Mass Spectral Library Expansion Across Instrument Parameters
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Solution Overview
Problem
Existing mass spectrometry methods face challenges in optimizing instrument parameters for accurate compound identification due to the complexity of mass spectrometers and limited data granularity in spectral libraries, which can lead to inaccurate compound identification, especially in complex mixtures.
Innovation Solution
A method involving a mass spectrometer that modulates instrument parameters through a range of values, acquiring and encoding mass spectral datasets, and storing them in a spectral library, along with using machine learning algorithms to determine relationships between spectral features and instrument parameters, thereby enhancing data granularity and accuracy in compound identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optimization methods are used to optimize instrument parameters, then measurement precision of mass spectra is improved, but loss of time increases due to requiring large quantity of experiments
Solution Approach 1:
The patent applies preliminary action by pre-collecting mass spectral data across a comprehensive range of instrument parameter values and storing them in a spectral library before actual analysis. This allows the system to retrieve pre-acquired spectra matching the actual instrument conditions, eliminating the need for time-consuming experimental optimization while maintaining high measurement precision.
2Device complexity
If spectral libraries with limited instrument parameter information are used, then device complexity is reduced, but measurement precision of compound identification deteriorates
Solution Approach 1:
The patent applies dimensionality change by expanding the spectral library to include mass spectral data across multiple dimensions of instrument parameters (e.g., collision energy, ion source temperature, scan rate) rather than storing single-condition spectra. This multi-dimensional approach enables precise matching of actual instrument conditions with library data, significantly improving compound identification accuracy while maintaining manageable system complexity through efficient data organization.
3Ease of operation
If fixed instrument parameters are used for spectral library, then ease of operation is improved, but adaptability to different instrument conditions deteriorates
Solution Approach 1:
The patent applies parameter changes by organizing the spectral library to contain mass spectral data acquired under varied instrument parameter conditions for each reference compound. The system retrieves spectra corresponding to the actual instrument parameters used during analysis, enabling automatic adaptation to different instrument conditions without requiring manual reconfiguration or simplifying assumptions, thus maintaining both ease of operation and high adaptability.
Data Source
AI summary
Methods and systems for mass spectrometry are disclosed. In one example, a method comprises: receiving, by a mass spectrometer via a sampling system operably connected thereto, at least one sample containing at least one known compound; modulat-ing at least one instrument parameter of the mass spectrometer through a plurality of instrument parameter values; analyzing the at least one sample while applying each of the plurality of instrument parameter values; acquiring a plurality of mass spectral (MS) datasets each corresponding to one of the applied plurality of instrument parameter values; encoding each of the plurality of MS datasets to generate a corresponding plurality of MS results each corresponding to one of the applied instrument parameter values; and compiling and storing the MS datasets and MS results in a spectral library in association with the applied instrument parameter values.