基于主成分回归的土壤重金属LIBS定量分析方法研究

采用激光诱导击穿光谱(LIBS)技术结 合主成分分析(PCA )和基于PCA的偏最小二乘回归法(PLSR ),对8种土壤样品进 行分类及Cr元素含量的分析研究。对比分析了不同土壤样品基体元素的特征光谱,利用PCA 对8种土壤样品进行分类,并采用PLSR定量分析不同土壤中Cr元素的含量。研究结果表 明,不同土壤样品中的基体元素特征光谱出现较大差异,8种土壤样品经PCA分析后大致 分为3类；利用PLSR训练模型得到的定标曲线将拟合相关系数提高至0.986,对同一类的 土壤样品Cr的含量预测相对误差小于7.5%,对非同一类土壤样品Cr的预测相对 误差较大。这说明,对待测样品定量分析前,采用PCA对土壤样品分类可以提高对待测样品 重金属元素定量检测的精确度,对建立定量分析模型提供指导。

Quantitative analysis of Cr in soils using LIBS with principal components regres sion

Principal components analysis (PCA) and partial least squares regression (PLSR) were used to classify and quantita tive investigate soils using laser-induced breakdown spectroscopy (LIBS).Comparative analyzing t he (LIBS) spectra of different soils,eight kinds of soils were classified by PCA,and PLSR were then u sed to measuring the concentration of chromium (Cr) in eight kinds of soils,respectively.We optimize the principal components,then the results indicate that the PCA can classify eight kinds of soil s into three clusters, and the correlation coefficient of the calibration curve with PLSR is 0.986.For different clusters,the error of the prediction for the concentratio n analysis of Cr in soils is large.For the same cluster,the predicted errors of Cr for two soils ar e lower than 7.5%.It shows that using PCA on the soil samples cl assification before the quantitative analysis can improve the accuracy of the quantitative an alysis for heavy metal elements and guide the model building.