Coupling Backpropagation Neural Network and AdaBoost Algorithm for Quantitative Analysis of Nickel via Laser-Induced Breakdown Spectroscopy

Abstract

A wide area of cropland or soil might well be contaminated with heavy metals, contaminating agricultural goods and posing a risk to human health. As a result, it is required to evaluate the concentration of heavy metals in soil. The combination between laser-induced breakdown spectroscopy (LIBS) and multivariate chemometrics methods was employed to determine heavy metal Ni concentration in twelve soil samples. The comparison between univariate calibration curve, traditional backpropagation neural network (BPNN), and hybrid BPNN-AdaBoost was presented. The result revealed that BPNN-AdaBoost outperformed other models with the coefficient determination calibration ( R G 2 ) , coefficient determination prediction ( R P 2 ) , root mean square error calibration (RMSEC), root mean square error prediction (RMSEP) are 0.985, 0.977, 2.04, 3.18, respectively. This study indicates that BPNN-AdaBoost can be adopted as a reliable chemometric technique to enhance the quantitative analysis of heavy metals based on LIBS.

Application of laser-induced breakdown spectroscopy with a generalized regression neural network and LASSO-type methods for estimation of arsenic and chromium in soil

“…The composition and concentration of the elements can be derived from the location and signal intensity of the analyzed lines. LIBS technology has a wide range of applications in the analysis of the substance composition and has been widely used in agricultural soil, 12 steel industry, 13 nuclear industry, 14 food safety, 15 biomedical diagnostics, 16 environmental monitoring, 17 and many other fields. For LIBS applications, quantitative analysis methods are particularly critical.…”