# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 4 Conclusions and Future Perspectives  
This study aimed to investigate whether NIR-HSI analysis could potentially be used to categorize and identify various plastic waste based on the polymer types that constitute it across different sand backgrounds. The adopted analytical procedure included non-destructive investigative tools. In more detail, X-ray fluorescence and FT-IR spectroscopy were used to perform a fast waste polymer characterization/classification. Specifically, HSI proved to be a useful technique for material identification and recognition in a plastic recycling perspective. The significant potential of this analytical approach is strengthened by continuous advancements in sensor technology, the technological development of acquisition tools, and the growing capacity of computing power. Figure 11: The average spectrum of the PET sample compared with the spectrum of the polymer detected from the reference library, along with the associated reliability table. The study explored the ability to distinguish plastics from other materials on various types of sand collected from three different locations along the Pontine coast (i.e., F once Verde, Capo Portiere, and Sabaudia) and to identify/classify the types of polymer constituents. Specifically, three classification models were developed: the first to distinguish plastic materials from other substances in various sand samples, the second to detect plastic particles from the sand, and the third to differentiate the type of polymer in each identified plastic sample. The developed procedure can be profitably adopted for monitoring plastic waste presence on beaches, and the obtained results could lay the groundwork for establishing detection and monitoring systems that, if fully implemented, can be directly utilized in situ, making the entire process of plastic waste management more efficient and contributing to sustainable recycling practices. These developments open pathways for deploying the methodology on automated mobile platforms (i.e., UGVs or UAVs) to conduct large-scale and comprehensive monitoring of plastic debris along shorelines. In this context, given the increasing number of plastic debris accumulating in the sea and along coastal areas, which contributes significantly to environmental pollution, this approach could serve as an effective tool for monitoring the volume of plastic waste deposited on beaches. The adopted methodology can enable the development of suitable procedures for the creation of customized classification systems to be applied in situ for identifying the presence of plastic waste on beaches and for recognizing the type of polymers aimed at subsequent removal efforts for speeding up the recycling processes. Furthermore, this NIR-HSI-based classification system offers the significant advantage of being potentially fully automated and objective, unlike visual inspection (or RGB imaging systems) and manual sorting, which are operator-dependent. Additionally, it addresses the issue of density overlap among plastics, which often leads to misclassification of polymer recycling processes. This makes it a robust choice for core sorting applications. While the results are promising, some limitations were identified. Environmental complexity, such as the presence of more diverse mixtures of pollutants or organic-rich substrates, was not fully addressed in this study and may require further refinement of the methodology. Additionally, although three sand types were tested, broader validation across different shoreline conditions, such as rocky or mud-dominated coasts, is needed to ensure the generalizability of the approach. Furthermore, distinguishing highly degraded or weathered microplastics remains a challenge, which may affect polymer classification accuracy. Future research should focus on addressing these limitations by expanding the testing environments and exploring ways to enhance the classification of degraded plastic particles.