Spring Model for Peak Pattern Deviation Analysis
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Solution Overview
Problem
Existing methods for evaluating peak patterns struggle to accurately compare measured peak patterns with reference patterns, especially when acquired with different measurement setups or when dealing with varying peak parameters, leading to mismatches and difficulties in determining sample degradation.
Innovation Solution
Representing peak patterns using a spring model, where each peak parameter is represented by a spring, allowing for the calculation of deformation energy to measure the deviation between measured and reference patterns, enabling effective comparison and identification of mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional peak pattern comparison methods are used, then the evaluation process is simple, but the accuracy of matching measured patterns with reference patterns deteriorates when patterns are acquired with different measurement setups
Solution Approach 1:
The patent transforms peak pattern parameters (position, height, area) into spring model parameters (equilibrium positions, spring constants). This parameter transformation enables the system to handle variations from different measurement setups by modeling deviations as spring deformations rather than direct parameter comparisons, thereby improving matching accuracy while maintaining manageable complexity through the standardized spring model framework
Solution Approach 2:
The spring model acts as an intermediary between the measured peak pattern and the reference peak pattern. Instead of directly comparing peak parameters which may differ due to measurement setup variations, the patent uses spring deformations as a mediator to quantify deviations. This intermediary model provides a unified framework for comparing patterns across different measurement conditions, resolving the contradiction between accuracy and complexity
2Reliability
If detailed peak parameters are compared to detect sample degradation, then the detection sensitivity improves, but the difficulty of determining mismatches increases due to multiple varying parameters
Solution Approach 1:
The patent segments the complex task of peak pattern comparison into individual spring components, each representing a specific peak parameter. By dividing the reference peak pattern into multiple independent springs (one for each peak), the system can evaluate deviations separately for each peak and then aggregate them through deformation energy calculation. This segmentation reduces the complexity of analyzing multiple varying parameters while maintaining high detection sensitivity for sample degradation
Solution Approach 2:
The patent merges multiple individual peak parameter deviations into a single unified metric: total deformation energy. Each spring's deformation energy (based on its spring constant and displacement) is calculated separately to maintain sensitivity to individual peak variations, then these energies are summed to provide an overall degradation measure. This combining approach simplifies mismatch detection while preserving the reliability gained from detailed parameter analysis
Data Source
AI summary
Determining a property of a measured peak pattern having at least one peak includes extracting measured peak parameters from the measured peak pattern, and determining deviations of the measured peak parameters from reference values of a reference peak pattern. The reference peak pattern is represented by a spring model, with peak parameters being represented by corresponding springs. Determining a property of a measured peak pattern also includes determining a deformation energy necessary for deforming the reference peak pattern in a way that the deformed reference peak pattern substantially represents the measured peak pattern.