Synthetic generation of 2D data records based on Autoencoders

TL;DR

This paper presents a novel autoencoder-based method for generating synthetic 2D spectral data, specifically applied to GC-IMS, to enhance classification performance when labelled data are limited.

Contribution

The study introduces a new deep learning approach for synthetic 2D spectral data generation using Autoencoders, applicable beyond GC-IMS to other spectral measurement techniques.

Findings

Synthetic data improved classification accuracy.

Method is broadly applicable to 2D spectral data.

Enhanced machine learning performance with limited labelled data.

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.