The research team examined gastrointestinal tract samples pooled from cultured specimens of both species raised in inland and semi-coastal pond systems in Kerala state. In total, the researchers identified 307 individual microplastic items across the sampled animals. Average contamination loads came out at 0.709 plus or minus 2 particles per gram of gastrointestinal tract tissue for the giant freshwater prawn, and 1.015 plus or minus 2 particles per gram for the white leg shrimp, meaning the Pacific white shrimp, though the smaller animal, carried the heavier per-gram burden. Particle sizes ranged from below 250 micrometers up to 5 millimeters, but the distribution was far from uniform. The dominant size fraction fell between 500 micrometers and 1 millimeter, a range that overlaps with particles small enough to be mistaken for food by grazing and filtering crustaceans yet large enough to lodge in digestive structures. Visually, the story was equally consistent: blue-colored microplastics with a fiber morphology dominated both species, accounting for 47.31 percent of recovered particles in the giant prawn and 46.28 percent in the white leg shrimp. Fibers of this kind are widely associated with the degradation of fishing nets, ropes, synthetic textiles, and, critically in an aquaculture context, the polymer-based materials used in pond linings, aeration equipment, and feed packaging.

Identifying what the particles were actually made of required more than a microscope. The team deployed attenuated total reflectance Fourier transform infrared spectroscopy, known as ATR-FTIR, alongside a confocal Raman microscope integrated with atomic force microscopy. These complementary techniques interrogate the vibrational fingerprints of polymers, allowing researchers to match observed spectra against reference libraries and assign each particle to a specific plastic chemistry. The analysis confirmed five polymer types: polyethylene, polystyrene, acrylonitrile-butadiene-styrene, commonly abbreviated ABS, polycarbonate, and polypropylene. The presence of polyethylene and polypropylene is unsurprising, since these are the most produced plastics on Earth and permeate packaging, tubing, and agricultural films. But the detection of ABS and polycarbonate carried more weight, because these engineering polymers are ranked among the more hazardous plastic families in published chemical-composition-based hazard assessments. Polycarbonate raises particular concern due to its association with bisphenol A monomers, while ABS can leach styrene and acrylonitrile residues.

To translate these polymer identities into a measure of danger, the researchers calculated a Polymer Hazard Index, or PHI, for each species. This index weights the observed polymer mixture by the toxicity scores assigned to each plastic type in the widely cited Lithner hazard ranking, producing a single number that reflects not just how much plastic an animal has ingested but how toxic that plastic is likely to be. The giant freshwater prawn scored higher on this metric, with a PHI of 29.06 compared with 22.92 for the white leg shrimp, a direct consequence of the ABS and polycarbonate found in its digestive tracts. On polymer toxicity alone, the prawn appeared to be the riskier meal. Yet the authors recognized that hazard rankings capture only one dimension of exposure, and this is where the study makes its most distinctive contribution.

The team then constructed an integrated Pollution Risk Index, or PRI, which folds together three independent variables: the total microplastic load in the animal, the Polymer Hazard Index, and the shape profile of the ingested particles. Shape matters because fibers, with their high aspect ratios and needle-like geometry, are considered more likely to cause physical irritation, penetrate tissue, and persist in the gut compared with fragments or films. When all three factors were combined, the ranking inverted. The white leg shrimp registered a PRI of 15.12, exceeding the giant prawn’s 13.72, because its higher ingestion rate of particles outweighed the prawn’s more hazardous polymer cocktail. The result is a cautionary lesson in risk assessment methodology: single-metric approaches can mislead, and a composite index that accounts for load, chemistry, and morphology simultaneously produces a fundamentally different picture of which farmed species poses the greater ecological and food safety concern.

The machine learning component added a further layer of analytical rigor. The researchers trained a random forest model, an ensemble method that builds hundreds of decision trees on random subsets of the data and aggregates their votes, to classify microplastic risk levels from the contamination dataset. The model achieved a classification accuracy of 91.3 percent, and, crucially, its internal feature importance analysis revealed which variables carried the most predictive power. Three emerged as key predictors: the species of the animal, the microplastic density in its tissues, and the polymer type of the ingested particles. Random forests have a long track record in ecological classification, valued for their robustness to noise, their resistance to overfitting, and their ability to capture nonlinear interactions among predictor variables that traditional statistical models miss. Their application here suggests that risk profiling in aquaculture could eventually become predictive rather than merely descriptive, allowing regulators to estimate contamination risk from a handful of measurable parameters without exhaustive particle-by-particle screening for every batch of farmed product.

