Mass Spectrum Data Analysis for Resolution Enhancement
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Solution Overview
Problem
Enhancing the resolution of mass spectrometers is costly and inefficient when relying solely on hardware and software improvements, necessitating a more efficient and cost-effective method.
Innovation Solution
A method involving data analysis techniques, including training a resolution factor model, identifying similar standard compounds, optimizing baseline reference regions, separating and clustering peaks, and performing dimensionality reduction to enhance mass spectrometer resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware and software improvements are used to enhance mass spectrometer resolution, then resolution is improved, but cost increases and efficiency decreases
Solution Approach 1:
The patent replaces hardware-based resolution enhancement mechanisms with a data processing system that uses machine learning models and signal processing algorithms. The resolution factor model and denoising algorithms process mass spectrum data computationally to achieve resolution enhancement without modifying the physical mass spectrometer hardware, thereby maintaining high efficiency while improving resolution.
Solution Approach 2:
The patent changes the approach from modifying physical parameters of the mass spectrometer to transforming data parameters through computational methods. By applying dimensionality reduction, denoising, and baseline correction to the mass spectrum data, the system achieves resolution enhancement by optimizing data representation rather than physical instrument parameters.
2Measurement precision
If hardware and software improvements are used to enhance mass spectrometer resolution, then resolution is improved, but cost increases
Solution Approach 1:
The patent substitutes expensive hardware modifications with a software-based solution that uses existing mass spectrometer data. The resolution enhancement is achieved through computational algorithms including the resolution factor model, denoising processing, and baseline correction, eliminating the need for costly hardware upgrades while maintaining resolution improvement.
Solution Approach 2:
The patent creates a computational model (resolution factor model) that replicates the effect of hardware-based resolution enhancement through data processing. By training this model on mass spectrum data and using it to enhance resolution, the system produces results similar to hardware improvements without the associated costs.
3Productivity
If data analysis methods are used to enhance resolution, then cost decreases and efficiency increases, but measurement precision may be affected
Solution Approach 1:
The patent implements feedback mechanisms through the resolution factor model that learns from mass spectrum data and adjusts its processing parameters accordingly. The model uses training data to optimize its denoising and resolution enhancement parameters, ensuring that the data analysis methods maintain high measurement precision while achieving efficient resolution enhancement.
Solution Approach 2:
The patent performs preliminary data processing steps including denoising, baseline correction, and dimensionality reduction before final resolution enhancement. These preliminary actions prepare the data by removing artifacts and optimizing its structure, ensuring that subsequent resolution enhancement maintains high measurement precision while being computationally efficient.
Data Source
AI summary
A method and a system for mass spectrometer resolution enhancement based on data analysis are provided. The method includes: training a resolution factor model; determining basic information of a to-be-detected compound according to an original mass spectrum and an original fragment spectrum, determining a target signal-to-noise ratio of the original mass spectrum according to a mass spectrum of the standard compound, and performing denoising processing on the original mass spectrum through a noise reduction means; determining a baseline reference region according to the denoised original mass spectrum and the mass spectrum of the standard compound; identifying characteristics of peaks in the mass spectrum, separating overlapped peaks through the peak characteristics, and performing a cluster analysis on all the peaks according to the resolution factor; and performing dimensionality reduction processing on a category of peaks with the data dimensionality exceeding a dimensionality threshold.