Spectral Conformity Testing Using kNN Reference Selection
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Solution Overview
Problem
Existing vibrational spectroscopy methods for non-quantitative analysis, such as detecting adulteration or monitoring chemical processes, become less sensitive and specific as the reference spectrum library grows to accommodate new sample types, due to increasing spectral variance.
Innovation Solution
A dynamic approach using a k-nearest neighbor (kNN) search to select appropriate reference spectra from a continuously expanding library, allowing untargeted evaluations without requiring a new method for each sample type, and enabling process monitoring through a computer-implemented method that computes conformity based on spectral fingerprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the reference spectrum library is expanded to accommodate new sample types, then the adaptability of the system is improved, but the measurement precision deteriorates due to increasing spectral variance
Solution Approach 1:
The patent segments the reference spectrum library into multiple subsets, each corresponding to a specific sample type or category. Instead of using a single large library for all samples, the system divides the comprehensive library into smaller, more focused subsets that can be selectively applied based on the sample being analyzed. This segmentation maintains precision within each subset while preserving the ability to handle diverse sample types across the entire library.
Solution Approach 2:
The patent implements a dynamic approach where the system automatically selects the most appropriate reference spectrum subset based on the characteristics of the input sample. This dynamic selection process allows the system to adapt to different sample types while maintaining optimal measurement precision by using only the relevant subset for each analysis, rather than relying on a static, monolithic library.
2Adaptability or versatility
If more reference spectra are added to the library, then the coverage of sample types is improved, but the conformity test becomes less specific due to increased spectral variance
Solution Approach 1:
The patent segments the reference spectrum library into multiple subsets, each corresponding to a specific sample type or category. Instead of using a single large library for all samples, the system divides the comprehensive library into smaller, more focused subsets that can be selectively applied based on the sample being analyzed. This segmentation maintains precision within each subset while preserving the ability to handle diverse sample types across the entire library.
Solution Approach 2:
The patent applies local quality by ensuring that each reference spectrum subset is optimized for its specific sample type. Rather than requiring uniform quality across the entire library, the system allows each subset to have specialized characteristics tailored to its intended use. This local optimization maintains high specificity for each sample type while collectively covering a broad range of materials.
3Device complexity
If a static evaluation method is used, then the simplicity of the method is maintained, but the ability to detect new spectral variance is lost
Solution Approach 1:
The patent implements a dynamic approach where the system automatically selects the most appropriate reference spectrum subset based on the characteristics of the input sample. This dynamic selection process allows the system to adapt to different sample types while maintaining optimal measurement precision by using only the relevant subset for each analysis, rather than relying on a static, monolithic library.
Solution Approach 2:
The patent enables the system to self-adjust by automatically selecting appropriate reference subsets based on sample characteristics. The system performs self-service through automated subset selection and dynamic adaptation, eliminating the need for manual reconfiguration when analyzing new sample types. This self-service capability maintains simplicity for the user while internally implementing complex adaptive behavior.
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 highly specific and sensitive non-quantitative analysis by dynamically selecting reference spectra, maintaining sensitivity and specificity even with diverse sample types, and allowing continuous process monitoring for early deviation detection.
Implementation Method 1
Vibrational spectroscopy is a method for direct measurement of covalent bonds in molecules consisting of atoms. Vibrational spectroscopy methods include infrared spectroscopy - e.g., near-infrared (NIR) or mid-infrared (MIR) - and vibrational Raman spectroscopy.
Data Source
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AI summary
A system (100), method and computer program product are disclosed for determining conformity of a fingerprint of a sample (201) with at least one corresponding expected fingerprint of said sample. The system obtaining a measured spectrum (211) as the fingerprint of the sample (201). The measured spectrum is the result of a measurement reflecting a current chemical and physical state (C1) of the sample obtained by vibrational spectroscopy. The system accesses a spectrum library (300) comprising a plurality (311) of calibrated reference spectra (311-1 to 311-n). A kNN module (110) identifies a subset of the reference spectra in the spectrum library by determining representations (311k) of k nearest neighbor reference spectra in the vicinity of a corresponding representation (311s) of the measured spectrum (211) in accordance with predefined metrics. An averaged reference spectrum module (120) computes, based on the identified subset, an averaged reference spectrum representing the at least one corresponding expected fingerprint, and computes the standard deviation in each data point of the k nearest neighbor reference spectra. A difference spectrum module (130) determines a difference spectrum by computing the difference between measured spectrum and averaged reference spectrum divided by the corresponding standard deviation in each data point. A conformity module (140) determines deviating data points where the value of the difference spectrum exceeds a predefined deviation threshold, and determines conformity (145) in accordance with a predefined conformity rule (141) based on the data points exceeding said predefined deviation threshold.