Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization.

Area of Science:

• Analytical Chemistry
• Instrumental Analysis
• Computational Science

Background:

• Quadrupole mass spectrometers (QMS) are vital for clinical diagnosis and chemical analysis.
• Current QMS tuning methods are manual, labor-intensive, and require expert operators, limiting efficiency and reproducibility.
• Automating the QMS tuning process is crucial for improving accessibility and performance.

Purpose of the Study:

• To develop an innovative auto-tuning algorithm for QMS.
• To enhance the automation level and reduce the complexity of QMS calibration.
• To achieve optimal performance of QMS through automated parameter optimization.

Main Methods:

• An improved particle swarm optimization (PSO) algorithm was developed for QMS auto-tuning.
• The PSO algorithm integrated simulated annealing, multiple inertia weights, and dynamic boundaries to avoid local optima and premature convergence.
• A novel termination function based on mass spectrum peak characteristics was introduced to simplify algorithm termination.

Main Results:

• Auto-calibration testing demonstrated that resolution and mass axis calibration met experimental requirements.
• Auto-optimization of lens and ion source parameters resulted in values near the optimal solution, achieving expected performance.
• Experimental results confirmed the algorithm's reproducibility and effectiveness in auto-tuning QMS.

Conclusions:

• The proposed auto-tuning algorithm successfully optimizes QMS parameters, reducing tuning difficulty.
• This automated approach significantly improves the efficiency and accessibility of QMS calibration.
• The method ensures QMS instruments reach optimal states, meeting demanding analytical requirements.