GC EI M Spectrum

Gas chromatography coupled with electron ionization mass spectrometry (GC-EI-MS) generates spectral data used to identify and quantify compounds in complex mixtures. Current research focuses on leveraging machine learning, particularly convolutional neural networks and other deep learning models incorporating techniques like basis projection to address challenges posed by the high dimensionality and sparsity of GC-EI-MS data, improving compound identification and analysis even in complex scenarios like those encountered in extraterrestrial sample analysis. These advancements are significantly impacting various fields, including infectious disease diagnostics and planetary science, by enabling more efficient and accurate analysis of complex chemical compositions. The development of robust automated methods for data analysis, such as improved region-of-interest selection, is also a key area of ongoing research.