The implications extend well beyond the ponds of Kollam. Global aquaculture now supplies more farmed aquatic animal protein than wild capture fisheries, and shrimp and prawn farming is among its fastest-growing and most export-oriented sectors. India is one of the world’s largest shrimp producers, and both species examined in this study anchor major industries: white leg shrimp dominates international seafood trade, while the giant freshwater prawn is a staple of domestic consumption and regional markets. Microplastics in farmed crustaceans therefore sit at the intersection of food security, rural livelihoods, and export economics. Previous research has established that farmed shrimp can ingest microplastics from feed, pond water, and sediments, and that plastic particles can accumulate across grow-out cycles, sometimes in an age-dependent fashion. The Kerala findings add a tropical freshwater dimension to a literature that has traditionally focused on marine systems, and they underscore that even managed, semi-closed production environments are not sealed off from plastic pollution.

The presence of microplastics in the gastrointestinal tracts of farmed prawns also raises questions about the pathway to human exposure. In crustaceans destined for market, the digestive tract is not always removed before consumption, particularly for smaller shrimp that are eaten whole or lightly processed. Experimental work has shown that crustacean digestion can fragment larger microplastics into nanoplastics, potentially increasing their bioavailability, and laboratory studies have documented physiological effects in shrimp ranging from altered gut microbiota to reduced immune competence and heightened vulnerability to pathogens such as white spot syndrome virus. Microplastic particles also act as vectors for other contaminants, adsorbing heavy metals, persistent organic pollutants, and antimicrobial residues from the surrounding water, which means the health burden of an ingested fiber may exceed that of the plastic itself. The authors of the current study note that their combined chemical and morphological dataset provides exactly the kind of granular information needed to begin estimating realistic dietary exposure levels for consumers.

What distinguishes this research is its methodological architecture. Rather than presenting abundance figures in isolation, the team layered multiple analytical instruments, two complementary risk indices, and a supervised learning classifier into a single workflow that could, in principle, be replicated in any aquaculture region. The combination of ATR-FTIR and confocal Raman spectroscopy with atomic force microscopy addresses a persistent weakness in the microplastics field, where visual identification alone has been shown to overestimate particle counts and misclassify non-plastic materials. Spectroscopic confirmation, as applied here, raises confidence that every counted particle is a genuine synthetic polymer, and open-source spectral libraries are making such approaches increasingly accessible to laboratories in producing countries. The random forest layer, meanwhile, converts static contamination snapshots into a predictive tool, aligning aquaculture monitoring with a broader movement in environmental science toward machine learning-assisted pollution assessment, from stormwater microplastics modeling to water quality criteria prediction.

The study received no dedicated external funding but benefited from infrastructure support under India’s DST-FIST program at Fatima Mata National College in Kollam, and the sampling relied on cooperation with local aquaculture operations in the region. The authors emphasize that their framework, combining polymer-resolved contamination data with integrated risk indices and machine learning classification, is intended as a transferable template for microplastic risk assessment in cultured aquatic food systems anywhere. As plastic production continues to climb and as demand for farmed aquatic protein grows in parallel, tools that can rapidly profile contamination and rank risk across species, farms, and regions will become essential for food safety regulators, certification schemes, and the aquaculture industry itself. The Kollam prawns may be small animals, but the analytical machinery now pointed at them signals where the science of food contamination monitoring is headed.

Paint is a composite material rather than a single type of plastic. Modern coatings can contain polymers, pigments, binders, additives and mineral fillers, and their formulations vary widely depending on whether the product is intended for a house, automobile, ship, road surface, industrial structure or wooden object. Sunlight, heat, abrasion, salt water and repeated freezing and thawing can gradually break down a coating into particles ranging from visible flakes to microscopic fragments. Once released, those particles can enter stormwater, rivers, coastal sediments and the ocean. Some may be carried through the atmosphere as dust, while others accumulate near heavily painted infrastructure, harbors and roadways. Because the particles often retain colors and textures associated with their original coatings, they may be recognizable to a trained observer—but visual clues become less dependable as fragments weather, lose pigment or become mixed with other debris.

To build PLoPP, the researchers analyzed 90 paints spanning seven sectors: architectural, automotive, consumer, general industrial, marine, road-marking and wood coatings. The collection covered 15 colors and five appearances, including glitter, gloss, matte, pearl and semi-gloss finishes. It also represented at least 25 polymers, although polyurethane, polyurethane acrylics and polyvinyl chloride dominated the library. From these materials, the team generated 263 spectra. A spectrum is effectively a chemical fingerprint: it records how a material absorbs infrared light at different wavelengths. Different molecular bonds vibrate at characteristic frequencies, allowing scientists to infer the chemical composition of a tiny particle even when its origin cannot be established by sight. By assembling many reference spectra in one paint-specific database, the researchers aimed to give environmental scientists a much stronger comparison set than a general plastic library could provide.

