Abstract Optimization of testing parameters are the prerequisite for laser induced breakdown spectroscopy (LIBS) further data analysis, which can offer important reference value for the soil detection in the field. This work investigated the influence of the main testing parameters laser energy (LE), delay time (DT), and lens to sample distance (LTSD) of LIBS system. Based on the spectral characteristic of main elements in soils, the testing parameters of LIBS for soil detection were obtained and verified. The optimization analysis of three testing parameters LE (50–160 mJ), DT (0.5–4.5 μs), and LTSD (94–102 mm) were conducted by response surface methodology (RSM). Central composite design (CCD) in RSM was introduced to carry out experimental runs. The combined signal-background-ratio (SBR) of characteristic spectral lines from main elements (Si, Fe, Mg, Ca, Al, Na, K, etc.) in soil were defined as the objective function (named YSBR). The interaction influences among three independent variables (LE, DT, and LTSD) on soil plasma characteristics were explored and the optimized testing parameters of LIBS were summarized. Results revealed as follows: the factor LE showed a remarkable linear effect to YSBR, and factors DT and LTSD exhibited opposite results. The interactive items of three factors displayed a non-significant relationship. Meanwhile, the quadratic items of LE2, DT2 and LTSD2 offered significant surface relationships. Through the RSM analysis, the optimized testing parameters for LIBS soil detection were LE: 103.09 mJ; DT: 2.92 μs; LTSD: 97.69 mm; and a peak value YSBR of 198.60. After that, the LIBS data of 21 representative soil samples were collected under the optimized LIBS testing parameters. Partial least squares regression (PLSR) was introduced to predict the main elemental contents. Results indicated that PLSR models offered promising outputs for predicting the contents of Al, Ca, Fe, K, Mg, and Na in the sampled soil, which revealed that the testing parameters of LIBS optimized by RSM were available. This work provided a theoretical basis for the accurate LIBS data analysis and regarded as a technical support for the field soil LIBS testing parameters selection.

中文翻译：

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优化LIBS测试参数的响应面法：以土壤元素含量分析为例

摘要 检测参数的优化是激光诱导击穿光谱（LIBS）进一步数据分析的前提，可为现场土壤检测提供重要的参考价值。这项工作研究了 LIBS 系统的主要测试参数激光能量 (LE)、延迟时间 (DT) 和透镜到样品距离 (LTSD) 的影响。根据土壤中主要元素的光谱特征，获得并验证了LIBS用于土壤检测的测试参数。通过响应面方法 (RSM) 对三个测试参数 LE (50–160 mJ)、DT (0.5–4.5 μs) 和 LTSD (94–102 mm) 进行优化分析。引入 RSM 中的中心复合设计 (CCD) 来进行实验运行。来自主要元素 (Si, 土壤中的 Fe、Mg、Ca、Al、Na、K 等）被定义为目标函数（命名为 YSBR）。探讨了三个自变量（LE、DT和LTSD）之间的相互作用对土壤等离子体特性的影响，并总结了LIBS的优化测试参数。结果表明：LE因子对YSBR呈现显着的线性效应，DT和LTSD因子表现出相反的结果。三个因素的交互项显示非显着关系。同时，LE2、DT2 和 LTSD2 的二次项提供了显着的表面关系。通过RSM分析，LIBS土壤检测的优化测试参数为LE：103.09 mJ；DT：2.92 微秒；LTSD：97.69 毫米；和 198.60 的峰值 YSBR。之后，在优化的 LIBS 测试参数下收集了 21 个具有代表性的土壤样品的 LIBS 数据。引入偏最小二乘回归（PLSR）来预测主要元素含量。结果表明，PLSR 模型为预测采样土壤中 Al、Ca、Fe、K、Mg 和 Na 的含量提供了有希望的输出，这表明 RSM 优化的 LIBS 测试参数是可用的。该工作为准确分析LIBS数据提供了理论依据，为现场土壤LIBS测试参数选择提供了技术支持。这表明通过 RSM 优化的 LIBS 测试参数是可用的。该工作为准确分析LIBS数据提供了理论依据，为现场土壤LIBS测试参数选择提供了技术支持。这表明通过 RSM 优化的 LIBS 测试参数是可用的。该工作为准确分析LIBS数据提供了理论依据，为现场土壤LIBS测试参数选择提供了技术支持。