CAJ | 학술논문

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500～2500 nm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。
기우근홍외광보기술결합련속투영산법화회귀계수분석대검측토양총담함량진행연구。채집농전토양양본근홍외광보수거，토양양본수량공394개。유우원시광보수거량대，재500～2500 nm광보파장범위기출상，위간화모형，재원시광보기출상채용련속투영산법화회귀계수분석제취특정변량，이량충변량선택방법제취적특정변량작위수입，분별채용편최소이승회귀（PLS）、다원선성회귀（MLR）화최소이승지지향량궤（LS-SVM）건모방법건립총담예측모형，공건립료9개예측모형，최우예측집적결정계수위0.81，잉여예측편차RPD위2.26。연구표명，기우련속투영산법화회귀계수분석선택적특정파장가이응용우근홍외광보검측토양총담함량，동시가이대대간화모형，괄합개발편휴식토양양분검측의。
Visible near infrared spectra technology was adopted to detect soil total nitrogen content .394 soil samples were col-lected 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 and 2nd-D) were compared to determine the optimal wavelength range and pretreatment method for analysis .The results with 5 dif-ferent pretreatment methods were not improved compared to that both of full spectra PLS model and wavelength reduction spec-tra 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 wave-length 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 separate-ly .These calibration models were also compared to select the best model to predict soil TN .In total ,9 different 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 7 sensitive variables selected by RC using 0 .0002 regression coefficient threshold value obtained the best result with R2pre ,RMSEP and RPD as 0 .81 ,0 .0031 and 2 .26 .This prediction accu-racy is classied to be very good .For all the models ,it could be concluded that RCA and SPA could be very useful ways to select-ed sensitive wavelengths ,and the selected wavelengths were effective to estimate soil TN .It is recommended to adopt SPA vari-able selection or RCA variable selection method with both linear and nonlinear calibration models for measurement of the soil TN using Vis-NIR spectroscopy technology ,and wavelengths selection could be very useful to reduce collinearity and redundancies of spectra .