为研究土壤金属元素近红外光谱定量校正模型适应性, 采用近红外光谱结合偏最小二乘算法, 针对风干土壤中的K, As, Hg, Cu, Zn, Pb, Cr, Cd元素, 在剔除异常值后, 建立定量校正模型; 并对风干、 烘干处理的外部验证集样品分别预测上述元素含量。 结果表明, 风干外部验证集样品的预测值-参考值相关系数皆大于相应烘干外部验证集样品的预测值-参考值相关系数; 风干外部验证集各元素的预测值-参考值均具有显著的相关关系, 烘干外部验证集中K, Hg, Cr的预测值-参考值之间不具有显著的相关关系。 对土壤金属元素近红外光谱定量校正模型的适应性进行了初步研究, 可为土壤中金属元素快速定量监测方法以及农产品产地环境监测等提供一定的参考。

In order to research the adaptability of the NIR quantitative calibration models for the metal elements in soil, in this research, near-infrared spectroscopy combined with partial least square regression algorithm was applied to develop the quantitative calibration models of K, As, Hg, Cu, Zn, Pb, Cr, Cd in the air-dry soil samples after the outliers having been eliminated. The content prediction of the elements mentioned above was carried out for the air-dry soil samples and the oven-dry soil samples for the external validation set respectively. The result indicates that the correlation coefficient between the estimated and specified values of the air-dry soil samples is larger than that of the oven-dry soil samples for each element. A significant correlation exists between the estimated and specified values of the air-dry soil samples for each element, while there is no significant correlation exists between that of K, Hg, Cr of the oven-dry soil samples. In this thesis, the adaptability of the NIR quantitative calibration models for the metal elements in soil was researched preliminarily, which, to some extent, can provide reference for the rapid quantitative monitoring method of the metal elements in soil and the monitoring of the home environment of agricultural products.