4.7 Article

Towards the non-destructive analysis of multilayered samples: A novel XRF-VNIR-SWIR hyperspectral imaging system combined with multiblock data processing

Journal

ANALYTICA CHIMICA ACTA
Volume 1239, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.aca.2022.340710

Keywords

Hyperspectral imaging; Chemometrics; Multiblock data processing; Paintings; VNIR SWIR spectroscopy; XRF analysis

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The new challenge in the investigation of cultural heritage is to obtain stratigraphical information without sampling. In this paper, a commercialized analytical set-up is used in combination with multivariate and multiblock data processing to analyze multilayered paintings. The instrument allows for elemental and molecular information from superficial to subsurface layers.
The new challenge in the investigation of cultural heritage is the possibility to obtain stratigraphical information about the distribution of the different organic and inorganic components without sampling. In this paper recently commercialized analytical set-up, which is able to co-register VNIR, SWIR, and XRF spectral data simultaneously, is exploited in combination with an innovative multivariate and multiblock high-throughput data processing for the analysis of multilayered paintings. The instrument allows to obtain elemental and molecular information from superficial to subsurface layers across the investigated area. The chemometric strategy proved to be highly efficient in data reduction and for the extraction and integration of the most useful information coming from the three different spectroscopies, also filling the gap between data acquisition and data understanding through the combination of principal component analysis (PCA), brushing, correlation diagrams and maps (within and between spectral blocks) on the low-level fused. In particular, correlation diagrams and maps provide useful information for the reconstruction of a stratigraphic structure without the need to take any sample, thanks to the effective account for inter-correlation among data (variables), which is able to effectively characterize the possible combinations of components located in the same depth level. The highly innovative technology and the

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