Material Identification Using Laser Spectroscopy and Pattern Recognition Algorithms

Abstract

We report on pattern recognition algorithms in discriminant analysis, which were used on Laser Induced Breakdown Spectroscopy (LIBS) spectra (intensity of signal against wavelength) for metal identification and sorting purposes. In instances where accurate elemental concentrations are not needed, discriminant analysis can be applied, to compare and match spectra of “unknown“ samples to library spectra of calibration samples. This type of “qualitative“ pattern recognition analysis has been used here for material identification and sorting. Materials of different matrix materials (e.g. Al, Cu, Pb, Zn, vitrification glass, steels, etc.) could be identified with 100% certainty, using Principle Component Analysis and the Mahalanobis Distance algorithms. The limits within which the Mahalanobis Distance indicate a match status of Yes, Possible or No were investigated. The factors, which dictate these limits in LIBS analysis, were identified - (i) spectrum reproducibility and (ii) the sample-to-sample homogeneity. If correctly applied the combination of pattern recognition algorithms and LIBS provide a useful tool for remote and in-situ material identification problems, which are of a more “identify-and-sort” nature (for example those in the nuclear industry).