Microplastics Analysis Using FTIR Imaging

Microplastics (MPs) found in environmental samples must be identified with confidence so they can be easily distinguished from biogenic particles, such as particulate organic matter, and from other microparticles, including natural fibers, non-plastic synthetic fibers, and plastic additives. ^{1,2,3,4,5,6,7}

Since there are few ways to unambiguously identify polymers, optical microscopy that uses dye staining and electron and fluorescence microscopy are considered the standard non-invasive techniques to quantify suspected MPs.^8,9,10  

Vibrational spectroscopy employs Fourier Transform Infrared (FTIR) and Raman spectrometers, and this widely recognized non-destructive technique is well-suited to the characterization of polymers and other particles.

Selected particles are typically further analyzed after the optical investigation, usually with an FTIR spectrometer.

Attenuated Total Reflection FTIR (ATR-FTIR) can be used to analyze larger fibers or particles (> 500 μm). This method is suggested for larger particles as overestimation and underestimation of MPs can occur through FTIR alone.  

Particle quantification via microscopic counts and the subsequent analysis of selected particles is extremely time-consuming, particularly if the selected particles are to be unequivocally identified. It is also important to note that the few selected particles may not be representative of what is actually present in the sample.

Analysis of particles using micro-FTIR and micro-Raman can be equally time-consuming, but particle analysis via micro-FTIR allows MPs and other microlitter components to be unequivocally identified.

These particles can also be simultaneously and reliably quantified by microscopic count, avoiding over- or underestimation, even when investigating particles like small microplastics (SMPs) that are below 100 µm.^3,4,6,11,12

The Thermo Scientific^™ OMNIC^™ Picta^™ is the instrument software of the Thermo Scientific Nicolet^™ iN10 MX Infrared Imaging Microscope.

The software’s Particles WIZARD section is ideally suited to particle analysis in these applications. It can be performed on any filter used for the analysis of microplastics, for example, silicon oxide filters and aluminum oxide filters.

Figure 1. Examples of mosaic or count field: a) in permafrost sample, b) in soil sample, c) in seawater. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Figure 2. Particle Analysis via WIZARD: Example of how to select the particles on the count field. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Figure 3. Particle Analysis via WIZARD: The spectra of the particles are identified. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Figure 4. Some exemplary spectra of polymers optimally identified with a match percentage > 80 %: a) polyethylene, b) polypropylene, c) acrylic, d) polyamide 6. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Experimental Considerations

Particle Selection

Particle selection is performed within an area framed using the micro-FTIR’s objective. This objective features a spatial resolution of 100 µm.

A mosaic of definite dimensions is then drawn. For instance, the dimensions will be 2000 µm x 1400 µm in environmental samples. This mosaic will function as the ‘count area’ or ‘count field’ (Figure 1).

Once the mosaic has been saved, the Particles Analysis function can commence. This function is accessed via the WIZARD section of OMNIC Picta software.

Particles located on the filter’s surface, in the specific count area, will be detected in relation to the brightness value. The brightness value is defined as the brightness ratio of each particle in contrast to the background (Figure 2).

The user should uncheck the ‘Auto-mask particles’ option before selecting ‘Smooth,’ ‘Separate touching particles,’ and ‘Exclude partially visible particles’ in the ‘Image preprocessing’ section. The latter two of these options are essential for microscopic counting.

‘Auto-detect intensity’ should then be unchecked in Particle Mask intensity, while selecting ‘Show intensity histogram.’

It is possible to select the particles to be analyzed through the intensity histogram. The particles are enclosed in rectangles referred to as ‘bounding boxes.’ Because the amount of microplastics in that field is not known a priori, the image intensity histogram is necessary to be able to select an adequate and significant number of particles.

Any potential interference signal can be diminished using the Particle size sieve function (Figure 2). The brightness ratio is affected when spectral interferences and/or background interferences are present, resulting in the software detecting a lower number of particles.

Raw spectra of particles are collected following detection. Once spectra have been collected, a background location is selected on the count field. The combination of raw spectra and background spectra is used to calculate the resulting spectra of the particles.

The resulting spectra are then identified by comparing these spectra to reference libraries. A match percentage identifies each spectrum of each particle (Figure 3). Each particle’s coordinates in the count field are also retrieved, allowing the univocal identification of each particle.

Instrumental characteristics reveal that the optimal range of match percentage is ≥ 65 %, but, depending on the pretreatment, it is possible to achieve a match percentage of > 80 % or even higher (Figure 4).

It is necessary to eliminate spectral and background interferences during pretreatment, particularly when applying a purification procedure during the filtration.^3,4 This ensures efficient particle selection, enhancing the identification match percentage of each particle over the optimal range.

An identification match percentage of < 65 % means that it is not possible to identify and count particles well. MPs’ abundance is, therefore, underestimated.

Microscopic Counting for Microplastics and Microlitter

Microscopic counting has historically been employed for phytoplankton, pollen, bacteria, spores, and MPs.^3-6,13-22

One of microscopic counting’s major advantages is its capacity to eliminate doubt around the number of cells, organisms, or particles present within valid degrees of chance and computable limits.

It is straightforward to employ round or square filters to support counting. Analyzed filter areas (for instance, count fields or counting areas) must adequately represent the whole filter to avoid issues of reproducibility and representativeness.

Figure 5 shows different approaches for selecting representative measurement areas of the same size on the surface of the filter.

