A method derived from genetic algorithm, principal component analysis and artificial neural networks to enhance classification capability of laser-Induced breakdown spectroscopy

Abstract

Selection of characteristic lines is a critical work for both qualitative and quantitative analysis of laser-induced breakdown spectroscopy; it usually needs a lot of time and effort. A novel method combining genetic algorithm, principal component analysis and artificial neural networks (GA-PCA-ANN) is proposed to automatically extract the characteristic spectral segments from the original spectra, with ample feature information and less interference. On the basis of this method, three selection manners: selecting the whole spectral range, optimizing a fixed-length segment and optimizing several non-fixed-length sub-segments were analyzed; and their classification results of steel samples were compared. It is proved that selecting a fixed-length segment with an appropriate segment length achieves better results than selecting the whole spectral range; and selecting several non-fixed-length sub-segments obtains the best result with smallest amount of data. The proposed GA-PCA-ANN method can reduce the workload of analysis, the usage of bandwidth and cost of spectrometers. As a result, it can enhance the classification capability of laser-induced breakdown spectroscopy.

Improvement in classification accuracy of stainless steel alloys by laser-induced breakdown spectroscopy based on elemental intensity ratio analysis

“…Further improvement of classification accuracy may be possible provided that other characteristics of the 98 intensity ratios besides the upper level difference are identified and included in the analysis. For instance, Zhang et al [24] applied a genetic algorithm for the selection of characteristic lines, and a similar technique may be applied for the 98 intensity ratios to investigate other combinations of emission lines.…”

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018

“…Li et al 85 combined GA with ANN to determine the elements copper and vanadium in steel samples with satisfactory quantitative results. Zhang et al 86 developed a method combining GA, PCA and ANN to select spectral segments from the original spectra to improve the LIBS performance and proved that use only a fixed-length segment appropriate provides better results than selecting the entire spectral range.…”