Analysis of paint cross-sections: a combined multivariate approach for the interpretation of μATR-FTIR hyperspectral data arrays.
The present research is aimed at introducing a suitable approach for the exploitation of the hyperspectral data obtained by μATR-FTIR analyses of paint cross-sections. The application of principal component analysis for chemical mapping is well-established, even if a very limited number of applications to μFTIR data have been reported so far in the field of analytical chemistry for cultural heritage. Moreover, in many cases, chemometric tools are under-utilized and the outcomes under-interpreted. As a consequence, results and conclusions may be considerably compromised. In an attempt to overcome such drawbacks, the present work is proposing a comprehensive and efficient procedure based on an interactive brushing approach, which combines the structural information of the score scatter plots and the spatial information of the principal component (PC) score maps. In particular, the study demonstrates not only how the multivariate approach may provide more information than the univariate one, but also how the integration of different chemometric tools may allow a more comprehensive interpretation of the results with respect to the studies up to now reported in the literature. The examination of the average spectral profile of each score cluster, jointly with the loading analysis, is functional to characterize each area investigated on the basis of its spectral features. A multivariate comparison with spectra of standard compounds, projected in the PC score space, helps in supporting the chemical identification. The approach was validated on two real case studies.
Microchemistry and Microscopy Art Diagnostic Laboratory, University of Bologna, Ravenna, Italy.
Giorgia
G
Sciutto
Paolo
P
Oliveri
Silvia
S
Prati
Marta
M
Quaranta
Silvia
S
Lanteri
Rocco
R
Mazzeo
D010150:Paint / Q000032:analysis*; D010151:Paintings; D025341:Principal Component Analysis; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*