Recent advances in materials and fabrication techniques provided portable, performant, sensing optical spectrometers readily operated by user-friendly cabled or wireless systems. Such systems allow rapid, non-invasive, and not destructive quantitative analysis of human tissues. This proof-of-principle investigation tested whether infrared spectroscopy techniques, currently utilized in a variety of areas, could be applied in living humans to categorize muscles. Using an ASD FieldSpec® 4 Standard-Res Spectroradiometer with a spectral sampling capability of 1.4 nm at 350–1000 nm and 1.1 nm at 1001–2500 nm, we acquired reflectance spectra in visible short-wave infra-red regions (350–2500 nm) from the upper limb muscles (flexors and extensors) of 20 healthy subjects (age 25–89 years, 9 women). Spectra off-line analysis included preliminary preprocessing, Principal Component Analysis, and Partial Least-Squares Discriminant Analysis. Near-infrared (NIR) spectroscopy proved valuable for noninvasive assessment of tissue optical properties in vivo. In addition to the non-invasive detection of tissue oxygenation, NIR spectroscopy provided the spectral signatures (ie, “fingerprints”) of upper limb flexors and extensors, which represent specific, accurate, and reproducible measures of the overall biological status of these muscles. Thus, non-invasive NIR spectroscopy enables more thorough evaluation of the muscular system and optimal monitoring of the effectiveness of therapeutic or rehabilitative interventions.

In this work, the application of Short-Wave Infrared (SWIR: 1000–2500 nm) spectroscopy was evaluated to identify plastic waste containing brominated flame retardants (BFRs) using two different technologies: a portable spectroradiometer, providing spectra of single spots, and a hyperspectral imaging (HSI) platform, acquiring spectral images. X-ray Fluorescence (XRF) analysis was preliminarily performed on plastic scraps to analyze their bromine content. Chemometric methods were then applied to identify brominated plastics and polymer types. Principal Component Analysis (PCA) was carried out to explore collected data and define the best preprocessing strategies, followed by Partial Least Squares—Discriminant Analysis (PLS-DA), used as a classification method. Plastic fragments were classified into “High Br content” (Br > 2000 mg/kg) and “Low Br content” (Br < 2000 mg/kg). The identified polymers were acrylonitrile butadiene styrene (ABS) and polystyrene (PS). Correct recognition of 89–90%, independently from the applied technique, was achieved for brominated plastics, whereas a correct recognition ranging from 81 to 89% for polymer type was reached. The study demonstrated as a systematic utilization of both the approaches at the industrial level and/or at laboratory scale for quality control can be envisaged especially considering their ease of use and the short detection response.

In this study, the possibility of applying the hyperspectral imaging (HSI) technique in the Short-Wave InfraRed (SWIR) spectral range to characterize polymeric parts coming from Waste from Electric and Electronic Equipment (WEEE) is explored. Different case studies are presented referring to the identification of (i) plastic flakes inside a mixed waste stream coming from a recycling plant of monitors and flat screens, (ii) different polymers inside a mixed plastic waste stream coming from End-Of-Life (EOL) electronic device housings and trims, (iii) contaminants (i.e., metals) in a mix of shredded plastic particles coming from a recycling line of electrical cables, and (iv) brominated plastics in mixed streams constituted by small appliances (i.e., cathode-ray tube televisions and monitors). The application of chemometric techniques to hyperspectral data demonstrated the potentiality of this approach for systematic utilization for material characterization, quality control and sorting purposes. The experimental findings highlight the feasibility of employing this method due to its user-friendly nature and quick detection response. To increase and optimize WEEE valorization avoiding disposal in landfills or incineration, recycling-oriented characterization and/or quality control of the processed products are fundamental to identify and quantify substances to be recovered.

The life cycle of textiles (i.e., fabrics and apparel products) generates many environmental impacts, such as resource consumption, water, soil, and air pollution through the dispersion of chemical substances and greenhouse gases. For these reasons, in 2019, textiles were identified as a “priority product category for the circular economy” by the European Commission that proposed a new circular economy action plan focusing on recycling. An in-depth characterization of textile fabrics could lead to an ad hoc recycling procedure, reducing resource consumption and chemicals utilization. In this work, NIR (1000–1650 nm) spectroscopy was applied to extract information regarding fabric composition, with reference to cotton, silk, viscose, and some of their blends, using two different devices: a hyperspectral imaging (HSI) platform and a portable spectroradiometer. The different fabrics were correctly classified based on their spectral features by both detection instruments. The proposed methodological approach can be applied for quality control in the textile recycling sector at industrial and/or laboratory scale thanks to the easiness of use and the speed of detection.

Reusing and recycling End-Of-Life (EoL) textiles is a successful approach to develop sustainable and circular strategies in the apparel industry. Textile reuse and recycling can help to reduce the environmental impact of the fashion and textile industry by preserving natural resources and reducing waste. Textile fibers recognition and sorting, according to material composition, are of primary importance for the implementation of efficient and sustainable recycling strategies. In this work, Short-Wave InfraRed (SWIR: 1000–2500 nm) spectroscopy was applied to extract information regarding the fabric composition of different EoL textiles in order to set up a hierarchical classification procedure able to recognize different type of textile. In more detail, Partial Least Squares-Discriminant Analysis (PLS-DA) pattern recognition technique was used and classifications were performed in two steps: (1) recognition of the fiber origin [i.e. plant-derived, animal-derived, artificial textiles such as synthetic and/or Man-Made Cellulosic Fibers (MMCFs)] and, (2) discrimination of fabrics according to the material classes (i.e. silk, cotton, wool, viscose, linen, jute, polyester and blends). The proposed chemometric technique successfully classified textiles based on their spectral properties. The acquired results are highly promising and provide important insight into the EoL textile recycling business. These analytical techniques have the potential to be utilized to successfully automate the recycling process, either in addition to or as a replacement for manual processes, hence improving sorting procedures. Graphical Abstract