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• W3156014717 abstract "Conjoint laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy and high level data fusion were used to discriminate sixteen different glass samples by principal component analysis (PCA). LIBS provides information on the elemental composition of a sample and Raman spectroscopy gives information about the molecular composition. The results were compared to single LIBS and Raman measurements as well as to μ-XRF and SEM-EDS which are also frequently used in glass analysis. The direction dependent Mahalanobis distance between the sample point groups in the space of the most significant principle components was evaluated as a criterion for potential discrimination of the different samples. Very good discrimination (up to 99%) was achieved with conjoint LIBS and Raman and high level data fusion, as well as LIBS alone. The discrimination is based primarily on different contents of trace elements (e.g., Fe, Ti, Ba, Sr), which was confirmed by the μ-XRF and SEM-EDS and differences in fluorescence (detected by Raman). Besides the similar discrimination power of LIBS and conjoint LIBS and Raman, the combination and fusion of LIBS and Raman leads to greater distances between the data in the principal component space due to the additional information added by Raman. This should result in a lower misclassification rate of unknown samples. Results show that conjoint LIBS and Raman spectroscopy can be an alternative in the forensic analysis of glass samples. • Improved discrimination of automotive glasses by combination of LIBS and Raman. • High level data fusion was used for LIBS and Raman data. • Direction dependent Mahalabonis distance was used to check discrimination. • Comparison of this approach with μ-XRF and SEM-EDS." @default.
• W3156014717 created "2021-04-26" @default.
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• W3156014717 date "2021-06-01" @default.
• W3156014717 modified "2023-09-26" @default.
• W3156014717 title "Discrimination of automotive glass by conjoint Raman and laser-induced breakdown spectroscopy and multivariate data analysis" @default.
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• W3156014717 doi "https://doi.org/10.1016/j.sab.2021.106198" @default.
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