基于多元定标法的脐橙Pb元素激光诱导击穿光谱定量分析

Quantitative analysis of laser induced breakdown spectroscopy of Pb in navel orange based on multivariate calibration

建立了利用激光诱导击穿光谱(LIBS)技术分析脐橙中Pb含量的多元线性回归定量分析模型.选用脐橙中Ca II 393.37 nm与Ca II 396.84 nm特征谱线强度之和、Pb I 405.78 nm特征谱线强度、在405.03—405.96 nm范围内Pb元素的谱线积分强度作为自变量,得到了回归关联式,通过方差分析和回归统计验证了关联式的可行性.结果表明,多元线性回归模型预测值与原子吸收光谱法检测值之间的相对误差最大值为12.99%,平均值为4.87%,并且利用这两种方法得到的结果拟合效果很好,拟合度达到0.995.这说明多变量的定标法能比较充分地利用光谱中的信息,降低基体效应的影响,从而提高LIBS定量分析的精确度,并对LIBS技术进一步应用于水果中重金属元素的定量检测提供了实验指导.

The detection accuracy of laser induced breakdown spectroscopy （LIBS） is affected by system parameters, ambient gas, matrix effect, sample morphology, calibration methods etc. Heavy metals in Gannan navel orange are determined by LIBS in our laboratory. The experimental parameters are optimized. In this work, multivariate linear regression model is used to predict the concentration of Pb element in navel oranges. The real concentration of Pb is quantitatively determined by atomic absorb spectroscopy （AAS）. The concentration is set as dependent variable, while the intensity of PbⅠ 405.78 nm, the intensity sum of Ca Ⅱ 393.37 nm and Ca Ⅱ 396.84 nm, and the integrated intensity in a range of 405.03-405.96 nm are taken as independent variable. The calibration results indicate that the maximum relative error between the predicted Pb concentration from the multiple linear regression model and the measured one by the AAS is 12.99%, and the average relative error of the samples is 4.87%. And the fitting degree of the results of two methods is 0.995. The result shows that the multivariate calibration method can utilize the information about the spectra and reduce the influence of the matrix effect. The multivariate linear regression model is proved to be feasible in improving the prediction accuracy of LIBS.