In
light of the growing awareness regarding the ubiquitous presence
of microplastics (MPs) in our environment, recent efforts have been
made to integrate Artificial Intelligence (AI) technology into MP
detection. Among spectroscopic techniques, Raman spectroscopy is preferred
for the detection of MP particles measuring less than 10 μm,
as it overcomes the diffraction limitations encountered in Fourier
transform infrared (FTIR). However, Raman spectroscopy’s inherent
limitation is its low scattering cross section, which often results
in prolonged data collection times during practical sample measurements.
In this study, we implemented a convolutional neural network (CNN)
model alongside a tailored data interpolation strategy to expedite
data collection for MP particles within the 1–10 μm range.
Remarkably, we achieved the classification of plastic types for individual
particles with a mere 0.4 s of exposure time, reaching an approximate
confidence level of 85.47(±5.00)%. We postulate that the result
significantly accelerates the aggregation of microplastic distribution
data in diverse scenarios, contributing to the development of a comprehensive
global microplastic map.