CNN-based Deep Auto-Encoders for Limited Gas Chromatography - Ion Mobility Spectrometry Data

Gas Chromatography - Ion Mobility Spectrometry (GC-IMS) serves as a valuable analytical tool for enhancing non-targeted screening of volatile organic compounds in a wide range of different applications. Nowadays, authenticating various food samples such as honey and olive oil, as well as identifying microorganism cultures, is essential for practical applications and can be effectively achieved using GC–IMS alongside various classification methods. However, limited resources and the time-intensive data collection process have led to a scarcity of datasets with a relatively low amount of data. As a result, training of generalized classification models becomes more challenging and complex. To address issues, such as effectively learning essential spectral features, compressing the data into a more compact, representative latent space of less than 1% of the input parameters, and classifying unseen spectra with limited training data available, we propose utilizing various configurations of CNN-based Deep Auto-Encoders. Moreover, we propose a detailed pre-processing pipeline along with three different denoising protocols for GC-IMS spectra to achieve a better generalization of our models. Through the evaluation of the proposed models utilizing three publicly available datasets, the experimental results demonstrate that these models outperform state-of-the-art methods reported in the literature across various tasks, including the classification of complex food samples and the identification of microorganism cultures.