Figure 5. Different approaches may be used for representative measurement areas on filters; at least 20 count areas or count fields should be considered. These approaches can be employed on filters of different diameters and materials (e.g., aluminum oxide, silicon oxide, PTFE). Example a) represents a quarter of the filters; b) represents the cross-section of the four axes c) represents a helical assembly; and d) represents a randomized assembly. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

It is possible to apply the approaches employed in the Bürker chamber to square filters. When analyzing MPs, it is important to ensure that the number of filtering areas is equal to or greater than 20 to ensure meaningful and reliable quantification.

It is not possible to know the loading of the filters in advance, meaning that count areas with varying abundances should be considered to avoid issues related to the accuracy of extrapolation of findings related to organisms, cells, microplastics, or bacteria.

The randomized approach without overlapping (Figure 5d) has been shown to be the most suitable and in line with this guideline.^3,4,5,7 The microscopic count is representative when a significant and reliable number of particles has been analyzed. However, this value should never be below 4,000 particles.

The microscopic count is considered robust when these two conditions are met, meaning that the quantification will not be affected by under- or overestimation.

It is necessary to multiply the total counts from each filter by the appropriate microscope conversion (optical factor F) and volume or dilution factors to calculate the absolute abundance, for example, number of MPs per kg, number of MPs per L, or number of MPs per m.^3,4,5,7

Equations 1 and 2 can be used to calculate the abundances.

Equation 1.

Equation 2.

In these equations, N_MPs L^-1 or N_MPs kg^-1 represent the total abundance in the samples analyzed; V represents the volume of water analyzed, W is the analyzed weight of sediments, soil, etc., n, the sum of all the plastic particles in the count fields analyzed, and F represents the factor calculated via Equation 3.

Equation 3.

A particle or fiber’s size, density, and shape are essential characteristics that impact their permanence and transport in the environment, and their inhalation and/or ingestion by animals and humans.

For example, microplastic particles and fibers found in aerosols can exhibit a range of different irregular shapes, which shape descriptors can then describe.^23-26

Particle Selection and Counting Using WIZARD

The iN10’s OMNIC Picta software’s Particles Analysis function not only identifies and counts particles but also retrieves the length and width of each particle during the analysis.

Selecting particles in the software encloses them in rectangles (Figure 1). These rectangles are referred to as ‘bounding boxes’ because they correspond to the smallest rectangles enclosing each particle’s shape. Particles’ shapes can then be categorized based on the bounding boxes’ aspect ratios.

Calculation of Aspect Ratio and Volume of MPs

Aspect ratio (AR) is defined as the ratio between the maximum width (W) and maximum length (L) of the bounding box enclosing the shape. For example:

• Particles are considered spherical when the AR values are ≤ 1
• Particles are considered ellipsoidal when the AR values are ≥ 2
• Particles are considered cylindrical when the AR values are ≥ 3

AR allows volumes of particles and fibers to be calculated according to their geometrical shape, for instance, sphere, ellipse, or cylinder. Particles’ density can be retrieved because they have been optimally identified, meaning that each particle’s weight can then be calculated.

Equation 4.

Conclusions

When analyzing MPs, it is essential that polymers be differentiated from the rest of the microlitter components, as well as any other particles in the environmental matrix.

It could take a very long time to individually analyze hundreds of particles to obtain robust quantification, potentially taking several days to analyze all the FTIR spectra retrieved. Counting MPs separately from analysis using vibrational spectroscopy would also significantly contribute to this long analysis time, possibly taking as long as performing a full analysis of the entire filter.

Instead, a single particle is unambiguously located by its own spatial coordinates in each field count, allowing its spectrum to be optimally and unambiguously identified, and its size (length and width) collected.

Quantification via microscopic count is simultaneously performed with spectral identification when operating in Particles Analysis mode.

It is possible to save each count field with the .map filename extension, enabling a subsequent analysis of each particle in a precise count field to verify spectral identification.

The use of the Particles Analysis software tool makes microplastics analysis considerably less time-consuming and significantly more robust.

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. Doi: 10.1007/978-3-319-16510-3_8. https://link.springer.com/chapter/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) Doi: 10.24200/amecj. https://www.researchgate.net/publication/334587752_Microplastics_an_emerging_concern_A_review_of_analytical_techniques_for_detecting_and_quantifying_microplatics.
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. DOI: 10.1016/j.chemosphere.2019.124564. https://www.sciencedirect.com/science/article/abs/pii/S0045653519317886?via%3Dihub.
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. DOI: 10.1016/j.scitotenv.2021.148937. https://www.sciencedirect.com/science/article/abs/pii/S0048969721040092?via%3Dihub.
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. DOI: 10.3390/toxics10070383. https://www.mdpi.com/2305-6304/10/7/383.
6. Ivleva, N.P. (2021). Chemical Analysis of Microplastics and Nanoplastics: Challenges, Advanced Methods, and Perspectives. Chemical Reviews, 121(19), pp.11886–11936. DOI: 10.1021/acs.chemrev.1c00178. https://pubs.acs.org/doi/10.1021/acs.chemrev.1c00178.
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Acknowledgments

Produced from materials originally authored by Fabiana Corami and Beatrice Rosso from the Institute of Polar Sciences, CNR ISP; and Barbara Bravo from Thermo Fisher Scientific.

This information has been sourced, reviewed, and adapted from materials provided by Thermo Fisher Scientific - Vibrational Spectroscopy.

For more information on this source, please visit Thermo Fisher Scientific - Vibrational Spectroscopy.