select the best model to predict soil TN.In total,9different models were built and the best results indicated that PLS,MLR and LS-SVM obtained the highest precision with determination coefficient of prediction R2pre=0.81,RMSEP=0.0031 and RPD=2.26 based on wavelength variables selected by RCA（0.0002）and SPA as inputs of models.SPA-MLR model and other three models based on 7sensitive variables selected by RC using 0.0002 regression coefficient threshold value obtained the best result with R2 pre,RMSEP and RPD as 0.81,0.0031 and 2.26.This prediction accuracy is classied to be very good.For all the models,it could be concluded that RCA and SPA could be very useful ways to selected sensitive wavelengths,and the selected wavelengths were effective to estimate soil TN.It is recommended to adopt SPA variable selection or RCA variable selection method with both linear and nonlinear cal

关 键 词：近红外光谱 土壤总氮 连续投影算法 回归系数分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]