CAJ | 학술논문

在空气环境下,采用激光诱导击穿光谱( LIBS)技术对土壤成分进行检测,建立了基于遗传算法(GA)和偏最小二乘法(PLS)的定量分析模型。将配制的58个土壤样品分为定标集、监控集和预测集,对11种组分Mn, Cr, Cu, Pb, Ba, Al2 O3, CaO, Fe2 O3, MgO, Na2 O和K2 O的含量分别进行预测。结果表明, GA作为一种谱线选择的预处理方法,可以有效减少用于PLS建模的光谱谱线的数目,从而简化模型。对于土壤中的大部分组成成分,GA-PLS模型能够显著改善传统PLS模型的预测能力。以Mn元素为例,浓度预测均方根误差(RMSEP)从0.0215%降低至0.0167%,平均百分比误差(MPE)从8.10%降低至5.20%。本研究为进一步提高土壤的LIBS定量分析准确度提供了方法参考。
재공기배경하,채용격광유도격천광보( LIBS)기술대토양성분진행검측,건립료기우유전산법(GA)화편최소이승법(PLS)적정량분석모형。장배제적58개토양양품분위정표집、감공집화예측집,대11충조분Mn, Cr, Cu, Pb, Ba, Al2 O3, CaO, Fe2 O3, MgO, Na2 O화K2 O적함량분별진행예측。결과표명, GA작위일충보선선택적예처리방법,가이유효감소용우PLS건모적광보보선적수목,종이간화모형。대우토양중적대부분조성성분,GA-PLS모형능구현저개선전통PLS모형적예측능력。이Mn원소위례,농도예측균방근오차(RMSEP)종0.0215%강저지0.0167%,평균백분비오차(MPE)종8.10%강저지5.20%。본연구위진일보제고토양적LIBS정량분석준학도제공료방법삼고。
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.