应用可见/短波近红外光谱分析测量土壤有机质和速效钾含量。 光谱预处理包括平滑, 标准归一化, 多元散射校正和平滑结合一阶导数, 以消除系统噪声和外部干扰, 分别应用偏最小二乘和最小二乘支持向量机方法建立校正模型, 模型的输入为基于连续投影算法得到的特征波长。 比较显示基于连续投影算法得到的特征波长为输入的最小二乘支持向量机优于偏最小二乘法建模。 模型评价指标由相关系数和预测均方误差表示。 有机质的相关系数和预测均方误差分别0.860 2和2.98, 速效钾为0.730 5和15.78。 表明基于连续投影算法可见/短波近红外光谱利用最小二乘支持向量机建模, 可以作为一个精确的土壤有机质和速效钾的测定方法。

Visible and short wave infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement of soil organic matter (OM) and available potassium (K). Four types of pretreatments including smoothing, SNV, MSC and SG smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were implemented for calibration models. The LS-SVM model was built by using characteristic wavelength based on successive projections algorithm (SPA). Simultaneously, the performance of LS-SVM models was compared with PLSR models. The results indicated that LS-SVM models using characteristic wavelength as inputs based on SPA outperformed PLSR models. The optimal SPA-LS-SVM models were achieved, and the correlation coefficient (r), and RMSEP were 0.860 2 and 2.98 for OM and 0.730 5 and 15.78 for K, respectively. The results indicated that visible and short wave near infrared spectroscopy (Vis/SW-NIRS) (325～1 075 nm) combined with LS-SVM based on SPA could be utilized as a precision method for the determination of soil properties.