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Related Experiment Video

Updated: Feb 8, 2026

Sampling and Identification of Microplastics in Groundwater

Sampling and Identification of Microplastics in Groundwater

Published on: November 7, 2025

Identification of microplastics using Raman spectroscopy: Latest developments and future prospects.

Catarina F Araujo¹, Mariela M Nolasco¹, Antonio M P Ribeiro¹

¹CICECO - Aveiro Institute of Materials, Departamento de Química, Universidade de Aveiro, 3810-193 Aveiro, Portugal.

|June 18, 2018

Summary

Microplastic pollution poses risks to organisms. Raman microscopy, though powerful for tiny plastic particle analysis, faces challenges. This review offers solutions for faster, more accurate microplastic identification using Raman spectroscopy.

Keywords:

Automation Fluorescent tagging Library matching Real-time analysis Small microplastics Stimulated Raman scattering

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Sampling and Identification of Microplastics in Groundwater

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Separation and Identification of Conventional Microplastics from Farmland Soils

Separation and Identification of Conventional Microplastics from Farmland Soils

Published on: March 21, 2025

Area of Science:

Environmental Science
Analytical Chemistry
Spectroscopy

Background:

Microplastic pollution is a growing environmental concern with potential detrimental effects on living organisms.
Accurate risk assessments require representative data on microplastic abundance, size distribution, and chemical composition.
Raman microscopy is crucial for analyzing microplastics smaller than 20 micrometers.

Purpose of the Study:

To address the limitations of Raman microscopy in microplastic analysis, specifically long measurement times and fluorescence-induced spectral distortion.
To present practical solutions for enhancing the speed and accuracy of microplastic identification using Raman spectroscopy.
To explore advanced Raman techniques for real-time detection and imaging of microplastics.

Main Methods:

Review of current Raman microscopy techniques and their drawbacks for microplastic analysis.
Discussion of solutions including improved detectors, spectrum processing, and automated particle selection for Raman mapping.
Introduction to non-conventional Raman methods like non-linear Raman, hyperspectral imaging, and standoff Raman.

Main Results:

Enhanced signal quality and spectrum processing can improve microplastic detection.
Automated particle selection significantly speeds up Raman mapping processes.
Comprehensive spectral reference libraries are essential for accurate microplastic identification.
Advanced Raman techniques offer potential for real-time microplastic detection and imaging.

Conclusions:

Overcoming limitations in Raman spectroscopy, such as long measurement times and spectral distortion, is key to effective microplastic analysis.
Practical solutions and advanced techniques can significantly improve the speed, accuracy, and scope of microplastic identification.
Raman microscopy, enhanced by these advancements, is vital for robust environmental risk assessments of microplastic pollution.