Mussels are widely used as sentinel species in coastal monitoring programs for microplastic contamination. Because they are suspension feeders, they process substantial volumes of water and retain particles present in the water column; consequently, the particles found in their tissues provide an integrated indication of contamination at a given location.
Determining the polymer type of these retained particles depends on chemical analysis, since visual examination alone is insufficient. Material isolated from digested tissue can vary greatly in size and is often mixed with mineral and organic matter that cannot be reliably separated from plastic particles by appearance alone.
FTIR imaging addresses this limitation by generating chemically resolved maps across large filter surfaces, including complete filters, and assigns particles to polymer classes using their infrared spectra while also recording particle size and shape.
This article presents the application of this approach to microplastic analysis in two mussel species collected from Guanabara Bay, Rio de Janeiro, during two seasons, following the work of Dr. J. Paulo Felizardo and Dr. C. Rodrigues from UFF, Brazil.
Experimental
In total, 120 mussels were obtained from two aquaculture locations in Guanabara Bay: Jurujuba and the area near the Rio–Niterói Bridge. Sampling included the brown mussel Perna perna and the Asian green mussel Perna viridis. Specimens were collected in February 2025, during the rainy season, and in August 2025, during the dry season, so that seasonal differences in particle burden could be considered.
Tissue samples underwent alkaline digestion with a 10% KOH solution at a five-to-one solution-to-sample volume ratio. They were maintained at 40 °C for 72 hours. After digestion, the material was passed through a stainless-steel mesh with a 25 μm pore size, and the retained material was placed into glass beakers.
Density separation was then performed using 10 mL of NaI solution (4.4 mol/L; 1.8 g/cm3), followed by 15 minutes of sonication and at least 15 minutes of mechanical mixing. A further 10 mL of NaI solution was introduced during mixing, and this procedure was repeated twice.
The samples were then centrifuged at 500 × g for roughly two minutes; this step was repeated when visible suspended material persisted. The low-density supernatant was finally collected by vacuum filtration onto 0.2 μm anodisc aluminum oxide filters.
A procedural blank, processed identically except that no biological tissue was included, was used to assess contamination introduced during preparation.
Figure 1. Data obtained from a Guanabara Bay mussel tissue sample. Left: transmitted-light mosaic of the anodisc filter following alkaline digestion and densitybased extraction. Right: corresponding hyperspectral μFTIR map acquired with the Bruker LUMOS II and evaluated with the Bruker MPID software. Image Credit: Dr. J. Paulo Felizardo and Dr. C. Rodrigues.
Results
The anodisc filters were examined in transmittance mode with a Bruker LUMOS II FTIR imaging microscope, and the resulting data was analyzed using Microplastic Identifier (MPID) software, which applies machine learning to detect and classify particles.
Microplastics were identified in every one of the 120 individuals examined within the measured size range, showing that particle exposure is widespread in Guanabara Bay under the preparation and μFTIR imaging workflow used. The complete detection rate reflects both the extent of particulate pollution in the bay and the sensitivity of the analytical approach.
Perna viridis contained more particles per individual in absolute terms, averaging 5.4 particles, than P. perna, which averaged 2.2 particles. After adjustment for soft-tissue mass, however, this pattern changed: contamination was significantly greater in P. perna, at 0.54 ± 0.45 particles/g, compared with 0.19 ± 0.13 particles/g in P. viridis.
Variation in body size, tissue composition, filtration activity, or exposure at the collection sites may explain this result; accordingly, it should be considered alongside biological and environmental factors.
Across both species, polypropylene (PP) was the most frequently detected synthetic polymer. In P. perna, PP was represented predominantly by fragments (92%), with average dimensions of 4.33 μm in width and 35.77 μm in length.
The abundance of PP is compatible with its low density and extensive application in packaging, ropes, and other materials associated with marine environments, although polymer identification by itself cannot establish the source of the particles.
Figure 2. Exemplary MPID result obtained from a Guanabara Bay mussel tissue sample. Extensive particle information is given together with an interactive classification map. When a particle or particle class is selected, all respective information is directly displayed. Image Credit: Bruker Optics
Conclusion
FTIR microspectroscopy is highly appropriate for analyzing microplastics in digested biological tissue. It can detect particles smaller than those typically identified during visual sorting and provides automated, chemically specific polymer classification, thereby limiting variation introduced by different operators.
The presence of microplastics in every mussel sample highlights the scale of contamination in Guanabara Bay. The contrast in mass-normalized particle burdens between P. perna and P. viridis suggests that biological differences among species need to be incorporated into standardized biomonitoring approaches.
As filter feeders, mussels take up microplastics from the surrounding water and can therefore act as bioindicators of environmental pollution. Their position in marine food webs and their use as seafood also mean that they may represent a pathway through which particles reach higher trophic levels, including humans.
Ongoing monitoring is therefore important for evaluating ecological contamination and possible risks to human health.
Acknowledgments
Produced using materials originally authored by João Paulo Felizardo (PhD), Camila Rodrigues e Silva (PhD), and Alexander Staub (PhD), Product Manager IR Microscopy, Bruker Optics GmbH & Co. KG.
This information has been sourced, reviewed, and adapted from materials provided by Bruker Optics.
For more information on this source, please visit Bruker Optics.