Peak Detection Method Using Smoothed Curve Reference for Chromatogram Noise Filtering
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Solution Overview
Problem
Existing peak detection methods in chromatograms and spectra incorrectly identify noise or baseline drift as sample peaks, leading to inaccurate peak detection.
Innovation Solution
A peak detection method involving tentative peak detection, actual measurement value determination, smoothing processing, reference value calculation, and true peak identification, which uses a smoothed curve to filter out noise and drift, ensuring accurate peak detection by determining peaks within a predetermined range from the reference value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional peak detection methods are used to identify peaks in chromatograms or spectra, then peak detection can be performed quickly and simply, but noise or baseline drift may be incorrectly identified as sample peaks, leading to inaccurate detection results
Solution Approach 1:
The detection process is divided into multiple stages: first detecting tentative peaks based on simple criteria, then filtering these candidates through smoothing processing and reference value comparison to identify true peaks. This segmentation allows the method to maintain simplicity in the initial detection phase while ensuring accuracy through subsequent filtering stages.
Solution Approach 2:
The method performs preliminary smoothing processing on the chromatogram or spectrum data before peak detection, and establishes reference values in advance based on smoothed curves. This preliminary action prepares the data structure to facilitate accurate peak identification while filtering out noise and baseline drift effects.
2Measurement precision
If noise elimination and baseline removal are performed before peak detection, then measurement accuracy is improved, but the processing time and computational complexity increase
Solution Approach 1:
The invention extracts only the essential smoothing operation needed to establish reference values, rather than performing complete noise elimination and baseline removal. By taking out only the critical smoothing step and using the resulting curve to generate reference values for peak validation, the method achieves improved accuracy without the time cost of comprehensive preprocessing.
Solution Approach 2:
The method applies partial smoothing processing - enough to establish reliable reference values for peak validation, but not excessive processing that would completely remove all signal features. This partial action achieves the necessary accuracy improvement while minimizing additional processing time.
Data Source
AI summary
A method for detecting a peak in data of a chromatogram or a spectrum, includes: detecting multiple tentative peaks in the data on the basis of a predetermined criterion; determining an actual measurement value of a predetermined feature value indicating a size of a tentative peak from each of the detected multiple tentative peaks, the feature value; determining a smoothed curve on the basis of respective horizontal axis values and actual measurement values of the multiple tentative peaks; determining a reference value of the feature value with respect to each of the multiple tentative peaks from the smoothed curve; and detecting, of the multiple tentative peaks, a tentative peak whose actual measurement value is within a predetermined range from the corresponding reference value as a true peak. Only tentative peaks whose actual measurement value is within a predetermined range from the corresponding reference value as a true peak.