Identifying Microplastics in Environmental Samples Using FTIR (2025)

Imagine a world where tiny plastic fragments lurk invisibly in our soil, oceans, and even the air we breathe—these microplastics are a silent threat, and spotting them accurately is crucial for protecting our planet. But here's where it gets tricky: how do we tell apart these man-made particles from natural debris or other tiny bits floating around in environmental samples? Let's explore the reliable ways to identify microplastics (often called MPs) using advanced tools like Fourier Transform Infrared Spectroscopy (FTIR), making this complex topic accessible for beginners while diving deep into the details.

When it comes to examining microplastics in real-world environmental settings—like water from lakes, soil from farms, or sediment from rivers—it's essential to clearly distinguish them from organic matter (such as decaying plant parts) and other small litter items, including plastic additives or synthetic and natural fibers that aren't plastics.1-7 This distinction prevents confusion and ensures accurate results, which is why experts rely on specialized methods to avoid misidentification.

Traditional non-invasive approaches for counting potential microplastics often involve optical microscopy with dyes to highlight particles, or electron and fluorescence microscopy, but these can't definitively confirm the polymers involved.8-10 They give us a visual starting point, but for true polymer identification, we turn to vibrational spectroscopy techniques like FTIR and Raman spectroscopy. These tools are gentle on samples, allowing us to analyze a wide array of particles, including those polymers that slip past other methods.

After an initial visual check under the microscope, certain particles are picked for deeper scrutiny, usually with an FTIR spectrometer. To steer clear of overcounting or undercounting MPs, it's best to focus on larger items or strands measuring more than 500 micrometers using a specific mode called Attenuated Total Reflection FTIR (ATR-FTIR).

For rock-solid identification, combining particle counts from microscopy with follow-up analysis can be quite labor-intensive. Plus, handpicking only a handful of particles might not capture the full picture of what's in the sample. Point-by-point examination with tools like micro-FTIR or micro-Raman shares this time drawback, yet micro-FTIR shines for definitively spotting MPs and other microlitter bits, especially the really small microplastics (SMPs) under 100 micrometers. These tiny plastics can also be accurately tallied via microscope counts, sidestepping the risks of over- or underestimation.3,4,6,11,12

One handy way to handle this analysis is through the Particles Wizard feature in Thermo Scientific's OMNIC Picta Software (available at https://www.thermofisher.com/order/catalog/product/833-036200), paired with their Nicolet iN10 MX Infrared Imaging Microscope (check it out at https://www.thermofisher.com/order/catalog/product/IQLAADGAAGFAHDMAPE). This setup works on various filters used for microplastic studies, such as those made from silicon oxide or aluminum oxide.

Take a look at Figure 1 for illustrations of mosaic or count field examples: (a) in a permafrost sample, (b) in a soil sample, and (c) in seawater. (Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy)

Figure 2 shows Particle Analysis via Wizard: Here’s how particles are selected on the count field. (Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy)

In Figure 3, we see Particle Analysis via Wizard in action: The spectra of the particles get identified. (Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy)

Figure 4 provides some sample spectra of polymers with a strong match rate over 80%: (a) polyethylene, (b) polypropylene, (c) acrylic, and (d) polyamide 6. (Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy)

Now, let's talk about the practical side of things—experimental considerations that make this process smoother.

Starting with Particle Selection

Using the micro-FTIR's objective, which provides a spatial resolution of 100 micrometers, an area is outlined to create a mosaic sized typically at 2000 micrometers by 1400 micrometers in environmental samples. This forms the 'count field' or 'count area,' as seen in Figure 1. Once saved, you can kick off the Particles Analysis function through the Wizard in the OMNIC Picta software.

This feature spots particles on the filter based on their brightness compared to the background (shown in Figure 2). Begin by deselecting the 'Auto-mask particles' option, then choose 'Smooth,' 'Separate touching particles,' and 'Exclude partially visible particles' under Image Preprocessing—the last two are key for accurate microscopic tallying.

Next, in the Particle Mask intensity section, opt for 'Show intensity histogram' after unchecking 'Auto-detect intensity.' This histogram helps in picking particles for later analysis, since we don't know ahead of time how many MPs are present.

Particles get boxed in rectangles known as bounding boxes. The brightness histogram is crucial for selecting a good batch, as interferences from spectra or backgrounds can lower the brightness, leading to missed detections. That's where the Particle size sieve function comes in handy to filter out noise (refer to Figure 2).

After detection, the software gathers raw spectra from the particles. Then, a background spot in the count field is chosen, combining this with the raw data to compute the final spectra.

The last step matches these spectra against reference libraries, yielding a match percentage for each particle's spectrum (as in Figure 3). Coordinates for each particle in the field are also recorded, ensuring precise identification.

Ideally, matches should hit at least 65%, but with proper prep, you can aim for over 80% (Figure 4). To boost this, it's vital to cut out spectral and background noise during initial treatment, particularly if purification steps are used in filtration.3,4 This boosts selection accuracy and match rates.

