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

Angela Rizzo; Silvia Serranti; Paola Cucuzza; Stefania Lisco; Antonella Marsico; Giuseppe Bonifazi; Giuseppe Mastronuzzi

doi:10.1117/12.3013227

7 June 2024 Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches

Angela Rizzo, Silvia Serranti, Paola Cucuzza, Stefania Lisco, Antonella Marsico, Giuseppe Bonifazi, Giuseppe Mastronuzzi

Author Affiliations +

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 130310Q (2024) https://doi.org/10.1117/12.3013227
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Angela Rizzo, Silvia Serranti, Paola Cucuzza, Stefania Lisco, Antonella Marsico, Giuseppe Bonifazi, and Giuseppe Mastronuzzi "Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 130310Q (7 June 2024); https://doi.org/10.1117/12.3013227

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