The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

Détails

ID Serval
serval:BIB_0FE46385D2D9
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints
Périodique
Forensic Science International
Auteur(s)
Muehlethaler C., Massonnet G., Esseiva P.
ISSN
0379-0738
Statut éditorial
Publié
Date de publication
06/2011
Peer-reviewed
Oui
Volume
209
Numéro
1-3
Pages
173-182
Langue
anglais
Résumé
The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.
Mots-clé
Paint, Infrared, Raman, Chemometrics, Principal component analysis, Hierarchical clusters analysis
Création de la notice
27/06/2011 10:47
Dernière modification de la notice
20/08/2019 13:36
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