On the use of machine learning for microplastic identification from holographic phase-contrast signatures

Marika Valentino; Jaromir Behàl; Daniele Pirone; Pasquale Memmolo; Lisa Miccio; Vittorio Bianco; Pietro Ferraro

doi:10.1117/12.2674886

9 August 2023 On the use of machine learning for microplastic identification from holographic phase-contrast signatures

Marika Valentino, Jaromir Behàl, Daniele Pirone, Pasquale Memmolo, Lisa Miccio, Vittorio Bianco, Pietro Ferraro

Proceedings Volume 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III; 126210U (2023) https://doi.org/10.1117/12.2674886
Event: SPIE Optical Metrology, 2023, Munich, Germany

Abstract

Microplastics (MPs) are not degradable pollutant, yet curbing their consumption to safeguard marine waters is not an easy task. Plastic fragmentation makes detection and discrimination of micron sized elements a difficult task, decelerating plastic recovery policies, especially in marine waters. MPs of all types and shapes are subject to continuous research for defining standard identification protocols. Among the most recent methodologies, digital holography (DH) demonstrated to be an efficient and reliable imaging tool for MPs discrimination. DH furnishes quantitative information of samples in a label-free mode and with flexible refocusing capabilities. DH refocuses the probed object in post-processing by using numerical solutions of the diffraction integral, and can be designed to be compacted for high-throughput field portable microfluidic systems. Here we show the fruitful combination of DH and artificial intelligence to identify and characterize micro-plastics. We show how fractal geometry applied to DH phase-contrast maps plays an important role in providing a set of features that well describe MPs and micro-plankton. Classification results prove the reliability of DH fractal features. Besides, we show that adding multiple information channels to a DH system allows mimicking material specificity at a certain extent. In particular, we discriminate between different types of synthetic and natural microfibers by combining AI and a polarization-resolved DH system. We distinguish microfibers by training several classifiers using features calculated from the Jones matrix and birefringence features. Results corroborate the use of conventional machine learning applied to holographic maps to solve a variety of classification issues in water pollution assays.

Conference Presentation

Citation Download Citation

Marika Valentino, Jaromir Behàl, Daniele Pirone, Pasquale Memmolo, Lisa Miccio, Vittorio Bianco, and Pietro Ferraro "On the use of machine learning for microplastic identification from holographic phase-contrast signatures", Proc. SPIE 12621, Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, 126210U (9 August 2023); https://doi.org/10.1117/12.2674886

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available