An efficient method has been developed to analyze metal species by using laser‐induced breakdown spectroscopy (LIBS). A least squares support vector machine (LSSVM) was applied to quantitative analysis of multimetal samples with different matrix content, as LSSVM can select the input variables and convert the mapping to the feature space to obtain the linear performance. In this work, three kinds of metal species (alloy steel, ferrochromium alloy, and ferromanganese alloy) were used to construct the LSSVM model. The correlation coefficients (R²), the root‐mean‐square error of prediction (RMSEP), and the average relative error (ARE) were calculated according to the estimated concentrations of chromium (Cr), manganese (Mn), titanium (Ti), and molubdenum (Mo) using the LSSVM model. The results showed that RMSEP decreased from 1.7047%, 1.3453%, 1.9253%, and 2.9431% to 0.0747%, 0.0942%, 0.0192%, and 0.0809%. The ARE of Cr, Mn, Ti, and Mo decreased from 10.2370, 9.8276%, 13.2460%, and 16.2386% to 1.0179%, 1.3937%, 2.1228%, and 1.6257%, respectively. This study demonstrated that LSSVM is an effective algorithm to improve both the accuracy and stability for metal analysis using LIBS.

中文翻译：

---

使用最小二乘支持向量机的激光诱导击穿光谱法提高多金属分析的精度

通过使用激光诱导击穿光谱法（LIBS），已开发出一种有效的方法来分析金属种类。最小二乘支持向量机（LSSVM）被用于定量分析具有不同基质含量的多金属样品，因为LSSVM可以选择输入变量并将映射转换为特征空间以获得线性性能。在这项工作中，使用了三种金属种类（合金钢，铬铁合金和锰铁合金）来构建LSSVM模型。相关系数（R ²），根据估计的铬（Cr），锰（Mn），钛（Ti）和钼（Mo ）使用LSSVM模型。结果表明，RMSEP从1.7047％，1.3453％，1.9253％和2.9431％降至0.0747％，0.0942％，0.0192％和0.0809％。Cr，Mn，Ti和Mo的ARE分别从10.2370、9.8276％，13.2460％和16.2386％降至1.0179％，1.3937％，2.1228％和1.6257％。这项研究表明，LSSVM是一种有效的算法，可以提高使用LIBS进行金属分析的准确性和稳定性。