AI Mass Spectrometry Tuning for Data Variability
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Solution Overview
Problem
Mass spectrometry (MS) instruments are challenging to tune due to numerous adjustable variables and interdependencies, leading to high subjectivity and variability in data results, making it difficult for non-experts to achieve consistent results across different operators, labs, and machines.
Innovation Solution
An AI-configured tuning system that searches for optimal parameter values to reduce data variability by analyzing reference materials and updating its model based on results, ensuring reproducibility and stability across different conditions and instruments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning by experts is used to adjust MS instrument parameters, then measurement precision and reliability can be improved, but device complexity and difficulty of operation increase significantly
Solution Approach 1:
The system enables self-service tuning by allowing the MS instrument to automatically adjust its own parameters through AI-driven optimization. The tuning system analyzes instrument responses and autonomously selects optimal parameter values without requiring expert manual intervention, thus reducing data variability while eliminating the complexity burden on operators.
Solution Approach 2:
The patent replaces the manual mechanical adjustment process with an automated computational system. Instead of experts physically adjusting numerous parameters based on experience, an AI algorithm systematically evaluates parameter combinations and determines optimal settings, substituting human expertise with algorithmic optimization to reduce data variability.
2Measurement precision
If expert tuning is performed to optimize instrument performance, then measurement precision improves, but ease of operation deteriorates due to specialized knowledge requirements
Solution Approach 1:
The system empowers non-experts by enabling the instrument to perform self-tuning. The automated system handles the complex task of parameter optimization, allowing any operator to achieve expert-level reproducibility without requiring specialized knowledge. The instrument serves itself by automatically adjusting parameters based on real-time performance feedback.
Solution Approach 2:
The tuning system implements continuous feedback loops where instrument performance is monitored and used to guide parameter adjustments. The AI algorithm analyzes measurement outcomes and iteratively refines parameter settings, creating a self-correcting system that automatically maintains optimal performance and reproducibility without human expertise.
3Measurement precision
If multiple parameter adjustments are made to improve measurement precision, then data quality improves, but the time required for tuning increases
Solution Approach 1:
The system performs preliminary actions by pre-evaluating parameter combinations using AI algorithms before actual measurements begin. The tuning system proactively determines optimal parameter settings in advance, avoiding time-consuming trial-and-adjustment cycles during operation. This preliminary optimization reduces both tuning time and data variability.
Solution Approach 2:
The patent replaces time-consuming manual parameter adjustment with rapid computational optimization. The AI system evaluates numerous parameter combinations virtually and instantaneously, identifying optimal settings without the iterative physical adjustments that would consume significant time. This computational substitution dramatically reduces tuning time while improving measurement precision.
Data Source
AI summary
Exemplary embodiments provide methods, mediums, and systems for automatically tuning a mass spectrometry (MS) apparatus. The MS apparatus may include a number of parts, each of which may be associated with adjustable parameters that affect a performance of the part. An artificial intelligence may determine values for the parameters that are predicted to reduce data variability when performing an experiment with the MS apparatus. By reducing data variability, experiments run with the MS apparatus are more likely to be repeatable on different devices, in different labs, by different operators, and at different times.