# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 1 Introduction
In recent years, rigorous monitoring of plastic debris on shoreline sands has been carried out to achieve two primary objectives: (i) to gain a comprehensive understanding of marine debris distribution along shorelines and to establish standardized data collection procedures for the effective monitoring and management of shoreline pollution [17], and (ii) to facilitate the classification, collection, and categorization of plastic debris, offering valuable insights into how these materials are identified, mapped, and managed in local cleanup initiatives [18]. Plastic debris found on shoreline sands can be regarded as a resource, distinct from waste plastics collected through conventional separate waste collection methods typically employed in urban or industrial areas. These shoreline plastics show unique characteristics that significantly affect their handling and subsequent processing [19]. The key aspects influencing their management are reported in the following, that is:  
* The spatial distribution and location. Shoreline plastic debris is spread across extensive areas and often found superficially rather than deeply embedded, requiring significant effort for collection across broad spatial scales. This spread is compounded by topographical challenges and the varied morphology of plastic debris [19; 20]. * The presence of polluting materials. Plastic debris on shorelines is frequently intermixed with sand, seaweed, and various organic materials, negatively affecting the separation process necessary for effective recycling. This contamination disrupts recycling operations, necessitating additional cleaning steps that increase costs and reduce operational efficiency [20]. * The state and characteristics of degradation. Exposure to sunlight, saltwater, and various weather conditions can significantly degrade the quality of plastic [21]. Photodegradation caused by UV rays, combined with the abrasive action of sand, leads to plastics becoming brittle and fragmenting into smaller particles, commonly referred to as microplastics. These degraded plastics often lose the structural integrity necessary for recycling into high-quality products. When mixed with the same non-degraded recovered polymer, they result in a low-grade polymer stream, which limits their potential applications, decreasing the overall value of the recycled material. Several studies have focused on accelerating the morphological and chemical characterization of MPs using spectroscopic methods and advanced microscopy software [22]. For instance, [PERSON] et al. [23] highlighted that combining machine learning models with hyperspectral imaging (HSI) on sediments could be promising for large-area MP monitoring due to its flexibility, fast data collection, and minimal sample preparation. However, the approach is limited by challenges such as dealing with impure environmental MPs, spectral artifacts from sediments on MP surfaces, and the complexity of sand mineral compositions that may disrupt protocol effectiveness. Moreover, this protocol seems to be not suitable for microfibers (MFs) (i.e., polyamide fishing lines). On the other hand, HSI's innovative approaches can address the recycling challenges posed by shoreline plastics and require innovative approaches. One promising method is the use of hyperspectral imaging (HSI), which enables in situ mapping (i.e., topological assessment) of plastic debris. HSI-based mapping of plastic debris can provide detailed information on the composition, quantity, and quality of plastic fragments, as well as the level of contamination, allowing for optimized collection, storage, and exploitation strategies [15; 24]. HSI technology captures high-resolution spectral data for each pixel in an image, which facilitates the identification and classification of materials based on their unique spectral signatures. The HSI approach provides detailed chemo-physical information regarding samples.