| Download | - Download dataset part 1: Autoencoder-based detection of nanoplastics in biological matrices via infrared hyperspectral imaging (ML_ILIM_AE_TRAIN.IPYNB, 48 KiB)
- Download dataset part 2: Autoencoder-based detection of nanoplastics in biological matrices via infrared hyperspectral imaging (ML_ILIM_AE_EVAL.IPYNB, 57 KiB)
- Download dataset part 3: Autoencoder-based detection of nanoplastics in biological matrices via infrared hyperspectral imaging (ML_ILIM_AE_EVAL_VOILA.IPYNB, 148 KiB)
- Download dataset part 4: Autoencoder-based detection of nanoplastics in biological matrices via infrared hyperspectral imaging (ILIM_AE_ENVIRONENT-CONDA-FORGE-CUDA.YAML, 9 KiB)
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| DOI | Resolve DOI: https://doi.org/10.4224/40004000 |
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| Author | Search for: Prezgot, Daniel1ORCID identifier: https://orcid.org/0000-0001-6498-4422; Search for: Sakib, Sadman1; Search for: Chen, Maohui1; Search for: Pegoraro, Adrien1ORCID identifier: https://orcid.org/0000-0003-3334-227X; Search for: Corriveau, David1; Search for: Zou, Shan1ORCID identifier: https://orcid.org/0000-0002-2480-6821 |
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| Affiliation | - National Research Council Canada. Metrology Research Centre
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| Format | Text, Dataset |
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| Abstract | 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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| Date created | 2026-03 |
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| Publisher | National Research Council Canada |
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| Licence | |
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| Note | Description of contents: Attached is python code in the form of Jupyter notebooks used for the training and use of models used in this work.
- ML_ILIM_AE_Train.ipynb - Contains code for the training of FC AE, CNN AE, and CNN-FC AE models used for the residual anomaly detection process.
- ML_ILIM_EVAL.ipynb - Contains code for applying the guided or unguided residual anomaly detection process to a target dataset using a pre-trained model.
- ML_ILIM_AE_Eval_Voila.ipynb - A version of the ML_ILIM_AE_Eval which can be run as a standalone web application using Voilà. Contains UI elements for configuration of processing parameters and figure display.
- ILIM_AE_environment-conda-forge-cuda.yaml - environment used for this work. Note: uses a CUDA-enabled version of PyTorch, reinstall PyTorch if not using a GPU. |
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| Language | English |
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| Export citation | Export as RIS |
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| Collection | NRC Research Data |
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| Record identifier | 224e3df6-b2e4-47f8-b498-3eee29ed9c2b |
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| Record created | 2026-03-25 |
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| Record modified | 2026-04-09 |
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