Neural network algorithm enables mass calibration autocorrection for miniature mass spectrometry systems

Mass spectrometry (MS) is a powerful analytical technology widely used in a broad range of applications. Laboratory-scale mass spectrometers, however, are hardly used outside the analytical laboratories due to the large sizes and weights. Miniature mass spectrometers are therefore developed to facilitate on-site MS analysis. How to stabilize their analytical performances under complex environmental conditions on-site is a challenging problem, which needs to be addressed for the development of miniature MS instrumentation. Here, we report a neural network algorithm which enables automatic mass calibration corrections for a Cell miniature MS system (PURSPEC Technologies Inc.). To simulate the change of complex environmental conditions on-site, variations of temperature from 5 °C to 40 °C, pressure from 98647 Pa to 99406 Pa, humidity from 30 % to 65 %, were employed. The mass accuracy, characterized by the difference between measured mass and nominal mass, after autocorrection of the algorithm was within 0.08 Da.