Methods for Microplastics Detection
Explore the technologies currently being used and developed to detect microplastics.
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Microplastics (MPs) and nanoplastics (NPs) – plastic fragments smaller than 5 mm and less than 100 nm, respectively – have invaded nearly every corner of our planet.
Once believed to be confined to industrial waste, these plastics are now found in all environments, from deep-sea trenches to the peaks of mountains, and even within our bodies.1,2,3,4 Over the last few decades, the proliferation of MPs has become a major global environmental issue, with serious implications for the health of ecosystems and organisms.
Recent scientific studies have revealed the presence of these fragments in food, water and air.5,6,7 The strong impact of environmental plastic pollution on the development, growth and survival of species has prompted the scientific community to develop new methods for monitoring and mitigating this pollution. But what types of plastics are we dealing with, where are they coming from and how are they entering food and water supplies? Answering these questions requires robust detection methods that can accurately identify, quantify and trace the sources of MPs and NPs.
What are microplastics?
MPs are generally categorized into two types based on their origin:8
- Primary MPs: These are intentionally manufactured small particles, such as microbeads in cosmetics or industrial abrasives.
- Secondary MPs: These result from the degradation of larger plastic items, like bottles, bags, or fishing nets, into smaller pieces due to environmental factors such as UV radiation and mechanical abrasion.
Beyond these classifications, MPs can vary significantly in terms of physical properties like shape, size, chemical additives and aging states (Figure 1). The most common polymers found in MPs are polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), nylon (PA) and polyester (PET).
All of these properties affect the MPs’ level of toxicity and, therefore, their impact on species and ecosystems. Smaller particles, such as NPs, can penetrate biological membranes, potentially leading to harmful bioaccumulation and tissue damage.9 Similarly, the specific polymers and chemical additives, which vary widely across different types of MPs, influence their environmental persistence, degradation rates and ability to attract other pollutants, such as heavy metals or pesticides.10
Therefore, it’s crucial to employ sensitive, accurate and high-throughput methods for detecting and characterizing both MPs and NPs.
Accurate analytical systems are needed to determine physical and chemical properties of MPs, such as shape, size and polymer composition. Credit: Technology Networks.
Microscopy methods for microplastics detection
Microscopy, including optical and electron microscopy, is a standard method for identifying MPs based on their size and shape due to its simplicity and low cost.11 However, this approach is limited to MPs, is time-intensive and relies heavily on the analyst’s judgment, making it susceptible to errors influenced by environmental factors and sample impurities.
Fluorescence staining is now widely applied as a supplementary technique to enhance MP identification by microscopy. This method involves staining MPs with hydrophobic dyes and using specific wavelengths to trigger fluorescence, which aids in detection under specialized microscopes.12 However, issues like false positives due to staining of organic materials or interference from natural fluorescence in samples remain challenges. The development of new fluorescent dyes that can recognize specific MPs would greatly advance the field by improving the accuracy of microplastic identification in complex environmental samples.
Advanced methods like scanning electron microscopy (SEM) and atomic force microscopy (AFM) are increasingly being used to study the smaller dimensions of NPs.13 SEM provides high-resolution imaging of morphological features, especially surface characteristics. When paired with energy-dispersive X-ray spectroscopy, it can also provide valuable insights into chemical composition. However, SEM is slow and requires time-intensive sample preparation.14 In contrast, AFM is a promising tool for micro- and nanoscale analysis due to its ability to capture high-resolution images directly from the sample without pretreatment.15 Furthermore, AFM is fast, simple and can distinguish material types within polymer blends, detecting compounds like heavy metals adsorbed onto microplastic surfaces.
Spectroscopy techniques for microplastics detection
One of the most widely used methods for MP detection is spectroscopy, specifically Fourier-transform infrared (FTIR) and Raman spectroscopy.16 These techniques rely on the interaction between light and matter to determine the chemical composition of particles. Both methods are non-destructive, require minimal sample volumes and operate in real-time, enabling comprehensive analysis of surface properties and chemical composition.17
- FTIR spectroscopy: By analyzing how a sample absorbs infrared light, FTIR can identify the chemical bonds in plastic polymers, making it a highly effective tool for distinguishing MPs from other environmental particles. However, this technique cannot reliably detect particles smaller than 20 μm, struggles with opaque or black MPs and is affected by sample heterogeneity and environmental factors.
- Raman spectroscopy: Known for its exceptional sensitivity, Raman spectroscopy can detect plastic particles as small as 1 µm.14 It offers broad spectral coverage, heightened sensitivity to non-polar functional groups, reduced interference from water, and narrower spectral bands compared to FTIR.18 However, Raman spectroscopy is prone to fluorescence interference, has an inherently low signal-to-noise ratio and can cause sample heating, leading to background emission or polymer degradation.
