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Usage of multivariate calibration methods for the analysis of Mn in complex matrices like glass using Laser-Induced Breakdown Spectroscopy (LIBS).

In the present work, we discuss the evaluation and optimization of a stand-off laser induced breakdown spectroscopy (ST-LIBS) system, developed indigenously for remote analysis of heavy elements in soil. A compact Q-switched Nd:YAG laser operating at fundamental wavelength 1064 nm was used for plasma generation at distances up to 6 meters. Techniques for optimal experimental results were evaluated for detection of Cd, Cr, Pb, Mo and Ni in soil. The system was evaluated with two NIST certified soil samples. The effect of working distance on the LIBS signal is also discussed briefly. Results confirm the capabilities of the system for remote monitoring.

The penultimate aim of all analytical techniques is to provide best quantitative information with high sensitivity and accuracy. Such techniques then can be ideal to estimate the concentration of different species in a sample or to measure the surface concentration and so on. The preliminary step involved in quantitative analysis is evaluation of the system response for a given amount of the analyte under investigation. Different methodologies are proposed and executed for the above mentioned purpose. In this work, we have demonstrated the performance of chemometric methods to study the atomic emissions from simulated nuclear waste glasses. Five samples of iron‐phosphate glasses were synthesized by doping Cr, Sr, and Ti at various concentrations. The spectra recorded using laser‐induced breakdown spectroscopy (LIBS) system were analyzed using principal component analysis (PCA), partial least square‐discriminant analysis (PLS‐DA), and support vector machines (SVM) for the classification. As compared with the PLS‐DA, SVM provided better results for classification with accuracy of 100% on both calibration and validation sets, respectively. The quantitative analysis of glass samples is carried out using partial least square regression (PLSR) and support vector regression (SVR). The root mean squared error of prediction (RMSEP) is found to be 0.16, 0.20, and 0.08 (wt%) for Cr, Sr, and Ti using PLSR. The detailed investigation elucidates the advantages of chemometrics in handling the complex LIBS spectrum of glass samples and their significance in process control of nuclear waste, nuclear forensics, and so on.