Li Shefeng, Zheng Qi, Liu Xiaodan, Liu Peng, Yu Long
School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430023, China.
College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan 430023, China.
Molecules. 2024 Aug 5;29(15):3699. doi: 10.3390/molecules29153699.
Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.
研究高效、准确的土壤重金属检测技术对于建立土壤污染监测、预警和风险评估的现代体系具有重要意义，有助于土壤质量的持续改善和食品安全的保障。与传统检测技术相比，激光诱导击穿光谱法（LIBS）被认为是一种新兴且有效的重金属检测工具。受土壤基体效应的限制，用于土壤重金属检测的目标元素的LIBS信号容易受到干扰，从而影响定量检测的准确性。因此，人们研究了一系列信号增强方法。本研究旨在探讨NaCl和石墨等导电材料对利用LIBS定量检测土壤中铅（Pb）的影响，试图找到一种可靠的LIBS信号增强方法来测定土壤重金属元素。研究了NaCl和石墨添加量对光谱强度以及信号背景比（SBR）、信噪比（SNR）和相对标准偏差（RSD）等参数的影响，并基于对烧蚀坑三维轮廓数据和等离子体参数（等离子体温度和电子密度）的分析，探讨了NaCl和石墨的信号增强机理。针对添加了最佳量NaCl和石墨的土壤中Pb的定量检测，建立了包括偏最小二乘回归（PLSR）、最小二乘支持向量机（LS-SVM）和极限学习机（ELM）在内的单变量和多变量定量分析模型，并进一步比较了这些模型的性能。添加了最佳量石墨的PLSR模型获得了最佳预测性能，其决定系数Rp达到0.994。此外，在Pb的三条光谱线中，Pb I 405.78 nm的单变量模型显示出最佳预测性能，Rp为0.984，最低检测限为26.142 mg/kg。总体结果表明，基于导电材料的LIBS信号增强方法结合适当的化学计量学方法可能是准确定量检测土壤中Pb的潜在工具，并可为环境监测提供参考。
