Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy

Abstract

Effects of different parameters regarding partial least squares (PLS) regression analysis are investigated for quantitative analysis of water-submerged brass samples. The concentrations of Cu and Zn in various brass alloys were quantified using PLS, and the performance after different signal processing steps (normalization, smoothing, and background subtraction) and database segmentation by excitation temperature is compared. In addition, the effects of averaging numbers on the results are examined. From the results, normalization was found to be the most effective among three established signal processing methods. The effects of both peak and background fluctuations seen in the signals are reduced by normalization. It was found that temperature segmentation of the database in an appropriate range, which should be high enough for reliable peak detection, can further improve the accuracy of PLS calculations. The proposed method is applicable in real time, and can potentially be used for automated fast and accurate measurements of solids at oceanic pressures.