Topological Spectral Analysis Database Expansion
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Solution Overview
Problem
Existing topological spectral analysis methods require a large number of standards and are limited by the need for strong correlations between spectra and properties, making them inefficient and labor-intensive, especially when dealing with nonlinear relationships and new data points outside established models.
Innovation Solution
A method that creates an expanded spectral database using a limited number of standards by eliminating 'polluting' wavelengths, generating synthetic standards through combinations, and optionally incorporating chemical compounds, allowing for characterization with a reduced number of standards and improved model stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of standards are used in topological spectral analysis, then measurement precision is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent performs preliminary spectral analysis to identify and eliminate 'polluting' wavelengths before generating synthetic standards. This preliminary action reduces the dimensionality of the spectral data, allowing for faster database establishment while maintaining characterization accuracy. The method pre-processes the spectral information to retain only the most relevant wavelengths for product characterization.
Solution Approach 2:
The patent generates synthetic standards by creating virtual copies of actual standards through mathematical combinations of spectral data. These synthetic standards replicate the spectral characteristics of real products without requiring physical samples, thereby expanding the spectral database efficiently. This copying approach maintains measurement precision while dramatically reducing the time and resources needed to establish the database.
2Ease of operation
If classical mathematical regression methods are used, then ease of operation is maintained, but reliability decreases due to inability to handle nonlinear relationships
Solution Approach 1:
The patent transforms the spectral data by identifying and eliminating specific wavelengths that contribute to noise and interference. This parameter change in the data representation allows topological analysis to effectively capture nonlinear relationships while maintaining operational simplicity. The method changes the parameters of spectral analysis from using all wavelengths to using only the most informative ones.
Solution Approach 2:
The patent replaces classical mathematical regression methods with topological spectral analysis. This substitution introduces a different analytical mechanism that is inherently capable of handling nonlinear relationships. The topological approach uses spectral fingerprints and pattern recognition rather than traditional regression, improving reliability for complex spectral data while keeping the method accessible.
3Adaptability or versatility
If spectral database is expanded with more standards, then adaptability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent extracts and eliminates 'polluting' wavelengths from the spectral database that contribute to complexity and interference. By removing these problematic wavelengths, the method reduces the difficulty of spectral analysis while maintaining or even improving adaptability. The extraction of essential spectral features makes the analysis more manageable without sacrificing coverage of product types.
Solution Approach 2:
The patent applies different treatment to different parts of the spectral data by identifying and eliminating specific wavelengths that are problematic, while retaining others that are informative. This local quality approach allows the spectral database to be expanded with diverse product types while managing complexity by focusing analysis on the most relevant spectral regions rather than treating all wavelengths uniformly.
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
Enables accurate characterization of products with a significantly reduced number of standards, enhancing model stability and reactivity, and reducing the need for extensive recalibration when new data points are introduced.
Implementation Method 1
measuring the absorption Dix of said material at more than one wavelength in the region of 600 to 2600 nm
Data Source
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AI summary
The invention relates to a method for generating and optimising a bank of spectral data that can be used in a method for characterising a target product by means of topological spectral analysis based on a limited number of available standards, said method consisting of a first step of performing the same spectral analysis on said standards, and, from the spectra obtained, forming a bank of spectral data A at multiple wavelengths and/or wavelength ranges.