Learning Model for Overlapping Spectral Peak Separation
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Solution Overview
Problem
Existing spectral analysis methods, such as HPLC, face challenges in accurately determining the concentration and amount of test substances in samples containing foreign substances, especially when peak overlapping occurs, leading to decreased estimated accuracy or inability to calculate quantitative information.
Innovation Solution
An information processing apparatus and method that utilize a learning model to estimate quantitative information by selecting and combining multiple pieces of spectral information from samples containing test and foreign substances, allowing for distinction between overlapping peaks through spectral information at different wavelengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HPLC with preprocessing and peak separation methods is used, then quantitative information can be obtained from spectral information, but the analysis becomes complex and requires skilled operators for preprocessing and peak separation when foreign substances are present
Solution Approach 1:
The system automatically performs peak separation and quantitative analysis using a learning model trained on spectral data, eliminating the need for manual preprocessing by skilled operators. The learning model self-adapts to separate overlapping peaks and calculate quantitative information without human intervention.
Solution Approach 2:
Manual peak separation methods (mechanical/arithmetic processing) are replaced with an automated learning model based on spectral information. The learning model uses machine learning algorithms to automatically distinguish and separate overlapping peaks from test substances and foreign substances, replacing complex manual arithmetic processing.
2Measurement precision
If peak separation methods (base line, vertical separation, fitting) are used, then quantitative information can be calculated, but accuracy decreases when foreign substances have overlapping peaks with test substances
Solution Approach 1:
The system changes the approach from traditional peak separation parameters (base line, vertical separation) to using multiple spectral parameters across different wavelengths. The learning model analyzes spectral information at multiple wavelengths simultaneously, allowing it to distinguish overlapping peaks by their unique spectral signatures rather than relying on single-parameter separation methods.
Solution Approach 2:
The system transitions from two-dimensional peak separation (time vs. intensity) to multi-dimensional spectral analysis by incorporating spectral information at multiple wavelengths. This additional dimensional information allows the learning model to differentiate between overlapping peaks of test substances and foreign substances that would be indistinguishable in traditional single-wavelength analysis.
3Measurement precision
If manual preprocessing and peak separation are required, then accurate quantitative information can be obtained, but the ease of operation decreases and beginners cannot perform analysis easily
Solution Approach 1:
The system performs all preprocessing and peak separation operations automatically through the learning model, making the analysis process self-sufficient. Beginners can simply input spectral data and receive accurate quantitative results without needing to learn complex preprocessing techniques or understand peak separation methodologies.
Solution Approach 2:
The learning model is designed to handle multiple types of samples (biological samples, environmental samples, food samples) and various foreign substances universally. This multi-functional capability allows beginners to analyze different sample types without requiring sample-specific preprocessing knowledge, making the system easy to operate across diverse applications.
Data Source
AI summary
An apparatus includes an acquisition unit configured to acquire quantitative information on a test substance, the quantitative information being estimated by inputting, to a learning model, two or more pieces of spectral information selected from a plurality of pieces of spectral information on a sample containing the test substance and a foreign substance.