The aim of this investigation is to study the changes occurring on the surface of poplar wood exposed to artificial irradiation in a Solar Box. Colour changes were monitored with a reflectance spectrophotometer. Surface chemical modifications were evaluated by measuring the infrared spectra. Hyperspectral imaging was also applied to study the surface wood changes in the visible-near infrared and the short wave infrared wavelength ranges. The data obtained from the different techniques were compared to find the possible correlations in order to evaluate the applicability of the Hyperspectral imaging to investigate wood modifications in a non-invasive modality. The study of colour changes showed an important variation due to photo-irradiation which is the greatest change occurring within the first 24 hours. Infrared spectroscopy revealed that lignin degrades mainly in the first 48 hours. Concerning Hyperspectral imaging, the spectral features in the visible-near infrared range are mainly linked to the spectral shape, whereas in the short wave infrared cellulose and lignin affect shape and reflectance levels. The proposed approach showed that a correlation can be established between colour variation and wood degradation in the visible-near infrared range; furthermore in the short wave infrared region surface chemical changes can be assessed.

Asbestos-Containing Materials (ACMs) are hazardous and prohibited to be sold or used as recycled materials. In the past, asbestos was widely used, together with cement, to produce “asbestos cement-based” products. During the recycling process of Construction and Demolition waste (C&DW), ACM must be collected and deposited separately from other wastes. One of the main aims of the recycling strategies applied to C&DW was thus to identify and separate ACM from C&DW (e.g., concrete and brick). However, to obtain a correct recovery of C&DW materials, control methodologies are necessary to evaluate the quality and the presence of harmful materials, such as ACM. HyperSpectral Imaging (HSI)-based sensing devices allow performing the full detection of materials constituting demolition waste. ACMs are, in fact, characterized by a spectral response that nakes them is different from the “simple” matrix of the material/s not embedding asbestos. The described HSI quality control approach is based on the utilization of a platform working in the short-wave infrared range (1000–2500 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: Principal Component Analysis for data exploration and hierarchical Partial Least-Square-Discriminant Analysis (PLS-DA) to build classification models. Following this approach, it was possible to set up a repeatable, reliable and efficient technique able to detect ACM presence inside a C&DW flow stream. Results showed that it is possible to discriminate and identify ACM inside C&DW. The recognition is potentially automatic, non-destructive and does not need any contact with the investigated products.