Nucleic Acid Mass Spectrum Processing for Reliable Peak Extraction
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Solution Overview
Problem
Current nucleic acid mass spectrum data acquisition methods suffer from low conversion rates and uneven data, which negatively impact the accuracy of nucleotide detection and subsequent gene analysis.
Innovation Solution
A numerical processing method for nucleic acid mass spectra involving recalibration, peak filtering, and wavelet filtering to extract reliable feature values, including steps like selecting anchor peaks, applying weight matrix convolution filters, fitting peaks with Gaussian functions, and synthesizing mass spectra using self-weighted averages, to improve data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional numerical processing methods are used for mass spectrum data, then the processing speed is maintained, but the data quality is uneven and conversion rate is low
Solution Approach 1:
The patent segments the mass spectrum data processing into multiple distinct stages: initial filtering to remove noise, peak detection to identify significant features, peak fitting to extract precise parameters, and recalibration to correct systematic errors. Each stage processes specific aspects of the data independently, allowing optimization of each step without compromising overall conversion rate.
Solution Approach 2:
The patent applies preliminary filtering and preprocessing steps before main data analysis. By pre-identifying and removing noise components, pre-detecting peak positions, and pre-calibrating mass axes, the method prepares data in advance for more accurate and efficient subsequent processing, improving both quality and conversion rate.
2Reliability
If advanced filtering and processing techniques are applied, then data reliability improves, but processing complexity increases
Solution Approach 1:
The patent introduces intermediary processing steps between raw data acquisition and final analysis. Weight matrix convolution acts as an intermediary filtering mechanism, Gaussian fitting serves as an intermediary for peak parameter extraction, and recalibration functions as an intermediary for error correction. These intermediaries simplify complex processing by breaking it into manageable, standardized operations.
Solution Approach 2:
The patent systematically changes key processing parameters including filter widths, convolution matrix dimensions, fitting function parameters, and calibration coefficients. By optimizing these parameters for different data types and quality levels, the method achieves high reliability without requiring fundamentally complex processing architectures.
Data Source
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AI summary
A nucleic acid mass spectrum numerical processing method, comprising the following steps: step S1: re-calibrating a single mass spectrum graph; for each detection point of a sample, obtaining several mass spectrum graphs of different positions corresponding to the detection point, wherein each mass spectrum graph needs to be re-calibrated by using a special group of peaks, i.e. anchor peaks, that have an expected mass-to-charge ratio; step S2: synthesizing the mass spectrum graphs, and on the basis of step S1, synthesizing the several mass spectrum graphs of different positions corresponding to the detection point into a single mass spectrum graph of the detection point; step S3: performing wavelet filtering, and on the basis of step S2, eliminating high-frequency noise and a baseline by means of a wavelet-based digital filter; and step S4: extracting a peak characteristic value, and on the basis of step S3, performing peak fitting, and obtaining the peak height, peak width, peak area, mass offset, and signal-to-noise ratio on the basis of a fitting curve of the mass spectrum graph. According to the method, the reliability of nucleic acid mass spectrum data acquisition is improved, and the accuracy of nucleotide detection is improved.