Synthetic generation of 2D data records based on Autoencoders

This work has been submitted to the IEEE for possible publication. 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. I NTRODUCTION Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a technique used to identify chemical components within a sample [1]. Initially, the sample, carried by a carrier gas, is introduced into the GC column, where interactions between the sample components and the column affect their transit speeds, leading to an initial separation.