Spectral enhancement and quantitative accuracy improvement of trace metal elements in aqueous solutions using electrostatic-assisted laser-induced breakdown spectroscopy

Abstract

This paper introduced electrostatic-assisted laser-induced breakdown spectroscopy (LIBS) to enhance spectral intensity and improve the quantitative accuracy of trace metal elements (Cu, Al, Zn, Ca and Na) in aqueous solutions. Firstly, the effect of the voltage and pole-plate distance of the electrostatic field on the spectral intensity was investigated. The spectral intensity of metal elements was enhanced by changing DC voltage and the plate distance. Secondly, the effect of electrostatic-assisted LIBS on the quantitative analysis was investigated, and the limits of detection (LOD) and quantification (LOQ) were calculated with and without electrostatic-assisted LIBS. The use of electrostatic-assisted LIBS reduced the LOD and LOQ of metal elements by an order of magnitude, respectively. Finally, several regression analysis models including least squares regression analysis (PLSR), support vector machine regression analysis (SVR), backpropagation neural network regression analysis (BP-ANN), whale optimization algorithm-SVR (WOA-SVR), and whale optimization algorithm-BP (WOA-BP) were established to predict the concentration regression based on the collected spectral information. Compared to the traditional regression methods, the improved machine-learning algorithms based on intelligent optimization algorithms achieved the highest regression prediction accuracy of 99.81%. These findings confirm the capability and accuracy of electrostatic-assisted LIBS combined with machine-learning algorithms for detecting trace metal elements in aqueous solutions.