Chromatogram Peak Separation Using Hybrid Model-Free Matrix Decomposition
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Solution Overview
Problem
Existing peak separation algorithms struggle to accurately quantify components with low concentrations when peaks of components with extremely large relative concentration ratios overlap on a chromatogram, due to restrictions in peak model functions leading to deviations in peak area values.
Innovation Solution
A data processing method and system that combines a peak model-using algorithm with a model-free algorithm, where adjustment target peaks are initially estimated using a peak model function and then fine-adjusted using matrix decomposition to improve the approximation of peak shapes and sizes, allowing for high accuracy in separating overlapping peaks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a peak model function is applied to separate overlapping peaks, then the peak shape can be reproduced with high accuracy, but the peak area value of low concentration components deviates significantly from the actual value
Solution Approach 1:
The patent combines two algorithms: a model-using algorithm (which applies peak model functions to reproduce peak shapes) and a model-free algorithm (which performs matrix decomposition without shape restrictions). By merging these algorithms, the system achieves both accurate peak shape reproduction and accurate peak area quantification, even for low concentration components in overlapping peaks
Solution Approach 2:
The patent changes the approach by introducing a hybrid algorithm that adjusts parameters dynamically - using the model-using algorithm when peak shape accuracy is critical and incorporating model-free matrix decomposition when quantitative accuracy is prioritized, particularly for low concentration components
2Adaptability or versatility
If a model-free algorithm using matrix decomposition is used to separate peaks, then the degree of freedom of separation is high and peak shapes are not restricted, but the reproducibility of separation results deteriorates
Solution Approach 1:
The patent merges the model-free algorithm (which provides high flexibility through matrix decomposition) with the model-using algorithm (which provides reliability through consistent peak model application). This combination allows the system to maintain high reproducibility while preserving the adaptability benefits of the model-free approach
3Shape
If peaks of components with extremely large relative concentration ratios are separated using a model-using algorithm, then the peak shape is restricted by the model function, but the peak area value of low concentration components cannot be accurately obtained
Solution Approach 1:
The patent applies local quality by treating different components differently in the separation process. For high concentration components, the peak model function provides accurate shape reproduction. For low concentration components, the hybrid algorithm incorporates model-free matrix decomposition to accurately determine peak area values without being constrained by model function restrictions
Solution Approach 2:
The patent combines the model-using algorithm and model-free algorithm to handle components with different concentration levels appropriately, achieving accurate quantification across the full concentration range
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 enables accurate quantification of components with low concentrations by improving the degree of approximation of estimation data to actual data, even when peaks of components with large relative concentration ratios overlap, enhancing separation accuracy.
Implementation Method 1
a photodiode array (PDA) detector, three-dimensional chromatogram data having three dimensions of time, wavelength, and signal intensity (absorbance) can be obtained by continuously acquiring an absorption spectrum of a sample eluted from an analysis column
Data Source
AI summary
A data processing method for separating peaks of a plurality of components overlapping on a chromatogram from each other using actual data of a three-dimensional chromatogram including a chromatogram and a spectrum acquired by chromatographic analysis on a sample. The data processing method includes an adjustment target peak acquisition step of obtaining a plurality of adjustment target peaks by applying, to the chromatogram, a peak model function prepared in advance to approximate a waveform of the chromatogram, and an adjustment target peak adjustment step of setting a plurality of the adjustment target peaks obtained in the adjustment target peak acquisition step as initial values before adjustment, and repeating adjustment of the adjustment target peaks until pseudo data of a three-dimensional chromatogram on the sample obtained by combining the adjustment target peaks after adjustment is similar to the actual data.