Digital holographic approaches to the detection and characterization of microplastics in water environments

Marika Valentino; Marika Valentino; Daniele Gaetano Sirico; Pasquale Memmolo; Lisa Miccio; Lisa Miccio; Vittorio Bianco; Pietro Ferraro

doi:10.1364/AO.478700

Applied Optics
Vol. 62,
Issue 10,
pp. D104-D118
(2023)
•https://doi.org/10.1364/AO.478700

Digital holographic approaches to the detection and characterization of microplastics in water environments

Marika Valentino, Daniele Gaetano Sirico, Pasquale Memmolo, Lisa Miccio, Vittorio Bianco, and Pietro Ferraro

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Marika Valentino, Daniele Gaetano Sirico, Pasquale Memmolo, Lisa Miccio, Vittorio Bianco, and Pietro Ferraro, "Digital holographic approaches to the detection and characterization of microplastics in water environments," Appl. Opt. 62, D104-D118 (2023)

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- Holography
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About this Article
History
- Original Manuscript: October 20, 2022
- Revised Manuscript: January 5, 2023
- Manuscript Accepted: January 9, 2023
- Published: February 7, 2023
Virtual Issues
Applied Optics Joint Feature Issue in Applied Optics and Journal of the Optical Society of America A: Digital Holography and 3D Imaging 2022 (2023)

JOSA A Joint Feature Issue in Applied Optics and Journal of the Optical Society of America A: Digital Holography and 3D Imaging 2022 (2023)

Abstract

Microplastic (MP) pollution is seriously threatening the environmental health of the world, which has accelerated the development of new identification and characterization methods. Digital holography (DH) is one of the emerging tools to detect MPs in a high-throughput flow. Here, we review advances in MP screening by DH. We examine the problem from both the hardware and software viewpoints. Automatic analysis based on smart DH processing is reported by highlighting the role played by artificial intelligence for classification and regression tasks. In this framework, the continuous development and availability in recent years of field-portable holographic flow cytometers for water monitoring also is discussed.

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Method	MP Type	MP Size	Advantages	Disadvantages
Optical microscopy/Stereomicroscopy [21,26,27]	PE, PP, PA, PET, PS, PMMA, PVC	0.1–1 mm	Easy and immediate. Fast recognition of morphological information.	Limited to morphological information. Transparent and small objects are not easily detected. No polymer specificity. AI needed for identification of MPs. AI training can be cumbersome.
SEM [29]	PE, PET, PS, PP, PS, PVC	$< 0.1 m m$	High resolution images. Morphological information at nm scale.	Expensive in costs and times. No polymer specificity. Low throughput and small FoV.
Polarization light optical microscopy (PLOM) [30]	PE, PET, PP	70–600 µm	Anisotropic optical properties. Birefringence.	No composition data. Limitation in nonbirefringent polymers.
Fourier transform infrared spectroscopy/µFTIR [31–34]	PE, PP, PA, PET, PS, PMMA, PVC	1 nm–20 µm	Detection of chemical composition and identification of very small objects. Polymer specific.	Expensive in costs and times. Only dried and regular samples to avoid scattering.
Raman spectroscopy/µRaman [35–39]	PE, PP, PA, PET, PS, PMMA, PU	1 nm–500 µm	Identification of very small particles. Nondestructive. Chemical information. Also samples in water environment.	Expensive in costs and times. Limited to high concentration of samples. Influenced by the surrounding interference.
Differential scanning calorimetry/thermal gravimetric analysis [40–42]	PE, PP, PVC, PA, PES, PET, PU, PE, PS	$< 1 m m$	Polymer thermal stability. Polymer information through temperature variation and weight loss as function of temperature. Detection of particles in low concentration samples.	Destructive analysis (plastic melt). Laborious sample preparation. Reference polymer requirement. Poor reproducibility.
Fluorescence microscopy [43,44]	PVC, PS, PET, PP.	$< 20 µ m$	Easy identification of transparent polymers. Low-cost and fast process.	Influenced by the interference of chemical additives. Laborious sample preparation.
Digital holography/holographic flow cytometry [45–50]	PP, PET, PVC, PE, PS, PA.	200 nm–1 mm	Detection of small and transparent objects. 3D tracking, high-throughput. Morphological and physical properties by complex amplitude reconstruction. Low-cost, portable systems available.	No polymer specificity. Diffraction limited. AI needed for identification of MPs. AI training can be cumbersome.

Daniele Gaetano Sirico	https://orcid.org/0000-0002-2760-9209
Pasquale Memmolo	https://orcid.org/0000-0002-9607-7728
Vittorio Bianco	https://orcid.org/0000-0003-1956-4976
Pietro Ferraro	https://orcid.org/0000-0002-0158-3856

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Applied Optics