Spectral Matching Using Range Bins and Hypergeometric Probability
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Solution Overview
Problem
Conventional spectral matching techniques are computationally challenging and inadequate for quickly identifying matches between an unknown sample and a large database of reference samples, as they require extensive computations and do not provide probabilistic information on the likelihood of matches.
Innovation Solution
The method involves partitioning the spectrum into multiple different sized range bins, where each peak from reference samples resides, and using a hypergeometric probability model to generate probability information for candidate matches, allowing for a computationally efficient search and identification of likely matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional spectral matching techniques are used to compare unknown samples against large reference databases, then comprehensive matching coverage is achieved, but computational complexity and processing time increase substantially
Solution Approach 1:
The patent segments the continuous spectral data into discrete peak features and further divides the reference database into smaller manageable groups. By focusing on peak positions, intensities, and shapes rather than comparing entire spectra point-by-point, the computational burden is dramatically reduced while maintaining matching accuracy
Solution Approach 2:
The patent extracts key spectral features (peak positions, intensities, widths) from the full spectral data. This extraction process isolates the most discriminative characteristics needed for identification, eliminating redundant information and reducing the dimensionality of the comparison task
2Reliability
If conventional spectral matching techniques are used to compare unknown samples against large reference databases, then comprehensive matching coverage is achieved, but processing time increases substantially
Solution Approach 1:
The patent performs preliminary processing of reference spectra by pre-identifying peak features and organizing them into searchable databases before actual sample analysis. This pre-computation of spectral fingerprints allows rapid querying during unknown sample identification without repeating full spectral comparisons
Solution Approach 2:
The patent creates simplified binary representations (spectral fingerprints) that copy only the essential peak information from full spectra. These binary strings serve as efficient proxies for complete spectral data, enabling rapid comparison operations that are orders of magnitude faster than conventional methods
3Productivity
If spectral data is compressed into binary format using conventional approaches, then search efficiency is improved, but probabilistic interpretation capability is lost
Solution Approach 1:
The patent changes the parameters used in binary encoding from simple presence/absence indicators to weighted representations that incorporate peak intensity information. This allows the binary format to encode both the location and relative importance of spectral features, enabling probabilistic scoring based on match quality
Data Source
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AI summary
A processing application receives peak information associated with multiple known references samples. The processing application partitions a spectrum into multiple different sized range bins such that a substantially equal number of the peaks associated with the known reference samples reside into each of the multiple different sized range bins. To identify a set of candidate reference samples in a library of reference samples that potentially are a good match an unknown sample under test, the processing application compares peaks associated with the unknown sample to the multiple different sized range bins. The greater the number of range bin matches based on peaks in the unknown sample and peaks in a corresponding reference sample, the greater the likelihood that the unknown sample matches the corresponding reference sample.