The central analytical technique was attenuated total reflectance Fourier-transform infrared microspectroscopy, known as µATR-FTIR. In FTIR analysis, infrared radiation is directed at a sample and the resulting pattern of absorbed wavelengths is measured. The attenuated-total-reflectance approach uses contact between the sample and a crystal to probe the material’s surface, while microscopy allows the instrument to target individual particles rather than a bulk mixture. That distinction matters because environmental samples commonly contain many particle types at once. A paint flake may be only one item among fibers, packaging fragments, tire-related particles, biological material and mineral grains. A general FTIR database can identify the polymer class, but it may not distinguish a painted plastic fragment from an unpainted fragment made from a similar polymer. PLoPP adds paint-specific reference patterns that can improve that decision.

The researchers also created a visual key to make identification possible before, or alongside, instrumental analysis. The guide organizes particles according to observable traits such as color, surface appearance and morphology. A fragment with a bright metallic sheen, layered structure or a characteristic matte surface may provide an immediate clue that it originated from a coating. Yet the study’s design recognizes that appearance is not proof of composition. Weathering can make glossy particles dull, while pigments and additives can obscure the underlying polymer signal. The visual key therefore works as a structured screening method rather than a replacement for spectroscopy. Its value is particularly important for laboratories that do not have immediate access to advanced instruments, and it may help researchers select which particles deserve more detailed chemical analysis.

To test whether the reference collection could separate paint from other microplastics, the team developed a spectral-analysis pipeline using a support vector machine, a type of machine-learning algorithm commonly used to classify complex data. The model learns boundaries between categories by examining patterns in the spectra rather than relying on a single chemical peak. Preprocessing steps included standard normal variate transformations, which can reduce variation caused by scattering and differences in signal intensity, and principal component analysis, which compresses many correlated spectral measurements into a smaller number of meaningful dimensions. When tested on pristine paint and non-paint microplastic samples, the model achieved an overall accuracy of 92 percent. That result indicates that the chemical fingerprints contained enough information to distinguish the two groups under controlled conditions.

Environmental samples presented a more difficult challenge, as the researchers expected. Using particles collected in Plymouth, United Kingdom, and spectra from Charleston, South Carolina, the team examined how the tools performed on materials that had been exposed to real-world conditions. The machine-learning model’s accuracy fell to 55 percent when it attempted to differentiate environmental paint particles from non-paint microplastics. Weathering likely contributed to the decline: ultraviolet radiation, oxidation, abrasion and chemical exposure can alter the surface chemistry of a particle, while dirt and biological films can add signals not present in pristine samples. The lower result is an important warning against treating laboratory accuracy as a direct measure of field performance. A model trained on clean reference materials may need much broader training data before it can reliably classify the chemically messy particles found in nature.

The other approaches performed better in the environmental tests. The visual key achieved an average accuracy of 92 percent for particles, while correlation-based searches using PLoPP in OMNIC software correctly classified 86 percent of environmental particle spectra as paint or non-paint. Correlation-based searching compares the shape of an unknown spectrum with reference spectra and assigns a match according to their similarity, often expressed through a hit quality index. Together, these findings suggest that no single method is likely to solve the identification problem in every setting. Visual assessment can be fast and surprisingly effective, but it depends on training and may be vulnerable to observer judgment. Spectral searches provide chemical evidence, though results can be affected by weathering and mixed materials. Machine learning can process large numbers of spectra, but its reliability depends heavily on how representative its training library is.

The researchers say PLoPP is intended as a foundation for improving estimates of paint-related microplastic pollution, not as a final classification system. One unanswered question is whether paint fragments can be assigned reliably to the sector in which they were used. If spectra or combinations of pigments, polymers and surface features prove distinctive enough, future versions of the library might help connect particles to road markings, marine coatings, buildings or vehicles. That could allow scientists to identify pollution hotspots and determine which activities contribute most to environmental contamination. For now, the study demonstrates the practical value of creating a paint-specific reference collection. By making the hidden fingerprints of coatings easier to recognize, the researchers offer a way to bring a previously overlooked source of microplastics into sharper focus—and potentially transform how scientists track the colorful fragments accumulating beyond the surfaces they once protected.