A review of hyperspectral imaging-based plastic waste detection state-of-the-arts

Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques are found to be effective in detecting plastic waste and microplastics as they were able to capture plastic reflectance spectral by using the near-infrared sensor. However, the review also shows that hyperspectral imaging techniques were less efficient in capturing the electromagnetic spectrum of black plastics due to carbon-black absorption properties. Carbon-black strongly absorbs light in the ultraviolet and infrared spectral range of the electromagnetic spectrum, therefore not detected by the near-infrared sensor. This paper also reviews how machine learning can alternatively detect and sort all types of waste, including plastics. Multiple studies show that the machine learning model achieved good accuracy in detecting all types of plastics based on the waste dataset. Finally, it can be seen that the spectral information of plastic can be used as feature extraction for machine learning models for better plastic detection. It is hoped that this study will contribute to more systematic research on the same topic

Critical review of real-time methods for solid waste characterisation: Informing material recovery and fuel production

Waste management processes generally represent a significant loss of material, energy and economic resources, so legislation and financial incentives are being implemented to improve the recovery of these valuable resources whilst reducing contamination levels. Material recovery and waste derived fuels are potentially valuable options being pursued by industry, using mechanical and biological processes incorporating sensor and sorting technologies developed and optimised for recycling plants. In its current state, waste management presents similarities to other industries that could improve their efficiencies using process analytical technology tools. Existing sensor technologies could be used to measure critical waste characteristics, providing data required by existing legislation, potentially aiding waste treatment processes and assisting stakeholders in decision making. Optical technologies offer the most flexible solution to gather real-time information applicable to each of the waste mechanical and biological treatment processes used by industry. In particular, combinations of optical sensors in the visible and the near-infrared range from 800 nm to 2500 nm of the spectrum, and different mathematical techniques, are able to provide material information and fuel properties with typical performance levels between 80% and 90%. These sensors not only could be used to aid waste processes, but to provide most waste quality indicators required by existing legislation, whilst offering better tools to the stakeholders

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 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

Increasing the reuse of wood in bulky waste using artificial intelligence and imaging in the VIS, IR, and terahertz ranges

Bulky waste contains valuable raw materials, especially wood, which accounts for around 50% of the volume. Sorting is very time-consuming in view of the volume and variety of bulky waste and is often still done manually. Therefore, only about half of the available wood is used as a material, while the rest is burned with unsorted waste. In order to improve the material recycling of wood from bulky waste, the project ASKIVIT aims to develop a solution for the automated sorting of bulky waste. For that, a multi-sensor approach is proposed including: (i) Conventional imaging in the visible spectral range; (ii) Near-infrared hyperspectral imaging; (iii) Active heat flow thermography; (iv) Terahertz imaging. This paper presents a demonstrator used to obtain images with the aforementioned sensors. Differences between the imaging systems are discussed and promising results on common problems like painted materials or black plastic are presented. Besides that, pre-examinations show the importance of near-infrared hyperspectral imaging for the characterization of bulky waste

A Review of Sorting and Separating Technologies Suitable for Compostable and Biodegradable Plastic Packaging

As a result of public pressure and government legislation to reduce plastic waste there has been a sharp rise in the manufacture and use of alternatives to conventional plastics including compostable and biodegradable plastics. If these plastics are not collected separately, they can contaminate plastic recycling, organic waste streams, and the environment. To deal with this contamination requires effective identification and sorting of these different polymer types to ensure they are separated and composted at end of life. This review provides the comprehensive overview of the identification and sorting technologies that can be applied to sort compostable and biodegradable plastics including gravity-based sorting, flotation sorting, triboelectrostatic sorting, image-based sorting, spectral based sorting, hyperspectral imaging and tracer-based sorting. The advantages and limitations of each sorting approach are discussed within a circular economy framework

Hierarchical modelling for recycling-oriented classification of shredded spent flat monitor products based on hyperspectral imaging

The number of flat monitors from televisions, notebooks and tablets has increased dramatically in recent years, thus resulting in a corresponding rise in Waste from Electrical and Electronic Equipment (WEEE). This fact is linked to the production of new high-performance electronic devices. Taking into account a future volume growth trend of WEEE, the implementation of adequate recycling architectures embedding recognition/classification logics to handle the collected WEEE physical-chemical at-tributes, is thus necessary. These integrated hardware and software architectures should be efficient, reliable, low cost, and capable of performing detection/control actions to assess: i) WEEE composition and ii) physical-chemical attributes of the resulting recovered flow streams. This information is fundamental in setting up and implementing appropriate recycling actions. In this study, a hierarchical classification modelling approach, based on Near InfraRed (NIR)-Hyperspectral Imaging (HSI), was carried out. More in detail, a 3-step hierarchical modelling procedure was designed, implemented and set up in order to recognize different materials present in a specific WEEE stream: End-of-Life (EoL) shredded monitors and flat screens. By adopting the proposed approach, different categories are correctly recognized. The results obtained showed how the proposed approach not only allows the set up of a “one shot” quality control system, but also contributes towards improving the sorting process

Systematic reduction of Hyperspectral Images for high-throughput Plastic Characterization

Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting