Automated NMR Spectral Analysis via Iterative Expert Systems
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Solution Overview
Problem
Current methods for analyzing MR spectra rely on predicting an expected structure, which can lead to incorrect concentration results if the predicted structure or multiplet matching is incorrect, and do not provide reliable interpretation without prior knowledge of the molecular structure.
Innovation Solution
A method using feedback-driven expert systems with embedded chemical and MR expert knowledge to iteratively process MR spectrum data, maximizing the ratio of interpreted to non-interpreted data, allowing for probability-weighted extraction of spectral information without prior structural knowledge, and enabling human-like dynamic problem-solving strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional peak analyzer and multiplet analyzer are used to process MR spectra, then automated analysis can be performed, but the reliability of concentration results deteriorates when expected structure prediction is incorrect
Solution Approach 1:
The patent implements feedback mechanisms where the multiplet analyzer feeds back corrected multiplet lists to the peak analyzer, and the structure analyzer feeds back structure corrections to the multiplet analyzer. This iterative feedback loop allows the system to automatically correct errors in structure prediction and maintain reliable concentration results even when initial predictions are incorrect.
Solution Approach 2:
The patent performs preliminary structure prediction using the structure analyzer before the main concentration calculation. This preliminary action allows the system to identify and correct structural errors before they affect the final concentration results, ensuring reliability while maintaining automation.
2Quantity of substance
If expected structure is provided for analysis, then concentration can be quantified, but the system cannot reliably interpret spectra when no prior structural knowledge is available
Solution Approach 1:
The patent creates a universal analysis system where the structure analyzer can operate in two modes: using provided expected structures for quantification, or generating structures de novo when none are provided. The multiplet analyzer and peak analyzer work consistently in both modes, making the entire system adaptable to both structured and unstructured spectral analysis scenarios.
Solution Approach 2:
The structure analyzer performs self-service by automatically generating expected structures from spectral data when none are provided externally. This self-service capability allows the system to quantify concentration and interpret spectra independently without requiring prior structural knowledge, while still maintaining the ability to use provided structures when available.
3Measurement precision
If iterative multiplet matching is performed to improve accuracy, then concentration precision improves, but analysis time increases
Solution Approach 1:
The patent implements dynamic control of the iterative analysis process where the number of iterations is not fixed but adapts based on the quality of matches and corrections. The system automatically determines when convergence is achieved or when further iterations would not significantly improve precision, thereby optimizing the balance between concentration precision and analysis time.
Solution Approach 2:
The patent changes the parameter of iteration count dynamically based on the specific spectral data and correction needs. When structural corrections are made, the system adjusts the number and scope of subsequent iterative matching operations, allowing high precision when needed while reducing time when the data is already consistent.
Data Source
AI summary
A fully automatic, parameter free MR interpretation system is based on human logic emulation. Information is derived mainly from an MR spectrum with maximum confidence and in a similar way as a human expert. This is achieved by the combination of different expert systems that interpret certain MR spectral features as well as features from a proposed structure. The expert systems are dynamically linked to each other and the analysis is performed iteratively in all directions in a way that the expert systems can utilize all of the interpretations of all expert systems at all times. The expert system may generate not just a single result but rather lists of probability weighted hypotheses.