Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling

Environmental pollution from plastic debris is raising concerns not only for the vulnerability of marine species to ingestion but also for potential human health hazards posed by small particles, known as microplastics. In this context, marine areas suffer from a lack of constant shoreline cleanups to remove accumulated debris, preventing their degradation and fragmentation. To establish optimal strategies for streamlining plastic recovery and recycling operations, it is important to have a system for recognizing plastic debris on the beach and, more specifically, for identifying the type of polymer and mapping (e.g., topologically assessing) the distribution of plastic debris on shoreline sands. This study aims to provide an operative tool finalized to perform an in situ detection, analysis, and characterization of plastic debris present in the coastal environment (i.e., beaches), adopting a near-infrared (NIR)-based hyperspectral imaging (HSI) approach. In more detail, the possibility of identifying and classifying polymers of plastic debris by NIR-HSI in three different areas along the Pontine coastline of the Lazio region (Latina, Italy) was investigated. The study focused on three distinct beaches (i.e., Foce Verde, Capo Portiere, and Sabaudia), each characterized by a different type of sand. For each location, the adopted approach allowed for the systematic classification of the various types of plastic waste found. Three Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed using a cascade detection strategy. The first model was designed to distinguish plastics from other materials in sand samples, the second to detect plastic particles in the sand, and the third to classify the type of polymer composing each identified plastic particle. Obtained results showed that, on the one hand, plastics were correctly detected from sand and other materials (i.e., sensitivity = 0.892–1.000 and specificity = 0.909–0.996), and on the other, the recognition of polymer type was satisfactory, according to the performance statistical parameters (i.e., sensitivity = 1.000 and specificity = 0.991–1.000). This research highlights the potential of the NIR-HSI approach as a reliable, non-invasive method for plastic debris monitoring and polymer classification. Its scalability and adaptability suggest possible future integration into mobile systems, enabling large-scale monitoring and efficient debris management.