Chemical Pattern Recognition for Traditional Chinese Medicine Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing quality evaluation methods for traditional Chinese medicines lack comprehensive and reliable systems, often relying on limited chemical components and not fully reflecting pharmacodynamics, leading to health risks and international reputation issues.
Innovation Solution
A method is developed to establish chemical pattern recognition for traditional Chinese medicine quality evaluation based on pharmacodynamics information, using spectrum-effect relationship analysis to identify characteristic chemical indexes correlated with medicinal effects, thereby creating a more accurate and reliable discriminant model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If quality evaluation is based on limited chemical components, then the evaluation process is simple, but the reliability and comprehensiveness of quality assessment deteriorates
Solution Approach 1:
The patent segments the complex chemical information into characteristic wave number points through spectrum-effect relationship analysis. By dividing the full spectrum into specific characteristic points correlated with pharmacodynamics, the method simplifies the evaluation process while maintaining comprehensive quality assessment coverage.
Solution Approach 2:
The patent extracts only the characteristic wave number points that are significantly correlated with medicinal effects from the complete chemical spectrum. This extraction process removes irrelevant information while retaining the essential quality-determining components, achieving both simplicity and reliability.
2Loss of information
If all chemical information is used for pattern recognition, then the chemical information is comprehensive, but the discriminant model becomes complicated and less accurate
Solution Approach 1:
The patent extracts only the characteristic wave number points that are significantly correlated with medicinal effects from the complete chemical spectrum. This extraction removes irrelevant information while retaining the essential quality-determining components, achieving both simplicity and reliability.
Solution Approach 2:
The patent applies different treatment to different parts of the chemical information spectrum. Specifically, it identifies and focuses on local characteristic wave number points that have significant correlation with pharmacodynamics, rather than treating all spectral data uniformly. This local quality approach optimizes the discriminant model by highlighting the most relevant features.
3Ease of manufacture
If quality evaluation does not consider pharmacodynamics, then the evaluation method is straightforward, but the clinical relevance and accuracy of quality assessment deteriorates
Solution Approach 1:
The patent performs preliminary spectrum-effect relationship analysis to pre-identify the characteristic wave number points that are significantly correlated with medicinal effects. This preliminary action establishes the pharmacodynamics-based criteria before the actual quality evaluation, ensuring that the subsequent assessment is both clinically relevant and accurate.
Solution Approach 2:
The patent incorporates pharmacodynamics information as feedback to guide the selection of characteristic chemical indexes. The spectrum-effect relationship analysis creates a feedback loop where clinical efficacy data informs the identification of relevant chemical markers, which then improve the accuracy of quality assessment while maintaining methodological simplicity.
Data Source
AI summary
A chemical pattern recognition method for evaluating the quality of a traditional Chinese medicine based on medicine effect information, comprising: collecting chemical information of a traditional Chinese medicine sample, obtaining medicine effect information reflecting a clinical therapeutic effect thereof, performing spectrum-effect relationship analysis on the chemical information and the medicine effect information, and obtaining an index significantly related to the medicine effect as a feature chemical index; dividing the traditional Chinese medicine sample into a training set and a test set; using a pattern recognition method to extract a feature variable from samples of the training set by taking the feature chemical index as an input variable; building a pattern recognition model using the feature variable; and substituting feature variable values of samples of the test set into the model, and completing chemical pattern recognition evaluation of the quality of the traditional Chinese medicine. According to the method, chemical reference substances are not needed, the chemical pattern recognition model is built on the basis of the feature chemical index reflecting the medicine effect, the one-sidedness and the subjectivity of the existing standards are overcome, and a traditional Chinese medicine quality evaluation system capable of reflecting both the clinical therapeutic effect and overall chemical composition information is finally formed.