In recent years, LIBS quantitative analysis based on multivariate regression has received considerable attention, and variable selection is critical for improving accuracy of multivariate regression analysis of LIBS. In the present study, sequential backward selection combined with random forest was proposed to improve detection accuracy of sulfur and phosphorus in steel. First, LIBS spectrum line of S and P was identified by the NIST database. Second, input variables for RF calibration model were selected and optimized by SBS, and RF model parameters (n_tree and m_try) were optimized by out-of-bag (OOB) estimation. Finally, optimized input variables and model parameters were employed to build an SBS-RF calibration model for quantitative analysis of P and S in steel. Results showed that the SBS-RF model provided good predictions for S (R² = 0.9991) and P (R² = 0.9994) compared with those provided by the univariate method, PLS model and traditional RF model. Thus, LIBS coupled with SBS-RF is an effective method for quality supervision and control of steel products.

中文翻译：

---

LIBS技术和顺序向后选择随机森林（SBS-RF）定量检测合金钢中的有害元素

近年来，基于多元回归的LIBS定量分析受到了广泛的关注，变量选择对​​于提高LIBS多元回归分析的准确性至关重要。为了提高钢中硫，磷的检测精度，提出了顺序后向选择与随机森林相结合的方法。首先，通过NIST数据库识别S和P的LIBS谱线。其次，选择并通过SBS优化的RF校准模型的输入变量，和RF模型参数（Ñ_树和米_试）是通过袋外（OOB）估算进行优化的。最后，采用优化的输入变量和模型参数建立了SBS-RF校准模型，用于钢中P和S的定量分析。结果表明，与单变量方法，PLS模型和传统RF模型提供的预测相比，SBS-RF模型对S（R ² = 0.9991）和P（R ² = 0.9994）提供了良好的预测。因此，LIBS与SBS-RF结合使用是一种有效的钢铁产品质量监督和控制方法。