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Autoencoder-based detection of nanoplastics in biological matrices via infrared hyperspectral imaging
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Scientists have developed a smart imaging tool that uses AI to spot tiny plastic particles (nanoplastics) hiding inside living cells, even when the biological material around them makes detection tricky. This matters because nanoplastics are so small they may slip into our cells and tissues, and having a reliable way to "see" them is a key step toward understanding whether — and how much — they might affect our health. This study focused on building and testing the detection method itself, not on measuring health effects, so more research is needed to link these findings to actual health risks.
This repository contains code used for autoencoder-based detection of micro- and nano-plastics (MNPs) in biological matrices. In this work, an autoencoder-based anomaly detection approach is employed to learn the biological matrix signal and then extract and highlight targeted extraneous signals from infrared spectra acquired with quantum cascade laser infrared (QCL-IR) microscopy. Residual-based anomaly mapping preserved characteristic spectral features, enabling heatmaps corresponding to known vibrational bands and visualization of localized nanoscale plastic (NP) accumulations in two- and three-dimensional cell culture models. Fully-connected (FC), convolutional neural network (CNN) and hybrid (CNN-FC) autoencoder architectures were evaluated, with FC or CNN-FC models accurately reconstructing spectra while preserving the spectral signature of the NPs.
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