Genetic Algorithm Adaptive Online Optimization Method for Multiple Reflection Time-of-Flight Mass Spectrometry Performance

Voltage parameters play a pivotal role in the sensitivity and resolution of Time-of-Flight Mass Spectrometry (TOFMS). The ion optical system within a complex mass spectrometer consists of numerous voltage parameters, necessitating high precision, which in turn makes the installation and debugging processes exceedingly challenging. Moreover, factors such as mechanical precision, environmental conditions, and aging of instrument components often render the optimal voltage parameter combination incompatible with the simulation design parameters. This study introduces a novel method for the online optimization of TOFMS performance, employing an adaptive genetic algorithm. The experiment was conducted on a self-constructed Multi-Reflection Time of Flight Mass Spectrometer (MR-TOFMS) device, performing step scan optimization experiments for two voltage parameters and adaptive optimization experiments for five voltage parameters, respectively. In comparison with the step scan method, the adaptive optimization method accomplished a wide-range, high-precision global search of five voltage parameters in just 2.25 hours. Subsequent to optimization, the sensitivity and resolution of the mass spectrometer improved by 52.4% and 50.6% respectively, in comparison to the scan optimization method. The findings underscore the significant potential of adaptive optimization methods in debugging and enhancing the performance of complex mass spectrometers.