分析化学Issue(2)：181-186,6.DOI:10.11895/j.issn.0253-3820.140668
基于遗传算法和偏最小二乘法的土壤激光诱导击穿光谱定量分析研究
Quantitative Analysis of Soil by Laser-induced Breakdown Spectroscopy Using Genetic Algorithm-Partial Least Squares
摘要
Abstract
Laser-induced breakdown spectroscopy ( LIBS) was used to detect the compositions of soil in the air, and the quantitative analysis model with genetic algorithm-partial least squares ( GA-PLS ) was established. A total of fifty-eight soil samples were split into calibration, monitoring and prediction sets. Eleven soil compositions including Mn, Cr, Cu, Pb, Ba, Al2 O3 , CaO, Fe2 O3 , MgO, Na2 O, and K2 O were quantitatively analyzed. The results demonstrated that, as a pretreatment method for optimizing the selection of spectral lines, GA could be effectively used to reduce the number of spectral lines for use in building PLS model, and hence simplify the quantitative analysis model. More importantly, for most of the soil compositions, GA-PLS could significantly improve the prediction ability compared with the conventional PLS model. Take Mn as an example, the root-mean-square error of prediction ( RMSEP ) was decreased from 0. 0215% to 0 . 0167%, and the mean percent prediction error ( MPE ) was decreased from 8 . 10% to 5 . 20%. The research provides an approach for further improving the accuracy of LIBS quantitative analysis in soil.关键词
激光诱导击穿光谱/遗传算法/偏最小二乘法/土壤Key words
Laser-induced breakdown spectroscopy/Genetic algorithm/Partial least squares/Soil compositions analysis引用本文复制引用
邹孝恒,郝中骐,易荣兴,郭连波,沈萌,李祥友,王泽敏,曾晓雁,陆永枫..基于遗传算法和偏最小二乘法的土壤激光诱导击穿光谱定量分析研究[J].分析化学,2015,(2):181-186,6.基金项目
国家重大科学仪器设备开发专项(No.2011YQ160017),中国博士后科学基金(No.2013M542014)和中央高校基本科研业务费(Nos. CXY13Q021, CXY13Q022)资助This work was supported by the National Major Scientific Instrument and Equipment Development Project (No.2011YQ160017), the 54-class General Financial Grant from the China Postdoctoral Science Foundation (No.2013M542014), and the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Nos. CXY13Q021, CXY13Q022) （No.2011YQ160017）