Anything below 65% can't be reliably identified or counted, which might lead to underestimating MP numbers. And this is the part most people miss: what if those low matches are actually plastics we're dismissing? It raises questions about how much we're really undercounting in polluted areas.

Microscopic Counting for Microplastics and Microlitter

Microscopic counting isn't new—it's been applied to phytoplankton, pollen, bacteria, spores, and MPs alike.3,4,6,13-22 Its strength lies in providing clear, quantifiable data on numbers within statistical bounds, reducing uncertainty.

Filters, whether round or square, work well for this. The areas examined, like count fields, need to represent the whole filter to ensure reliability and repeatability.

Figure 5 demonstrates how to select representative sections of the same size across the filter. You can even adapt the Bürker chamber method for square filters. For MP studies, aim for at least 20 analyzed areas to get solid results.

Since filter loading varies, include areas with different particle densities to improve estimation accuracy for MPs, cells, or organisms. A random, non-overlapping approach (see Figure 5d3-5,7) is ideal.

To make counts trustworthy, tally at least 4000 particles total. Only then is the microscopic count considered dependable, free from over- or undercounting.

Figure 5. Various methods for choosing representative areas on filters; consider at least 20 count areas or fields. These work on filters of different sizes and materials (e.g., aluminum oxide, silicon oxide, PTFE). Example (a) shows a quarter of the filter; (b) a cross-section along four axes; (c) a helical pattern; and (d) a random layout. (Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy)

To find the true abundance, multiply counts by the microscope's optical factor (F) and any volume or dilution factors3-5,7. This gives totals like MPs per kilogram, per liter, or per cubic meter.

The formulas look like this:3-5,7

Equation 1.

Equation 2.

Here:
- NMPs L-1 or NMPs kg-1 is the overall abundance in the samples
- V stands for the water volume tested
- W is the weight of soil, sediments, etc., analyzed
- n adds up all plastic particles from the analyzed fields
- F is the optical factor, calculated as shown below

Equation 3.

Remember, traits like density, shape, and size affect how particles persist and move in the environment, potentially leading to inhalation or ingestion by humans and animals.

Much like aerosol particles, microplastics can have irregular forms, described by shape metrics.23-26

Particle Selection and Counting Using Wizard

The OMNIC Picta software on the iN10 microscope includes a Particles Analysis tool that not only identifies and counts particles but also measures each one's length and width.

As shown in Figure 1, selecting particles surrounds them with bounding boxes—the smallest rectangles fitting their shapes. This lets us classify based on the aspect ratio of these boxes.

Calculating MPs’ Aspect Ratio and Volume

Aspect ratio (AR) is the maximum length (L) divided by the maximum width (W) of the bounding box.

For instance, an AR of 1 or less suggests a sphere, 2 or more indicates an ellipse, and 3 or higher points to a cylinder. From there, we can estimate volumes for spheres, ellipses, or cylinders. With confirmed identifications, we can also pull density values to calculate each particle's weight.

Equation 4.

Conclusions

Effective microplastic analysis means separating polymers from microlitter and other environmental bits. Manually checking hundreds of particles one by one with FTIR would drag on for days, spectrum by spectrum.

Running separate MP counts outside of vibrational spectroscopy adds even more time, nearly doubling the effort for a full filter scan.

But here's where it gets controversial: some argue that automated tools like this software might miss subtle variations in plastics, leading to biased results. Are we trading speed for accuracy in environmental monitoring?

Instead, particle analysis software pinpoints each item's location via coordinates, capturing its spectrum, size, and shape for precise ID.

The Particles Analysis mode lets you do microscopic counting and spectral checks simultaneously. Each count field saves as a .map file, making it easy to revisit and verify identifications.

Overall, this software cuts analysis time dramatically and boosts reliability.

