Qualitative Analysis of Glass Microfragments Using the Combination of Laser-Induced Breakdown Spectroscopy and Refractive Index Data

We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types (fused silica, flint, borosilicate and soda-lime) were involved in the tests. Four multivariate chemometric data evaluation methods (linear discrimination analysis, quadratic discrimination analysis, classification tree and random forest) for LIBS data were evaluated with and without data compression (principal component analysis). Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda-lime glasses. The developed methods can be used in forensic analysis.

Automated summary

We have successfully demonstrated that the qualitative discrimination analysis of sub-mm sized glass samples can be solved with high accuracy and reliability using multivariate chemometric evaluation of LIBS data or in combination with pre-screening based on refractive index data. Classification tree and random forest methods showed the most consistent and accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda-lime glasses.