Microplastics have become a near-ubiquitous presence in the natural world,
turning up in oceans, soil, drinking water and increasingly in the food
supply. Detecting them in seafood, however, has long required a laborious
and destructive process: chemically digesting or otherwise breaking down the
sample to isolate the plastic particles before they can be identified.
Working together with colleagues at Virginia Tech,
Lifeng
Zhou, PhD, an assistant professor of electrical and computer
engineering at Drexel University, has developed a way to detect
microplastics directly on fish surfaces without destroying them, by
combining light-based imaging with artificial intelligence. The study
appears in the Journal of Chemometrics.
The technique at the center of the research is hyperspectral imaging, which
captures a continuous spectrum of light intensity across hundreds of
wavelengths for every pixel in an image, integrating spatial and spectral
information in a way that enables precise material identification. The
approach has gained traction in food safety research because it requires
minimal sample preparation and is well-suited for field applications. The
challenge in detecting microplastics on food, however, is that the spectral
profiles of common plastic polymers like polyethylene overlap significantly
with those of organic food components such as proteins, water and lipids,
limiting the effectiveness of conventional analysis.
To overcome that challenge, the team trained neural network models to
classify each pixel in a hyperspectral image as either microplastic or fish
tissue. They contaminated tilapia fillet samples with small polyethylene
microspheres, 300 and 600 micrometers in diameter, and scanned them using a
near-infrared hyperspectral imaging system. The best-performing approach, a
one-dimensional convolutional neural network (1D-CNN) applied directly to
the full spectral data, achieved detection scores of 0.963 for the larger
particles and 0.950 for the smaller ones.
"The convolutional architecture is particularly well-suited to this problem
because it learns from localized regions of the spectral profile, capturing
correlations between adjacent wavelengths rather than treating each one
independently," Zhou said. "That sensitivity to local patterns is what
allows it to distinguish microplastics from fish tissue even when the two
look nearly identical at a glance."
That result held up only when the neural network was given access to the
complete spectral data. The team tested whether applying principal component
analysis, a common technique for simplifying large datasets, would improve
performance. It did not. Compressing the spectral data before training
consistently lowered detection accuracy, suggesting the model relies on
fine-grained spectral details that dimensionality reduction tends to
discard.
The findings point toward a practical path for food safety screening that
sidesteps the need for sample destruction. Current quality-control methods
depend on isolating microplastics through chemical digestion and examining
them under microscopes or spectrometers, a slow process requiring
specialized equipment. An imaging-based approach could eventually be adapted
for use at processing plants and fish markets without extensive sample
preparation.
"Microplastic contamination in seafood is a problem regulators are still
working out how to address, and part of what makes that hard is the absence
of fast, standardized detection methods," Zhou said. "Establishing those
methods is really the next frontier, and we see this kind of imaging-based
approach as a foundation for that work."
The researchers note that the current study was conducted under controlled
laboratory conditions and focused on a single plastic type. Future work will
aim to expand the training dataset to include more varieties of seafood,
other plastic polymers commonly found in marine environments, and samples
with varying moisture levels, which can complicate spectral readings.
Read the full study at
https://doi.org/10.1002/cem.70088