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

• Published in: International journal of mass spectrometry Vol. 490; p. 117085
• Main Authors: Wei, Yanjun, Jiao, Bin, Zhang, Haoyue, Zhang, Donghui, Bu, Jiexun, Zhou, Xiaoyu, Ouyang, Zheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2023
• Subjects: Mass shift; Miniature mass spectrometers; Neural network; Miniature mass spectrometers; Neural network; Mass shift
• ISSN: 1387-3806; 1873-2798
• DOI: 10.1016/j.ijms.2023.117085

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. [Display omitted] •The complex environmental conditions on-site have a significant impact on mass accuracy.•A neural network algorithm was developed to correct the mass shift under complex on-site environments.•A test workflow was applied to simulate a real on-site analysis.