激光诱导击穿光谱结合PLS算法检测脐橙皮中Pb元素

Detection of Pb in navel orange by peel laser induced breakdown spectroscopy coupled with PLS

采用LIBS技术与火焰原子吸收法(AAS),获取23个浓度梯度的含Pb元素脐橙样品的LIBS光谱及Pb元素真实浓度信息,再对LIBS谱线信息进行数据预处理,建立PLS定量分析模型。当采用9点平滑结合SNV作为预处理方法时,PLS模型最佳,其校正集相关系数(R_t)、交叉验证均方根误(RMSECV)、预测集相关系数(R_p)、预测均方根误差(RMSEP)分别为0.9633,1.56,0.9542和2.58,脐橙中Pb元素预测结果的平均相对误差为6.9%。与小组前期对脐橙中Pb元素单变量和多元定标法相比,LIBS结合PLS建模时提高对脐橙微量重金属检测的准确性。

In order to improve the detection accuracy and sensitivity in detecting Pb in orange by laser induced breakdown spectroscopy（ LIBS）,the lab polluted oranges were taken as examples. The spectral information of oranges was obtained by LIBS and the real concentrations were achieved by atomic absorption spectrometry（ AAS）. Based on intensity of plasma,the spectral data processing method included different smooth points and pre- treatment method were collected and then PLS model was built. The quality of model criterion was evaluated,and the regression coefficient（ R）,the root mean square error of cross validation（ RMSECV） and the root mean square error of prediction（ RMSEP） were compared and analyzed. The experimental results showed that four indicators of PLS model after 9 points smoothing and standard normal vitiate were found reaching 0. 9633,1. 56,0. 9542 and 2. 58,respectively. The average relative error of prediction model was only 6. 87%. It has a greater effect to improve the detection of Pb in orange compared with previous studies.