The feasibility and accuracy of several combination classification models, i.e., quadratic discriminant analysis (QDA), random forest (RF), Bernoulli naïve Bayes (BNB), and support vector machine (SVM) classification models combined with...

A focus-offset collinear dual-pulse laser-induced breakdown spectroscopy (FOC-DP-LIBS) is designed and used to investigate the laser ablation and spectral intensity with an aluminum alloy sample. The laser crater morphologies and ablation volumes were measured. An inter pulse time delay dependent ablation efficiency on a nanosecond laser heated sample was observed, which was similar to the trend of spectral intensity versus inter pulse time delay in the delay time less than 3 s. Based on the observation, the nanosecond pulse laser pre-heating effect on subsequent second laser ablation and signal enhancement is discussed, which will be helpful for understanding the ablation and signal enhancement mechanism in standard collinear DP-LIBS technique.

The identification of heavy metals in soil, specifically arsenic (As) and chromium (Cr), is critical for evaluating the preservation and quality of the soil. Laser-induced breakdown spectroscopy has become a...

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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.