Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches

The accumulation of microplastics (MPs) in different environmental compartments represents a real emergency with dangerous effects on all ecosystems and human health. MPs analysis by the commonly adopted methods (i.e. FT-IR or Raman spectroscopy) is time-consuming, limiting the ability to monitor and mitigate plastic pollution. In this context, hyperspectral imaging (HSI) can be considered a promising identification tool, allowing the possibility to obtain rapid classification maps of MPs in different environmental matrices. In this work, an innovative application of HSI technology in the short-wave infrared range (SWIR: 1000-2500 nm) for rapid recognition and classification of MPs in real beach sand samples, coupled with machine learning approaches, is presented and discussed. MP samples were collected during a sampling campaign at Torre Guaceto beach (southern Italy), located along the Adriatic flank of the Apulia region, belonging to a natural protected area. Different spectral preprocessing strategies were tested on the acquired hyperspectral images in order to build a classification model capable of recognizing the complex mixture of materials that constitute MPs and beach sand matrices. The results of the study demonstrated as the proposed approach represents a powerful, fast and effective alternative to the most common adopted analytical methods for MP classification.