Incorporating microscopes into both Raman and FTIR spectroscopy significantly enhances their ability to analyze MPs at finer scales. Micro-Raman spectroscopy, for instance, pairs a Raman spectrometer with an optical microscope, enabling spatially resolved spectral measurements and generating chemical images that reveal molecular heterogeneity at sub-micron levels.18
Similarly, coupling a benchtop FTIR spectrometer with a microscope improves the detection capabilities for MPs down to about 10 µm. This setup can be equipped with either a single-pixel (point) detector, which measures one spectrum at a time and often requires particle pre-selection, or a focal plane array (FPA) detector, which uses an array of pixels to capture individual spectra over larger areas.19 By integrating microscopy in both techniques, researchers achieve enhanced spatial resolution and accuracy in identifying MPs, making both micro-Raman and micro-FTIR spectroscopy valuable tools for detailed environmental analysis.
Mass spectrometry-based methods for microplastic detection
Pyrolysis–gas chromatography–mass spectrometry (Pyrolysis-GC-MS) is a powerful approach for identifying MPs. By heating the sample, this method breaks down complex polymers into simpler compounds, which are then analyzed by GC and MS to pinpoint their chemical makeup. This approach provides detailed data on the types of polymers present and can also differentiate various plastic additives, allowing simultaneous identification of primary polymers and organic additives in MPs. Moreover, it requires only a small sample size, has no strict limitations on microplastic size and is fully automatable.20
However, pyrolysis-GC-MS is relatively costly, limited to certain polymers and involves destructive sampling, limiting its practicality for large-scale environmental studies. Additionally, while this method offers chemical insights, it does not reveal physical characteristics due to the destructive nature of the analysis.21
Machine learning and automation’s rise in microplastic detection studies
To overcome the challenges of time and labor-intensive MP detection techniques, researchers are increasingly adopting machine learning and automation. These technologies can process large datasets with greater speed and accuracy, training algorithms to detect MPs more efficiently. Machine learning models can analyze spectral data or images, identifying patterns that distinguish MPs from other particles.
Several studies have now proven the potential of machine learning for MPs identification using SEM, fluorescence, Ramen spectroscopy and FTIR.22,23,24,25,26,27,28 One study introduced PlasticNet, a deep learning model specifically trained to recognize MPs from images generated by FPA-based micro-FTIR spectroscopy.24 PlasticNet, trained on spectra from 11 types of virgin plastic particles, achieved over 95% classification accuracy, demonstrating the huge potential for deep learning in MP detection. However, while the model shows high accuracy for virgin plastics, this model's efficacy on environmentally sourced MPs remains untested.
In another study, a dataset of over 64,000 Raman spectra from 47 environmental or wastewater samples was used to develop a human-computer hybrid approach.25 This method achieved high recall (≥99.4%) and precision (≥97.1%) for identifying MPs and reduced the annotation time from hours to under one hour per sample compared to human-only analysis.
Developing portable detection tools
Currently, in situ detection and quantification of MPs is difficult or even impossible, because of a lack of applicable methods. Certain environments, like wastewater treatment plants and water-intensive industries, contain high levels of organic and inorganic solids, complicating the detection of low-abundance MPs without sample pretreatment. Environmental factors, such as temperature and pressure fluctuations in natural water bodies, add further complexity by altering conditions at different depths, affecting the properties of MPs locally.
Recent advancements in compact light sources, detectors and optical components have led to the availability of portable and handheld photometers and spectrometers for environmental monitoring. Among these, commercial portable devices based on fluorescence, FTIR and Raman have shown promise. For example, a cost-effective portable Raman sensor has been designed to detect micrometer-sized magnetic plastic particles in water using a quartz cuvette.29 Additionally, a recent study utilized a portable photometer and fluorescent staining to measure the presence of MPs in water samples, representing significant progress toward accessible, field-ready detection methods.30
Towards faster, cost-effective and scalable methods
While no single technique provides a one-size-fits-all solution for detecting MPs, advancements in spectroscopy, mass spectrometry, imaging and AI-driven approaches are rapidly improving our ability to track these pollutants. The challenge now is to make these technologies faster, more cost-effective and scalable for widespread environmental monitoring.
By developing portable tools and integrating machine learning, we may soon have the capability to map the global distribution of MPs, as well as distinguish between different types of plastics, trace their origins and monitor their movement through ecosystems and food chains. By enhancing our detection capabilities, the scientific community can work toward informed, evidence-based policies and interventions aimed at reducing plastic pollution and protecting both environmental and human health.
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