Chromatography Mass Spectrometry Data Processing Device
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Solution Overview
Problem
Current chromatography/mass spectrometry techniques face inefficiencies and inaccuracies in analyzing samples with interfering components, leading to increased operator effort and time, especially in detecting residual agrochemicals, due to peak overlap and incorrect classification of target components.
Innovation Solution
A chromatography/mass spectrometry data processing device that stores retention times, standard mass spectra, and characteristic mass/charge ratios for each target component, classifies peaks, scales peak intensities, and evaluates peak intensity thresholds to accurately determine the presence of target components, reducing the need for unnecessary operator confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mass chromatogram analysis is used to detect target components, then quantitative analysis can be performed, but peak overlap from interfering components causes incorrect retention time recognition and false positives
Solution Approach 1:
The invention transitions from two-dimensional analysis (retention time vs. intensity in mass chromatogram) to three-dimensional analysis by incorporating mass spectral data (multiple m/z ratios) as an additional dimension. This allows differentiation of co-eluting components through their unique mass spectral fingerprints, resolving peak overlap issues while maintaining quantitative accuracy.
Solution Approach 2:
The invention introduces mass spectral confirmation ion ratios as an intermediary verification step between raw chromatogram data and final component identification. By calculating and comparing confirmation ion ratios against reference values, the system mediates the decision-making process to distinguish true target components from interfering substances, improving identification reliability.
2Reliability
If visual confirmation of each mass chromatogram and mass spectrum is performed to ensure analysis reliability, then identification accuracy improves, but operator effort and time consumption increase significantly
Solution Approach 1:
The system implements automated self-verification by programmatically calculating confirmation ion ratios and comparing them against pre-stored reference ranges. This self-service mechanism replaces manual visual confirmation, maintaining high identification accuracy while dramatically reducing operator time investment. The system serves itself by automatically flagging only those components requiring human review.
Solution Approach 2:
The invention establishes a feedback loop where confirmation ion ratio calculations automatically feed back into the identification decision process. When ratios fall within acceptable ranges, the system confidently identifies components without human intervention. When ratios deviate, the system provides feedback to operators for targeted review, creating an efficient automated verification workflow.
3Productivity
If threshold-based filtering is applied to narrow down components requiring confirmation, then operator workload reduces, but false positives and negatives increase due to peak overlap interference
Solution Approach 1:
The system performs preliminary confirmation ion ratio calculations and comparisons before final component classification. By pre-evaluating mass spectral data and comparing confirmation ratios against reference ranges, the system prepares accurate classification information in advance, enabling threshold-based filtering to work effectively without generating false positives or negatives from peak overlap interference.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate selection of components requiring visual confirmation, reducing operator burden and increasing analysis efficiency by minimizing false positives and negatives, and ensuring precise classification of target components.
Implementation Method 1
various components contained in a sample are separated by the chromatograph in the time direction
Implementation Method 2
ions originating from each of the separated components are detected
Implementation Method 3
a mass chromatogram at a mass/charge ratio m/z corresponding to the target component
Data Source
AI summary
Peaks are detected on a mass chromatogram at multiple m/z ratios characterizing a target component, and the detected peaks are classified into groups according to their occurrence time. The measured mass spectrum is acquired for each group, the measured mass spectrum and standard mass spectrum of the target component are matched for each m/z, and the standard mass spectrum is normalized by multiplying it by the same scale factor for all the m/z ratios such that it does not exceed the peak intensities on the measured mass spectrum. The quantitation ion m/z peak intensity on the normalized standard mass spectrum is then examined, and if this intensity exceeds a preset threshold and the confirmation ion ratio determined based on the measured mass spectrum obtained for the target component is outside a reference range, then that target component is taken as a narrowed result candidate.