## 2 Materials and Methods
### Instruments and Statistical Analysis  
HSI was carried out in the SWIR range (1000-2500 nm), adopting the SISUChema XL (Specim(r), Oulu Finland) analytical unit equipped with 31-mm lens, allowing an acquisition resolution of 150 \(\upmu\)m/pixel (i.e., 50 mm of field of view). The acquisition set up was established as results of the previous studies [11; 20; 21; 22], where the utilization of 15 mm lens optics, enabling a maximum pixel resolution of 600 \(\upmu\)m, allowed to perform ACM identification of classification. Such a result was achieved at laboratory scale. The operative conditions being: a spatial resolution (i.e., field of view) equal to 20 cm and a scanning speed of 72.5 mm/s. Samples are scanned on a moving imaging tray, under lighting conditions, using SPECIM's diffused line illumination unit. The scanner was integrated with a computer workstation allowing to acquire and save spectral images in real-time using the ChemaDAQ data acquisition software. An internal standard reference target, issued for calibration measurement before each sample scan, is utilized thus performing an automatic image data calibration in respect of the collected reflectance spectra. Its higher spatial and spectral resolution allows the SisuCHEMAT(tm) to record distinctive spectral absorption features in the SWIR wavelength region, which can be utilized to study mineralogical components on materials surfaces [41]. The collected hyperspectral data generate a hypercube, where the x and y dimensions allow to identify the pixel coordinates (i.e., spatial dimension) and the z coordinate the spectral dimension. Spectral data (i.e., hypercube) analysis was carried out adopting chemometric, utilizing the PLS_Toolbox (version 8.6, Eigenvector Research, Inc.) running inside MATLAB (version 9.3, The Mathworks, Inc., Natick, MA, USA). A single mosaic image was created from the four acquisitions (Figure 2). Mosaic image was the result of a composite \"tiled\" image obtained performing a hypercube concatenation. Following this strategy, a bigger hypercube embedding all particles hyperspectral attributes was defined and utilized to perform all the further processing. Principal Component Analysis (PCA) [42] for exploratory data analysis, Partial Least Square-Discriminant Analysis (PLS-DA) [43; 44; 45] to build classification models and Hierarchical Modelling (HM) were applied. PCA is a useful method capable of providing an overview of complex multivariate data. Figure 1: RGB color images of the acquired Asbestos Containing Materials (ACM) and Construction and Demolition Waste (C&DW) samples (**a**) and corresponding identification maps (**b**). PCA is a useful method capable of providing an overview of complex multivariate data. PCA can be used for revealing relations between variables and relations between samples (e.g., clustering), detecting outliers, finding and quantifying patterns, generating new hypotheses as well as many other things. It was used to decompose the \"processed\" spectral data into several Principal Components (PCs) (linear combinations of the original spectral data), embedding the spectral variations of each collected spectral data set. According to this approach, a reduced set of factors is produced. Such a set can be used for discrimination, since it provides an accurate description of the entire dataset. The first few PCs are generally used to analyses the common features among samples and their grouping: in fact, samples characterized by similar spectral signatures tend to aggregate in the score plot of the first two or three components. Spectra could be thus characterized either by the reflectance at each wavelength in the wavelength space, or by their score on each PC in the PC space. Samples characterized by similar spectra, which belong to the same class of products, are grouped in the same region of the score plot related to the first two or three PCs, whereas samples characterized by different spectral features will be clustered in other parts of this space.