中国激光, 2020, 47 (5): 0511001, 网络出版: 2020-05-12
基于激光诱导击穿光谱技术寻优定量分析土壤中Mn元素 下载: 1039次
Quantitative Analysis of Mn in Soil Based on Laser-Induced Breakdown Spectroscopy Optimization
激光光学 支持向量机 激光诱导击穿光谱技术 土壤 laser optics support vector machine laser induced breakdown spectroscopy soil
摘要
激光诱导击穿光谱技术(LIBS)与支持向量机(SVM)相结合用于分析土壤中Mn元素含量。44个土壤样品采集于安徽淮北地区,采用Kennard-Stone(K-S)方法将样品划分为训练集(34个)和测试集(10个),分别使用多元线性回归(MIR)、网格搜索法(GSM)、遗传算法(GA)、粒子群优化(PSO)和最小二乘法(LS)建立定量分析模型。结果表明:MIR、GSM和PSO模型所得到的训练集相关系数 Rtra2只有0.861、0.866和0.862,测试集相关系数 Rt2低于0.9,相对误差大于8.6%,误差较大;GA模型的 Rtra2大于0.93, Rt2小于0.9,训练时间较长,需减少训练时间和提高测试集相关性;LS模型寻优效果较好, Rtra2提高到0.998, Rt2提高到0.967,相对误差小,训练时间同比大幅度缩短,相关性好,泛化能力强,更适合用于土壤中Mn元素的快速检测。
Abstract
This paper uses laser-induced breakdown spectroscopy and support vector machine to analyze the content of Mn in soil. Forty-four soil samples were collected in Huaibei, Anhui. The samples were divided into training set (34 samples) and test set (10 samples) using Kennard-Stone (K-S) method. Multiple linear regression (MIR), grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares method (LS) were used to establish quantitative analysis models. The results show that the correlation coefficients of the training set of the MIR, GSM, and PSO models are only 0.861, 0.866, and 0.862, respectively. The correlation coefficients of the test set of corresponding models are lower than 0.9, the relative error is greater than 8.6%, and the error is larger. The of the GA model is greater than 0.93, and is less than 0.9. The training time of the GA model is long, so the training time must be reduced, and the correlation of the test set must be improved. The LS model works well with 0.998 and 0.967, and the relative error is small. The training time is greatly shortened year-on-year, correlation is good, and generalization ability is strong. The LS model is more suitable for the rapid detection of the Mn element in soil.
沙文, 李江涛, 鲁翠萍. 基于激光诱导击穿光谱技术寻优定量分析土壤中Mn元素[J]. 中国激光, 2020, 47(5): 0511001. Wen Sha, Jiangtao Li, Cuiping Lu. Quantitative Analysis of Mn in Soil Based on Laser-Induced Breakdown Spectroscopy Optimization[J]. Chinese Journal of Lasers, 2020, 47(5): 0511001.