HSI is a type of multivariate imaging. A typical multivariate image is an image of rows and columns measured for variables. The variables can be wavelengths.4 Therefore, data collected through a hyperspectral sensor generate a three-dimensional (3-D) dataset, the “hypercube,” characterized by two spatial dimensions and one spectral dimension. The wavelength bands in hyperspectral images are typically in an equally spaced sequence; as a consequence, a full spectrum is obtained for each pixel. Each pixel in a hyperspectral image can be analyzed when the system is given spectral information about samples. According to the different investigated wavelengths and the spectral sensitivity of the device, several physical–chemical features, linked to their spectral attributes, can be collected. The HSI approach can, thus represent a powerful solution for characterization, classification, and quality control of different materials in several applications fields. For these reasons, NIR-HSI has rapidly emerged and has quickly grown in recent years, including in the solid waste sectors: glass recycling,5 automotive shredder residue characterization (i.e., fluff),6 bottom ashes resulting from municipal solid waste incinerators,7 compost products quality control,8,9 different polymers identification,10–13 construction and DW recycling,14,15 and so on.