Integrated micro X-ray fluorescence and chemometric analysis for printed circuit boards recycling

A novel approach, based on micro X-ray fluorescence (μXRF), was developed to define an efficient and fast automatic recognition procedure finalized to detect and topologically assess the presence of the different elements in waste electrical and electronic equipment (WEEE). More specifically, selected end-of-life (EOL) iPhone printed circuit boards (PCB) were investigated, whose technological improvement during time, can dramatically influence the recycling strategies (i.e. presence of different electronic components, in terms of size, shape, disposition and related elemental content). The implemented μXRF-based techniques allow to preliminary set up simple and fast quality control strategies based on the full recognition and characterization of precious and rare earth elements as detected inside the electronic boards. Furthermore, the proposed approach allows to identify the presence and the physical-chemical attributes of the other materials (i.e. mainly polymers), influencing the further physical-mechanical processing steps addressed to realize a pre-concentration of the valuable elements inside the PCB milled fractions, before the final chemical recovery

Hyperspectral imaging applied to end-of-life (EOL) concrete recycling

The recovery of materials from DW is an important target of the recycling industry and it is important to know which materials are presents in order to set up efficient sorting and/or quality control actions. The implementation of an automatic recognition system of recovered products from End-Of-Life (EOL) concrete materials can be an useful way to maximize DW conversion into secondary raw materials. In this paper a new approach, based on HyperSpectral Imaging (HSI) sensors, is investigated in order to develop suitable and low cost strategies finalized to the preliminary detection and characterization of materials constituting Demolition Waste (DW) flow stream. The described HSI quality control approach is based on the utilization of a device working in the near infrared range (1000-1700 nm). Acquired hyperspectral images were analyzed. Different chemometric methods were applied. Results showed that it is possible to recognize DW materials and to distinguish the recycled aggregates from the investigated contaminants (brick, gypsum, plastic, wood and foam)

hyperspectral imaging applied to demolition waste recycling innovative approach for product quality control

Hyperspectral imaging (HSI) based sensing devices were utilized to develop nondestructive, rapid, and low cost analytical strategies finalized to detect and characterize materials constituting demolition waste. In detail, HSI was applied for quality control of high-grade recycled aggregates obtained from end-of-life concrete. The described HSI quality control approach is based on the utilization of a platform working in the near-infrared range (1000–1700 nm). The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis for data exploration and partial least-square-discriminant analysis to build classification models. Results showed that it is possible to recognize the recycled aggregates from different contaminants (e.g., brick, gypsum, plastic, wood, foam, and so on), allowing the quality control of the recycled flow stream

Near InfraRed-based hyperspectral imaging approach for secondary raw materials processing in solid waste sector

In secondary raw materials and industrial recycling sectors there is the need of solving quality control issues. The development and deployment of an effective, fast and robust sensing architecture able to detect, characterize and sort solid waste products is of primary importance. Near InfraRed (NIR) based HyperSpectral Imaging (HSI) techniques to detect materials to recycle and/or solid waste products to process represents an interesting solution to address quality control issues in these sectors. In this paper, are presented two different case studies on the utilization of NIR-HSI to detect contaminants in household plastic packaging waste and recognize materials occurring in processed monitors and flat screen waste. The proposed approach consists of a cascade detection based on Partial Least Squares – Discriminant Analysis (PLS-DA) classifiers applied on hyprspectral images acquired in NIR range (1000-1700 nm)

Hyperspectral imaging logics: efficient strategies for agri-food products quality control

The increasingly normative severity and market competitiveness have led the agriculture sector and the food industry to constantly look for logic improvements that can be applied in processes monitoring systems. In a context where fast, non-destructive and reliable techniques are required, image analysis-based methods have gained interest, thanks to their ability to spatially characterize heterogeneous samples. In such a scenario, HyperSpectral Imaging (HSI) is an emerging technique that provides not only spatial information of imaging systems, but even spectral information of spectroscopy. The utilization of the HSI approach opens new interesting scenario to quality control logics in agricultural and food processing/manufacturing sectors. Three different case studies are presented in this paper. In particular, the utilization of an HSI system, working in SWIR range, was applied for: i) detecting contaminants in dried fruits to be packaged, ii) identifying olive fruits attacked by olive fruit flies and iii) recognizing flour type. In particular, the proposed approach is based on the application of Partial Least Squares – Discriminant Analysis (PLS-DA) classification method to HyperSpectral images in Short Wave InfraRed (SWIR) range (1000-2500 nm). The proposed case studies demonstrate that this logic can be successfully utilized as a quality control system on agri-food products coming from different manufacturing stages, but it can even be seen as an analytical core for sorting engines

Il riciclo meccanico dei rifiuti di apparecchiature elettriche ed elettroniche. Una sfida tecnologica

Due to the technological progress, electronics became more and more part of our lifestyle. Alongside the continuous progress, the volume of electric and electronic waste (WEEE) steady arising. The WEEE, because of a large number of hazardous substances in various equipment, such as lead in printed circuit boards and cadmium in semiconductor chips, could cause serious environmental problems if not properly handled at the end of their life cycle (i.e. recycling and/or disposed-off). However, a significant amount of valuable materials is contained in WEEE, such as metals, high-quality plastic and other materials/elements that can be profitably recovered. WEEE recycling is considered a real opportunity for contrasting an inbound threat for the Industry and for the Environment. For these reasons it is thus essential to improve the WEEE recycling process, both from an economic and an environmental point of view. These two goals can be reached adopting new, and up-to-date, processing/recycling strategies based on innovative technologies allowing to implement more environmentally friendly and economically sustainable processing

HyperSpectral Imaging based approach for monitoring of micro-plastics from marine environment

The possibility to develop a sensor based procedure in order to monitor plastic presence in the marine environment was explored in this work. More in detail, this study was addressed to detect and to recognize different types of microplastics coming from sampling in different sea areas adopting a new approach, based on HyperSpectral Imaging (HSI) sensors. Moreover, a morphological and morphometrical particle characterization was carried by digital image processing. Morphological and morphometrical parameters, combined with hyperspectral imaging information, give a full characterization of each investigated particle, concurring to explain all the transportation, alteration and degradation phenomena suffered by each different polymer particle. Obtained results can represent an important starting point to develop, implement and set up monitor strategies to characterize marine microplastics. Moreover, the procedure developed in this work is fast, not expensive and reliable, making its utilization very profitable

Hyperspectral imaging applied to WEEE plastic recycling. A methodological approach

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

A dataset of Visible – Short Wave InfraRed reflectance spectra collected in–vivo on the dorsal and ventral aspect of arms

Advancement of technology and device miniaturization have made near infrared spectroscopy (NIRS) techniques cost–effective, small–sized, simple, and ready to use. We applied NIRS to analyze healthy human muscles in vivo, and we found that this technique produces reliable and reproducible spectral “fingerprints” of individual muscles, that can be successfully discriminated by chemometric predictive models. The dataset presented in this descriptor contains the reflectance spectra acquired in vivo from the ventral and dorsal aspects of the arm using an ASD FieldSpec® 4 Standard–Res field portable spectroradiometer (350–2500 nm), the values of the anthropometric variables measured in each subject, and the codes to assist access to the spectral data. The dataset can be used as a reference set of spectral signatures of “biceps” and “triceps” and for the development of automated methods of muscle detection

Taking advantage of hyperspectral imaging classification of urinary stones against conventional infrared spectroscopy

The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories