Mass Spectrometer Reinforcement Learning Control for Adaptive Acquisition
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Solution Overview
Problem
Existing mass spectrometry (MS) technologies face challenges in efficiently determining the maximum number of compounds within a given time, as they rely on pre-programmed acquisition metrics and operation settings that fail to adapt to changing experimental conditions.
Innovation Solution
The implementation of a reinforcement learning (RL)-based system that employs a neural network (NN) to automatically determine acquisition metrics and operation settings for MS experiments, allowing for adaptive control and optimized compound identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-programmed acquisition metrics and operation settings are used in mass spectrometry, then the system is simple to operate, but it cannot adapt to changing experimental conditions and fails to optimize compound identification efficiency
Solution Approach 1:
The patent implements dynamic control of mass spectrometry experiments through a reinforcement learning agent that continuously adapts acquisition metrics and operation settings based on real-time experimental conditions. The system transitions from static pre-programmed parameters to dynamic, condition-responsive control, allowing the instrument to optimize its behavior during the experiment rather than following fixed protocols.
Solution Approach 2:
The reinforcement learning agent enables the mass spectrometry system to self-optimize its acquisition parameters without external intervention. The agent learns from experimental outcomes and automatically adjusts settings to maximize compound identification, making the system self-directed and autonomous in its decision-making process.
2Productivity
If reinforcement learning-based adaptive control is implemented, then compound identification efficiency is optimized, but the device complexity increases due to neural network integration
Solution Approach 1:
The patent replaces traditional mechanical control systems with software-based reinforcement learning algorithms. Instead of physically adjusting parameters through complex mechanical interfaces, the system uses neural network computations to determine optimal acquisition settings, substituting computational intelligence for mechanical complexity.
Solution Approach 2:
The reinforcement learning agent serves as an intermediary layer between the user and the complex mass spectrometry instrument. Rather than requiring users to directly manage complex parameters, the agent translates high-level experimental goals into specific acquisition settings, mediating between simple user intent and complex instrument operation.
3Ease of operation
If pre-programmed acquisition metrics are used, then the device is easy to operate, but resources are not optimally allocated during acquisitions
Solution Approach 1:
The system implements feedback loops where the reinforcement learning agent continuously monitors experimental outcomes and uses this information to adjust acquisition parameters. The agent receives feedback on which compounds are successfully identified and which resources are consumed, then uses this feedback to optimize future acquisition decisions, creating a closed-loop control system that improves resource efficiency while maintaining ease of operation.
Data Source
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AI summary
Embodiments herein relate to neural network control of mass spectrometry processes. A system can comprise a memory that stores, and a processor that executes, computer executable components. The computer executable components can comprise an acquisition component that acquires data for a compound, the data defining a first mass spectrometry spectrum for the compound, an evaluation component that, based on the data, and employing a neural network that is trained on an input dataset comprising an acquisition metric, and employing an associated score that is associated with the acquisition metric, generates a recommendation to perform a mass spectrometry action for the compound, and an execution component that, based on the recommendation, directs execution of the mass spectrometry action at a mass spectrometer and obtaining a mass spectrum result.