When organisms consume microplastic particles and the toxic contaminants they might carry (such as additives and persistent organic pollutants), they can experience physical harm (like blockages, abrasions, and inflammation) and negative physiological impacts (such as reduced food intake, weight loss, slower growth rates, lower reproductive success, and energy depletion) [20]. Plastics are widespread in marine systems mainly due to their long-lasting nature and high buoyancy, which leads to slow degradation and enables long-distance transport [21]. The mobility of the microplastics in the marine environment is strongly influenced by intrinsic parameters such as morphological, morphometrical and textural attributes and, obviously, by the polymeric composition. Plastic density is one of the key factors affecting microplastic behavior in the ocean. Density depends not only on the polymer chain but also on the additives usually utilized to enhance specific characteristics of the polymer-based manufactured product [22; 23; 24]. Polymer structure and additives control the degree of degradation in nature, dependent on temperature or solar exposure time. The development of efficient strategies to mitigate microplastic pollution requires fast and reliable tools to characterize microplastics and understand their behavior. A major obstacle to reach this goal is related to the analytical effort needed to characterize the composition, the morphological and the morphometric attributes of microplastics as well as their level of degradation. Polymer identification is both time-consuming and expensive, being carried out by trained operators using classical analytical techniques, such as FTIR [25; 26; 27; 28] and Raman spectroscopy [29; 30; 31; 32; 33], at the laboratory scale. More in detail, these techniques demand a high level of expertise and often require extensive sample preparation and analysis periods. As a result, the dependence on specialized equipment and trained staff can make polymer identification both expensive and prohibitive, creating obstacles for effective and widespread analysis across different applications. Therefore, although some improvements to characterize and quantify microplastics in environmental matrices have been recently made, there are no standardized systems or official methods to do that [34]. In this scenario, hyperspectral imaging (HSI) emerges as a valuable alternative, offering detailed information and the ability to analyze multiple samples simultaneously. HSI offers significant advantages over traditional methods such as Attenuated Total ReflectanceFourier Transform Infrared (ATR-FTIR) and Raman spectroscopy for the analysis of microplastics [34]. With the possibility to scan and identify multiple particles simultaneously, it drastically reduces the time required for analysis. For instance, while ATR-FTIR processes one particle at a time, taking less than one minute per analysis, HSI can acquire hundreds of thousands of spectra in just a few minutes over a large scanning area. Moreover, HSI operates in a non-destructive manner, allowing for the analysis of a wide range of sample sizes without the need for extensive sample preparation. HSI is a more user-friendly option for high-throughput microplastic identification, particularly for particles larger than 500 \(\upmu\)m. Additionally, HSI minimizes the complications associated with subsampling, as it can quickly analyze multiple particles within the same field of view. This efficiency is crucial for large-scale sampling and characterization of microplastics, making HSI a superior choice in terms of analysis throughput, cost-effectiveness, and feasibility compared to conventional spectroscopic techniques. Each pixel in a hyperspectral image is a high-dimensional vector composed of hundreds of contiguous narrow-band spectral channels, typically containing reflectance information [35]. Nowadays, HSI technologies can provide high spectral resolution and enough spatial resolution.