Comprehensive Mass Spectrometric Mapping of Chemical Compounds for the Development of Algorithms for Machine Learning and Artificial Intelligence

The influence of the accuracy of mass measurements on the number of possible structural compositions and the computation time of computer-aided interpretation of mass spectrometric data has been evaluated. Experimental measurements have been performed for two model objects in the range of small and medium masses using high, ultrahigh, and extreme high resolution electrospray ionization mass spectrometers. The number of possible solutions have been examined and prospects of using machine learning in combination with mass spectrometry for predicting new data on reaction mechanisms and searching for hidden relationships in the chemical space have been demonstrated. It has been shown that there are two types of relationships between the molecular formula and the mass determination error depending on the ion mass: a nonlinear curve is observed for small molecules and a linear relationship is observed for large molecules.