Characteristic detection method for LC-MS original data

CN122017102APending Publication Date: 2026-05-12DALIAN INSTITUTE OF CHEMICAL PHYSICS CHINESE ACADEMY OF SCIENCES
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
DALIAN INSTITUTE OF CHEMICAL PHYSICS CHINESE ACADEMY OF SCIENCES
Filing Date
2024-11-11
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Existing LC-MS data feature detection methods have a large number of false positive and false negative results, and are relatively inaccurate in quantification, which affects the accuracy of subsequent biostatistical analysis.

Method used

An image processing-based approach is employed to detect and integrate the ROI (Region of Interest) of chromatographic peak regions in raw LC-MS data, construct an ROI matrix, and perform edge detection, nonmaximum suppression, and dynamic programming algorithms to calculate the start and end points of features. Finally, peak area integration is performed to achieve accurate detection and quantification of features.

Benefits of technology

It effectively reduced false positives, improved the sensitivity and accuracy of feature detection, simplified the operation steps, and achieved precision in the retention time of chromatographic peaks between samples and the accuracy of relative quantification.

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Abstract

The invention relates to a feature detection method for LC-MS original data. According to the method, automatic feature detection is carried out on the LC-MS original data through a plurality of steps. The method comprises the following steps: firstly, carrying out chromatographic peak region detection on LC-MS original data of the same batch, retaining a chromatographic peak region detected in a sample, and constructing a two-dimensional intensity matrix about the sample and scanning time according to the chromatographic peak region; and the positions and boundaries of the LC-MS features are obtained from the two-dimensional matrix through the steps of edge detection, dynamic planning and the like in sequence, so that feature detection of the batch of LC-MS original data is realized.
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