TCM Quality Grade Detection via PCA and Logistic Regression
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Solution Overview
Problem
Current methods for evaluating the quality grade of traditional Chinese medicine (TCM) are inadequate due to the complex and variable nature of TCM production, which makes it difficult to establish a reliable and comprehensive evaluation system that correlates bioactivity and chemical components with quality grades.
Innovation Solution
A detection method combining Principal Component Analysis (PCA) and binary logistic regression to establish mathematical expressions for predicting the quality grades of TCM based on bioactivity and component interactions, using ultra-performance liquid chromatography and in vitro bioactivity assays to determine the grades of Chinese medicinal materials or herbal slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single and parallel evaluation methods are used for TCM quality assessment, then the evaluation process is simple, but the method cannot comprehensively evaluate the grades of TCM due to non-linear correlation between indexes and grades
Solution Approach 1:
The patent combines multiple evaluation methods (sensory evaluation, chemical analysis, and bioactivity assessment) into an integrated comprehensive evaluation system. This merging allows the system to capture the complex non-linear relationships between multiple indexes and quality grades, resolving the contradiction between method simplicity and evaluation accuracy.
Solution Approach 2:
The patent transforms multiple evaluation indexes (sensory characteristics, chemical component contents, bioactivity values) into a unified grading scale through parameter transformation. This allows diverse parameters with different units and scales to be integrated and correlated with quality grades, improving measurement precision while maintaining systematic evaluation.
2Reliability
If multiple evaluation indexes (bioactivity, chemical components, composition proportion) are used to assess TCM quality, then the evaluation comprehensiveness is improved, but the system complexity and data processing difficulty increase
Solution Approach 1:
The patent segments the complex evaluation system into three distinct but coordinated modules: sensory evaluation module, chemical component analysis module, and bioactivity assessment module. Each module independently evaluates specific aspects and generates standardized outputs, which are then integrated. This segmentation reduces system complexity while maintaining comprehensive and reliable evaluation.
3Measurement precision
If comprehensive data collection from multiple sources is performed to establish prediction models, then the model accuracy is improved, but the data processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary data processing and standardization during the data collection phase. Evaluation indexes are pre-processed, normalized, and transformed into a unified format before model building. This preliminary action reduces the computational burden during actual prediction, decreasing data processing time while maintaining high prediction accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a comprehensive and objective evaluation of TCM quality grades, enabling precise scoring and accounting for multiple influencing factors, thus supporting better quality control and pricing during TCM circulation and ensuring clinical safety and efficacy.
Implementation Method 1
using ultra-performance liquid chromatography to determine a fingerprint of the Chinese medicinal materials or herbal slices
Implementation Method 2
principal component analysis (PCA) is used to conduct dimension reduction on multidimensional data to simplify a few uncorrelated comprehensive indexes (principal components)
Implementation Method 3
binary logistic regression model... use Logit transformation to establish a linear regression model based on a curve relationship between variable and dependent variable
Data Source
AI summary
Disclosed is a detection method for quality grade of traditional Chinese medicine (TCM), including: detecting the levels of quality control index components of TCM and efficacy-related in vitro activity by establishing a correlation between principal components of TCM and in vitro activity; determining the state of sample cluster by principal component analysis; constructing a logistic regression model of quality grade versus index components and bioactivity and establishing corresponding grade detection formulas of Chinese medicinal materials by fitting a large number of sample data for Chinese medicinal materials from different places of origin and batches. The method of the present invention realizes the mathematical expression of a standard for quality difference of TCM, and provides a feasible solution for the industrialized evaluation of quality grades of Chinese medicinal materials or herbal slices finally.