Chromatogram Data Processing Orthogonal Vector Impurity Detection
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Solution Overview
Problem
Conventional methods for determining the presence of impurities superposed on target peaks in chromatograms are unreliable, particularly when the impurity cannot be detected due to overlapping spectra or low slope absorption spectra, leading to inaccurate quantitative analysis.
Innovation Solution
A chromatogram data processing system that calculates an auxiliary vector orthogonal to the target component's spectrum, using it as a filter to determine the presence of impurities by calculating the inner product of the process-target spectrum, which helps in identifying impurities even in noisy data and overlapping cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional impurity determination methods are used, then the analysis can be performed with simple computations, but impurities cannot be detected when absorption spectra overlap or have low slope
Solution Approach 1:
The patent transforms the impurity detection problem from conventional wavelength-domain analysis to a vector space representation. By expressing absorption spectra as vectors and using orthogonal vectors as filters, the method adds a dimensional transformation that enables detection of impurities with overlapping or low-slope spectra while maintaining computational feasibility through standardized vector operations.
Solution Approach 2:
The patent changes the parameter representation from raw absorption spectrum values to orthogonal vector projections. By calculating the inner product of the target spectrum with orthogonal filter vectors, the method transforms the detection parameters into a form that maximizes signal-to-noise ratio and enables reliable impurity detection even when conventional spectral parameters fail.
2Measurement precision
If the absorption spectrum of impurity has low slope or overlaps with target component, then conventional methods fail to detect impurity, but the patent's orthogonal vector method can still identify it
Solution Approach 1:
The patent extracts impurity information by projecting the target spectrum onto orthogonal vectors that are perpendicular to the target component's spectrum. This extraction method isolates the impurity signal from the overlapping target spectrum, enabling detection even when the impurity's absorption spectrum has low slope or completely overlaps with the target component in the conventional wavelength domain.
3Productivity
If quantitative analysis is performed without impurity determination, then the analysis process is faster, but the quantitative results become inaccurate due to undetected impurities
Solution Approach 1:
The patent performs impurity determination as a preliminary action before quantitative analysis by first calculating orthogonal filter vectors from the target spectrum and then using these filters to detect impurities. This preliminary screening ensures that only samples without significant impurities proceed to quantitative analysis, maintaining both speed and accuracy by avoiding unnecessary reanalysis.
Data Source
AI summary
For vector A which expresses an absorption spectrum of a target component, vector F orthogonal to vector A is designated as a filter for extracting an impurity superposed on the target component on a chromatogram. For vector I which expresses a measured spectrum obtained by a chromatographic analysis performed on a sample, the inner product of vectors I and F is defined as an index value u of the amount of impurity. If an impurity is present, a peak-like waveform appears on a graph which shows a temporal change in the index value u for the measured spectrum obtained at each point in time of the measurement. By detecting this waveform, the presence or absence of the impurity can be correctly determined. The direction of vector F may be determined so that, when vector B which expresses a spectrum of the impurity is decomposed into vector Ba parallel to vector A and vector Bo orthogonal to vector A, vector F becomes nearly parallel to vector Bo (i.e. the cosine similarity index is maximized).