Fang Xiao-rong, Zhang Hai-liang, Huang Ling-xia, He Yong
Guang Pu Xue Yu Guang Pu Fen Xi. 2015 May;35(5):1248-52.
Visible near spectra tecnnology 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 and 2nd-D) were compared to determine the optimal wavelength range and pretreatment method for analysis. The results with 5 different 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, 9 different models were built ahd the best results indicated that PLS, MLR and LS-SVM obtained the highest precision with determination coefficient of prediction R2(pre) =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 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 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.
采用可见近红外光谱技术检测土壤全氮含量。从浙江省文成县采集了394个土壤样本，用于建立校准模型（n = 263）和独立预测集（n = 131）。比较了原始光谱和采用五种不同预处理方法（标准正态变量变换（SG）平滑、标准正态变量变换（SNV）、多元散射校正（MSC）、一阶导数（1st - D）和二阶导数（2nd - D））的波长压缩光谱，以确定分析的最佳波长范围和预处理方法。与全光谱偏最小二乘法（PLS）模型和波长压缩光谱模型相比，五种不同预处理方法的结果均未得到改善。光谱变量选择是光谱建模分析中的一项重要策略，因为它有助于简化数据表示，并能得到性能更好的多元模型。为了简化校准模型，分别提出将两种不同变量选择方法（即回归系数分析（RCA）和连续投影算法（SPA））选择的波长变量作为PLS、多元线性回归（MLR）和最小二乘支持向量机（LS - SVM）模型校准方法的输入。还比较了这些校准模型，以选择预测土壤全氮的最佳模型。总共建立了9种不同的模型，最佳结果表明，基于RCA（0.0002）和SPA选择的波长变量作为模型输入，PLS、MLR和LS - SVM获得了最高精度，预测决定系数R2(pre) = 0.81，预测均方根误差RMSEP = 0.0031，相对预测偏差RPD = 2.26。基于使用0.0002回归系数阈值的RCA选择的7个敏感变量的SPA - MLR模型和其他三个模型，其R2(pre)、RMSEP和RPD分别为0.81、0.0031和2.26，获得了最佳结果。这种预测精度被归类为非常好。对于所有模型，可以得出结论，RCA和SPA可能是选择敏感波长的非常有用的方法，所选波长对估计土壤全氮有效。建议采用SPA变量选择或RCA变量选择方法，结合线性和非线性校准模型，使用可见 - 近红外光谱技术测量土壤全氮，波长选择对于减少光谱的共线性和冗余性可能非常有用。
