31 August 2021
Saer Samanipour
Speaker: Saer Samanipour, Assistant Professor at the University of Amsterdam and honorary research fellow at the UQ, Australia
Abstract:
High resolution mass spectrometry is one of the main tools for chemical characterization of complex samples. The samples analyzed with this instrument result into large datasets comprising of up to 8.0e12 variables that could potentially carry crucial structural information about the sample chemistry. At the same time there are different sources of signal redundancy in such datasets. In this talk I will present two case studies where machine learning enables the removal of the data redundancy without any information loss. The first case study will discuss the seamless conversion from the profile mode to centroided and vice versa. In this case, we developed a self adjusting centroiding algorithm to detect and extract the meaningful information in such complex datasets. Additionally, a regression model was developed to convert the extracted information to the raw data. The second case study, is related to the development of a stochastic classification model to detect the isotopic signal in the mass spectra and therefore increase the level of confidence in the generated identifications. COLLOQUIUM
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