Zhang Rongling, Song Chenjia, Liu Jingzhong, Zhang Tianlong, Tang Hongsheng, Li Hua
Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China.
College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an 710065, China.
Anal Chem. 2025 Jul 15;97(27):14246-14255. doi: 10.1021/acs.analchem.5c00630. Epub 2025 Jun 30.
Microplastics (MPs) have emerged as a critical environmental pollutant, causing composite pollution through their widespread production, usage, and disposal, as well as their capacity to carry other contaminants such as heavy metals. This study presents a novel approach combining laser-induced breakdown spectroscopy (LIBS) with machine learning for the simultaneous quantitative detection of three metals (Cr, Pb, and Cu) in 25 contaminated poly(methyl methacrylate) (PMMA) samples with a diameter of 2 μm. The effects of different preprocessing methods and variable selection techniques on the predictive performance of partial least-squares (PLS) calibration models were investigated. Based on optimized input variables and model parameters, PLS calibration models were developed using mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination () as evaluation metrics. The models of standard normal variate-competitive adaptive reweighted sampling-PLS (SNV-CARS-PLS) for Cr and Pb, and wavelet transform-CARS-PLS (WT-CARS-PLS) for Cu, demonstrated superior correlation relationships (Cr: = 0.9750, Pb: = 0.9759, Cu: = 0.9088) compared to univariate calibration methods. The values of RMSE for Cr, Pb, and Cu decreased by 5.495, 9.170, and 3.765 ppm, respectively, while values of MRE decreased by 71.73%, 65%, and 66.81%, respectively. The values of ratio of prediction to deviation (RPD) for three models in -leave-one-out cross-validation (LOOCV) were 20.4, 31.6, and 31.6 respectively. Furthermore, the limits of detection (LODs) for the three heavy metal elements were ≤1.534 ppm. The SNV/WT-CARS-PLS method significantly improved quantitative analysis accuracy, providing essential theoretical and technical support for composite pollution monitoring and prevention in MPs.
微塑料（MPs）已成为一种关键的环境污染物，通过其广泛的生产、使用和处置，以及携带重金属等其他污染物的能力，造成复合污染。本研究提出了一种将激光诱导击穿光谱法（LIBS）与机器学习相结合的新方法，用于同时定量检测25个直径为2μm的受污染聚甲基丙烯酸甲酯（PMMA）样品中的三种金属（铬、铅和铜）。研究了不同预处理方法和变量选择技术对偏最小二乘（PLS）校准模型预测性能的影响。基于优化的输入变量和模型参数，使用平均相对误差（MRE）、均方根误差（RMSE）和决定系数（）作为评估指标，建立了PLS校准模型。铬和铅的标准正态变量-竞争自适应重加权采样-PLS（SNV-CARS-PLS）模型以及铜的小波变换-CARS-PLS（WT-CARS-PLS）模型，与单变量校准方法相比，显示出更好的相关关系（铬：=0.9750，铅：=0.9759，铜：=0.9088）。铬、铅和铜的RMSE值分别降低了5.495、9.170和3.765ppm，而MRE值分别降低了71.73%、65%和66.81%。在留一法交叉验证（LOOCV）中，三个模型的预测偏差比（RPD）值分别为20.4、31.6和31.6。此外，三种重金属元素的检测限（LOD）≤1.534ppm。SNV/WT-CARS-PLS方法显著提高了定量分析精度，为微塑料复合污染监测与防治提供了重要的理论和技术支持。
