Machine Learning Spectral Analysis for Impurity Separation
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Solution Overview
Problem
Current methods for spectrum analysis, such as HPLC, require preprocessing and peak splitting techniques to separate test substances from impurities, which can be complex and require skilled operators, especially when dealing with samples like urine or pesticide residues.
Innovation Solution
An information processing apparatus using machine learning, specifically deep learning models, to analyze spectral information and estimate quantitative information about test substances without the need for preprocessing or peak splitting, allowing for accurate analysis even in the presence of impurities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If preprocessing and peak splitting methods are used to separate test substances from impurities, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The invention extracts and removes impurity components from the spectral information through machine learning processing, separating them from the test substance signals. The system identifies and eliminates impurity peaks automatically, allowing direct quantification without manual preprocessing or peak splitting operations.
Solution Approach 2:
The invention introduces machine learning models as an intermediary between raw spectral data and final quantification results. These models automatically process the complex separation and analysis tasks, acting as a mediator that transforms raw spectra into accurate concentration measurements without requiring manual intervention.
2Measurement precision
If preprocessing and peak splitting methods are used to separate test substances from impurities, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The system performs self-service by automatically handling impurity removal and peak separation through embedded machine learning algorithms. The processing is autonomous and does not require operator intervention or expertise in peak splitting techniques, making the system easy to operate while maintaining high precision.
Solution Approach 2:
The invention replaces manual mechanical operations (peak splitting, baseline correction) with automated machine learning processing. This substitution eliminates the need for operator skill in traditional peak analysis techniques while achieving superior or comparable measurement precision.
3Ease of operation
If machine learning models are used for direct analysis without preprocessing, then ease of operation is improved, but measurement precision may worsen
Solution Approach 1:
The machine learning models are trained in advance on extensive datasets containing various spectral patterns, impurity profiles, and test substance concentrations. This preliminary training equips the models with the knowledge to accurately process complex spectra during actual operation, ensuring high precision without requiring manual preprocessing.
Solution Approach 2:
The invention transforms the analysis approach by changing from manual parameter adjustment (peak splitting parameters, baseline settings) to automated machine learning parameter optimization. The models automatically adapt to different spectral conditions and impurity types, maintaining precision across diverse samples while simplifying operation.
Data Source
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AI summary
Hitherto, preprocessing for separating impurities and calculation processing, for example, a peak splitting method, are required in order to acquire information on test substances from spectral information. An information processing apparatus according to the present invention includes information acquisition means for acquiring quantitative information on a test substance estimated by inputting spectral information on a sample containing the test substance and impurities into a learning model.