A methodological approach to study the stability of selected watercolours for painting reintegration, through reflectance spectrophotometry, Fourier transform infrared spectroscopy and hyperspectral imaging

Hyperspectral Imaging as Powerful Technique for Investigating the Stability of Painting Samples

“…In some cases, pigments that are similar in colour and declared composition, locate themselves in different regions of the score plot, such as, for example, samples Br1 and Br2 (both being burnt umber + GA). This behaviour can also be observed for samples in pan and tube but with the same compositions, as also previously highlighted [60]. This is the case, for example, of samples GY1 and TY1 (yellow ochre in pan and tube), samples GBr2 and TBr2 (natural umber in pan and tube), GR2 and TR2 (Venetian red in pan and tube) and better in GC1 and TC1 (cobalt blue in pan and tube) and in GG2/TG2 (Viridian in pan and tube).…”

“…The group of green watercolours exhibits high stability. In the case of the other watercolours, the stability varies as function of colour and of watercolour typology, i.e., pan and tube, as also previously found [60]. Sienna-based watercolours have in general low stability, both in pan and tube.…”

“…The calibration dataset of the PCA-KNN classification model has been set by considering the spectra at time 0. PCA-KNN model, applied in this modality, allows for seeing the variation of paint layers for each ageing time in respect to time 0, based on the proximity of unknowns to the different groups in the training set [60,[77][78][79][80][81]. The full colour RGB (red, green and blue) images of painting samples, at the different ageing times, are shown in Figure 6.…”

“…Hyperspectral imaging data were acquired at the following ageing times: 0 h, 168 h, 336 h, and 504 h, corresponding to 0 J/m 2 , 3.3 × 10 8 J/m 2 , 6.7 × 10 8 J/m 2 , and 1.0 × 10 9 J/m 2 , respectively, of the total energy on the irradiated surfaces at the different times. Tables 1 and 2. GA and GG indicate gum Arabic and GG ground layer, respectively [60].…”

Hyperspectral Imaging and Hierarchical PLS-DA Applied to Asbestos Recognition in Construction and Demolition Waste

“…Starting from the collected spectra it was thus possible to create a classification model able to recognize, inside a C&DW flow stream, the presence of uncontaminated (i.e., absence of asbestos fibers in the matrix) and contaminated samples (i.e., presence of asbestos fibers in the matrix). Multivariate approach, HIS based, was largely adopted in many research fields [29][30][31][32] to manage the huge amount of data and to utilize the information to identify, to characterize and to sort ACM. The aim of the chemometric approach was to obtain a data dimensionality reduction for a better data spectral evaluation and to develop classification algorithm for an efficient handling of multiple classes when hyperspectral imaging [33][34][35][36][37][38][39][40][41] sorting strategies have to be set up.…”