Learning Model Spectral Analysis with Reliability Assessment
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Solution Overview
Problem
Conventional spectral analysis methods require expertise and complex pretreatment processes to separate test substances from impurities, making it difficult for beginners to accurately analyze samples, especially in biological and environmental samples with high impurity levels.
Innovation Solution
An information processing apparatus that uses a learning model to estimate quantitative information of test substances from spectral data, including a reliability assessment to determine the accuracy of the results, allowing for easy and accurate analysis without extensive pretreatment or expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spectral analysis methods are used with separation and pretreatment processes, then measurement precision can be improved, but device complexity and ease of operation deteriorate due to requiring expert knowledge and complex procedures
Solution Approach 1:
A learning model serves as an intermediary between the spectral data and the quantitative analysis. The learning model automatically processes the spectral information and provides quantitative results without requiring manual separation or complex pretreatment procedures, thus improving ease of operation while maintaining measurement precision
Solution Approach 2:
The patent replaces manual mechanical separation processes and expert-based arithmetic processing with an automated learning model. The learning model substitutes for the need for manual peak splitting and separation optimization, enabling beginners to achieve accurate quantitative analysis without complex operations
2Measurement precision
If conventional spectral analysis methods with separation and arithmetic processing are used, then measurement precision can be improved, but device complexity worsens due to multiple processing steps
Solution Approach 1:
The patent merges multiple separate processing steps (separation, pretreatment, peak splitting, arithmetic processing) into a single integrated learning model. This unified approach maintains measurement precision while significantly reducing process complexity by eliminating the need for multiple sequential operations
Solution Approach 2:
The learning model performs multiple functions simultaneously: it processes spectral data, identifies peaks, separates components, and provides quantitative analysis all within one system. This multi-functionality reduces device complexity by replacing multiple specialized processes with a single versatile tool
3Ease of operation
If learning model-based quantitative information estimation is used without pretreatment, then ease of operation improves, but reliability worsens due to potential inaccuracy in complex samples
Solution Approach 1:
The patent incorporates a reliability assessment mechanism that provides feedback on the accuracy of quantitative information estimation. This feedback loop allows the system to evaluate its own performance and indicate when results are reliable, thereby maintaining ease of operation while improving trust in the results through transparent reliability assessment
Data Source
AI summary
An information processing apparatus assists a user in determining quantitative information of a test substance estimated by using a learning model. The information processing apparatus has an information acquisition means and a reliability acquisition means. The information acquisition means acquires the quantitative information of the test substance estimated by inputting spectral information of a sample including the test substance and impurities into the learning model. The reliability acquisition means acquires reliability of the acquired quantitative information of the test substance.