Efficient microplastic identification by hyperspectral imaging. A comparative study of spatial resolutions, spectral ranges and classification models to define an optimal analytical protocol

Microplastics (MPs) pollution is a global and challenging issue, necessitating the development of efficient analytical strategies for their detection to monitor their environmental impact. This study aims to define an optimal analytical protocol for characterizing MPs by hyperspectral imaging (HSI), comparing different setups based on spatial resolution, spectral range and classification models. The investigated MPs include polymers commonly found in the environment, such as polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE), subdivided in three size classes (1000-2000 mu m, 500-1000 mu m, 250-500 mu m). Furthermore, MP particles with diameters ranging from 30 to 250 mu m were assessed to determine the limit of detection (LOD) in the different configurations. Hyperspectral images were acquired with two spatial resolutions, 150 and 30 mu m/pixel, and two spectral ranges, 1000-1700 nm (NIR) and 1000-2500 nm (SWIR). Three classification models, Partial Least Square-Discriminant Analysis (PLS-DA), Error Correction Output Coding-Support Vector Machine (ECOC-SVM) and Neural Network Pattern Recognition (NNPR) were tested on the acquired images. The correctness of these models was evaluated by prediction maps and statistical parameters (Recall, Specificity and Accuracy). The results demonstrated that for MP particles larger than 250 mu m, the optimal setup is a spatial resolution of 150 mu m/pixel and a spectral range of 1000-1700 nm, utilizing a linear classification model like PLS-DA. This approach offers accurate predictions while being time- and cost-efficient. For MPs smaller than 250 mu m, a higher spatial resolution of 30 mu m/pixel with a spectral range of 1000-2500 nm and a non-linear classification method like ECOC-SVM is preferable. The LOD is 250 mu m for the 150 mu m/pixel resolution and ranges from 100 to 200 mu m for the 30 mu m/pixel resolution. These findings provide a valuable guide for selecting the appropriate HSI acquisition conditions and data processing methods to optimally characterize MPs of different sizes.