在水体重金属激光诱导等离子体光谱定量分析中,一般提取光谱的多个特征变量进行浓度反演,但变量之间所包含的光谱信息可能存在重叠,回归模型的复杂程度也随之增大.为提取有效特征变量,研究了基于偏最小二乘法(PLS)的变量筛选方法.该方法以待测元素浓度为因变量,多个与待测元素浓度相关的LIBS光谱特征值为自变量,进行PLS建模;依据各原始变量的投影重要性指标值进行变量筛选,提取最优变量子集.结果表明湖库水体中Pb元素的最优变量子集为Pb Ⅰ 405.78 nm峰值及峰值前相邻点光谱值、内标校正值和信背比值,训练集的复相关系数R2m=0.912.以优化变量组合进行PLS回归分析,测试集预测结果的RSD和RE分别为10.2％和7.9％,显著优于内标法的预测结果.结果还表明,变量筛选结果对于不同元素和不同水样具有一定适用性.研究结果为水体重金属LIBS定量分析提供了优质特征数据,研究方法为其他涉及变量筛选的定量分析提供了参考.%The spectral characteristics of multiple variables were regularly extracted for concentration inversion in the quantitative analysis of heavy metal with LIBS.However,overlapped spectral information might be contained among variables and the complexity of the regression model also would be increased.To extract effective feature variables,the variable selection method based on PLS was studied.PLS model established with the concentration of the element under test used as the dependent variable and multiple LIBS spectrum characteristic variables used as independent variables.The optimal variable subset was extracted on the basis of the original variable importance projection index for variable selection.The results showed that the optimal variable subset for Pb was composed of the Pb Ⅰ 405.78 nm peak,the spectral values before Pb Ⅰ 405.78 nm peak,the intensity corrected by the internal standard element and the signal to background in lake water,and the correlation coefficient square of the training set was 0.912.The optimal variable subset was used for PLS regression analysis,and RSD and RE of the test set were 10.2％and 7.9％,respectively,significantly better than the predictive results of the internal standard.It also showed that variable screening results for different elements and different water samples was applicative to some extent,but the internal standard element failed in correction for different water samples.The results provided high quality characteristic data for the LIBS quantitative analysis,and the methods also provided a reference for other quantitative analysis involving variable selection.
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