A point FTIR spectrometer (ALPHA, Bruker) operating in standoff reflectance mode was used to collect reflectance spectra from ~5500 to 400 cm−1 (1818 to 25,000 nm), thus providing overlap with the spectra from the UV to NIR spectrometer. The measured spatial response function of the spectrometer in this mode is 1.5 mm2, and the area illuminated is 4 by 5 mm. The spectral sampling was 4 cm−1, which matches the NIR portion of the other spectrometer. The effective integration time required to obtain good-quality spectra was the time to collect three spectra, about 5 s. Given this long collection time, the scanning easel was operated in a stop and stare mode, that is, it moved to a predetermined position using the easel linear encoders, and the spectrum was collected before moving to the next position.

Using the positional information from the easel, the reflectance spectra collected from each spectrometer were assembled into an image cube. The UV-NIR image cube had 2151 spectral bands, and the mid-IR cube had 3468 bands. Given that the solid angles are different between the two spectrometers, the mid-IR reflectance spectra were scaled to match the spectral offset of the corresponding UV-NIR reflectance spectra using the common NIR region (5500 to 4000 cm−1 or 1818 to 2500 nm) shared by the two spectrometers. The two cubes were then concatenated to form a single image cube for further processing given that they were acquired with the same spatial sampling as noted above.

The exploitation of the resulting single image cube was done using the spectral analysis tools contained in the ENVI (Harris Corp.) remote sensing software package. Spectral endmembers, reflectance spectra representing specific materials, were identified by manually looking for known spectral features of the chemical compounds of interest in the image cube. The SAM or the mixture tuned matched filter algorithms were used to make maps of the materials of interest. To enhance the quality of the maps, only portions of the reflectance spectral endmembers with characteristic spectral features were used in the image processing. An internal spectral library and published spectral information about the artists’ materials of interest were used in the spectral assignments.

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