Synthetic generation of 2D data records based on Autoencoders

• URL: http://arxiv.org/abs/2502.13183v1
• Date: Tue, 18 Feb 2025 10:40:47 GMT
• Title: Synthetic generation of 2D data records based on Autoencoders
• Authors: Darius Couchard, Oscar Olarte, Rob Haelterman,
• Abstract summary: Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique. Data generated by GC-IMS is typically represented as two-dimensional spectra. This study introduces a novel method for generating synthetic 2D spectra using a deep learning framework based on Autoencoders.
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• Abstract: Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique widely used for identifying components in gaseous samples by separating and analysing the arrival times of their constituent species. Data generated by GC-IMS is typically represented as two-dimensional spectra, providing rich information but posing challenges for data-driven analysis due to limited labelled datasets. This study introduces a novel method for generating synthetic 2D spectra using a deep learning framework based on Autoencoders. Although applied here to GC-IMS data, the approach is broadly applicable to any two-dimensional spectral measurements where labelled data are scarce. While performing component classification over a labelled dataset of GC-IMS records, the addition of synthesized records significantly has improved the classification performance, demonstrating the method's potential for overcoming dataset limitations in machine learning frameworks.

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