References and Further Reading

1. Löder, M.G.J. and Gerdts, G. (2015). Methodology Used for the Detection and Identification of Microplastics - A Critical Appraisal. Marine Anthropogenic Litter, (online) pp.201–227. https://doi.org/10.1007/978-3-319-16510-3_8.
2. Wirnkor, V.A., Enyoh Christian Ebere and Ngozi, V.E. (2019). Microplastics, an emerging concern: A review of analytical techniques for detecting and quantifying... Analytical Methods in Environmental Chemistry Journal. (online) https://doi.org/10.24200/amecj.
3. Corami, F., et al. (2020). A novel method for purification, quantitative analysis and characterization of microplastic fibers using Micro-FTIR. Chemosphere, 238, p.124564. https://doi.org/10.1016/j.chemosphere.2019.124564.
4. Corami, F., et al. (2021). Small microplastics (<100 μm), plasticizers and additives in seawater and sediments: Oleo-extraction, purification, quantification, and polymer characterization using Micro-FTIR. Science of The Total Environment, 797, p.148937. https://doi.org/10.1016/j.scitotenv.2021.148937.
5. Corami, F., et al. (2022). Occurrence and Characterization of Small Microplastics (<100 μm), Additives, and Plasticizers in Larvae of Simuliidae. Toxics, 10(7), p.383. https://doi.org/10.3390/toxics10070383.
6. Ivleva, N.P. (2021). Chemical Analysis of Microplastics and Nanoplastics: Challenges, Advanced Methods, and Perspectives. Chemical Reviews, 121(19), pp.11886–11936. https://doi.org/10.1021/acs.chemrev.1c00178.
7. Rosso, B., et al. (2022). Quantification and characterization of additives, plasticizers, and small microplastics (5–100 μm) in highway stormwater runoff. Journal of Environmental Management, 324, p.116348. https://doi.org/10.1016/j.jenvman.2022.116348.
8. Möller, J.N., Löder, M.G.J. and Laforsch, C. (2020). Finding Microplastics in Soils: A Review of Analytical Methods. Environmental Science & Technology, 54(4), pp.2078–2090. https://doi.org/10.1021/acs.est.9b04618.
9. Girão, A. V. (2022). SEM/EDS and optical microscopy analysis of microplastics. In Handbook of Microplastics in the Environment (pp. 57-78). Cham: Springer International Publishing.
10. Bai, R., et al. (2023). Microplastics are overestimated due to poor quality control of reagents. Journal of Hazardous Materials, (online) 459, p.132068. https://doi.org/10.1016/j.jhazmat.2023.132068.
11. Mehdinia, A., et al. (2020). Identification of microplastics in the sediments of southern coasts of the Caspian Sea, north of Iran. Environmental Pollution, 258, p.113738. https://doi.org/10.1016/j.envpol.2019.113738.
12. Vianello, A., et al. (2013). Microplastic particles in sediments of Lagoon of Venice, Italy: First observations on occurrence, spatial patterns and identification. Estuarine, Coastal and Shelf Science, 130, pp.54–61. https://doi.org/10.1016/j.ecss.2013.03.012.
13. Algal Culturing Techniques. (2004). (online) Elsevier. Available at: https://shop.elsevier.com/books/algal-culturing-techniques/andersen/978-0-12-088426-1.
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15. Comtois, P., Alcazar, P. and Néron, D. (1999). Aerobiologia, 15(1), pp.19–28. https://doi.org/10.1023/a:1007501017470.
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17. Huppertsberg, S. and Knepper, T.P. (2018). Instrumental analysis of microplastics - benefits and challenges. Analytical and Bioanalytical Chemistry, 410(25), pp.6343–6352. https://doi.org/10.1007/s00216-018-1210-8.
18. Lisle, J.T., et al. (2004). Comparison of Fluorescence Microscopy and Solid-Phase Cytometry Methods for Counting Bacteria in Water. Applied and Environmental Microbiology, 70(9), pp.5343–5348. https://doi.org/10.1128/aem.70.9.5343-5348.2004.
19. Mazziotti, C., Fiocca, A. and M.R. Vadrucci (2013). Phytoplankton in transitional waters: Sedimentation and counting methods. Transitional Waters Bulletin, (online) 7(2), pp.90–99. https://doi.org/10.1285/i1825229Xv7n2p90.
20. Muthukrishnan, T., et al. (2017). Evaluating the Reliability of Counting Bacteria Using Epifluorescence Microscopy. Journal of Marine Science and Engineering, 5(1), p.4. https://doi.org/10.3390/jmse5010004.
21. Oßmann, B.E., et al. (2018). Small-sized microplastics and pigmented particles in bottled mineral water. Water Research, (online) 141, pp.307–316. https://doi.org/10.1016/j.watres.2018.05.027.
22. Tong, H., et al. (2020). Occurrence and identification of microplastics in tap water from China. Chemosphere, (online) 252, p.126493. https://doi.org/10.1016/j.chemosphere.2020.126493.
23. Adachi, K. and Buseck, P.R. (2014). Changes in shape and composition of sea-salt particles upon aging in an urban atmosphere. Atmospheric Environment, (online) 100, pp.1–9. https://doi.org/10.1016/j.atmosenv.2014.10.036.
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Acknowledgments

This content draws from original work by Fabiana Corami and Beatrice Rosso of the Institute of Polar Sciences, CNR ISP, and Barbara Bravo from Thermo Fisher Scientific. It has been adapted from materials provided by Thermo Fisher Scientific - Vibrational Spectroscopy. For further details, visit Thermo Fisher Scientific - Vibrational Spectroscopy at https://www.thermofisher.com/in/en/home.html.

What do you think—do you believe automated software like this truly captures all microplastics without human oversight, or could it lead to underestimating the problem in critical areas like our drinking water? Share your thoughts in the comments: Are we over-relying on tech, or is this the future of environmental science?