Covariance matrix adaptation based tuning of mass spectrometry parameters using experimental probability distributions

The operation of a mass spectrometry instrument, used in analytical chemistry, for custom applications requires the careful tuning of several instrument settings by an expert. In this work, we developed a model that allows the instrument to tune itself. The approach employs a fast, adaptive evolutionary algorithm, the Covariance Matrix Adaptation evolutionary strategy, to tune a mass spectrometry instrument. By developing a scheme for normalizing the values of the outcome variables (resolution, intensity and peak shape of a calibrant peak signal) based on their experimental probability distributions, we combined the outcomes into a single score that was used as the fitness score for the search algorithm. This approach resulted in a more thorough examination of the search space, and in an economical amount of time by being adaptive, resulting in a more stable tuning, no matter the initial state of the settings involved.