2025/02/18 by Darius Couchard, Couchard, Darius, Oscar Javier Olarte +3
Computer Science · #FOS: Computer and information sciences #FOS: Electrical engineering #Image Processing and 3D Reconstruction #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2502.13183
openalex publication_date 2025/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.