Fraction-Product Method for Optical Spectroscopy Classification
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Solution Overview
Problem
Current methods struggle to effectively distinguish between different biological specimens, such as those with Alzheimer's disease, Lewy bodies, and Gulf War Illness, due to the complexity of biological tissues and the difficulty in discerning differences in electromagnetic spectra recorded by modern spectrometers.
Innovation Solution
The use of a fraction-product method, which involves determining median values of spectral intensities, calculating taxonomic cut-off values, and identifying optimal discriminants to separate different biological specimens, allowing for the classification of unknown specimens through the analysis of optical spectroscopy data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If modern spectrometers record thousands of spectral intensities to analyze complex biological tissues, then the measurement precision is improved, but the difficulty of detecting and measuring increases because the human eye cannot discern differences between spectra from distinct specimens
Solution Approach 1:
The patent introduces an intermediary computational analysis system that processes the spectral data between the spectrometer and human interpretation. This system applies mathematical algorithms to identify discriminant regions in the spectrum, effectively mediating between the high-precision measurements and human cognitive limitations by translating complex spectral patterns into actionable diagnostic information
Solution Approach 2:
The patent replaces the mechanical/visual system of human eye inspection with a computational algorithmic system. Instead of relying on human visual discrimination capabilities, the system uses automated mathematical processing to detect and analyze spectral differences, substituting human sensory limitations with computational analytical capabilities
2Ease of operation
If traditional methods are used to discover distinguishing spectral regions, then the process is simple, but the productivity decreases due to the daunting task of discovering regions of difference in complex spectra
Solution Approach 1:
The patent transforms the spectral analysis problem by changing the parameters of analysis from visual inspection to mathematical computation. By applying algorithms that calculate discriminant regions based on spectral intensity variations, the system efficiently processes complex spectral data and identifies diagnostic regions that would be impossible to discover through traditional visual methods
Solution Approach 2:
The system enables self-service automated analysis where the computational algorithm independently identifies discriminant spectral regions without requiring manual intervention or expert interpretation. The algorithm automatically processes the spectral data, identifies distinguishing features, and provides diagnostic information, making the complex analysis process self-sufficient and highly productive
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 approach enables the accurate classification of biological specimens by identifying specific regions of the electromagnetic spectrum that distinguish between different conditions, improving diagnostic accuracy and reducing the need for parameter adjustment, as demonstrated by successful classification of Alzheimer's disease and Lewy bodies in vivo.
Implementation Method 1
Spectra of electromagnetic radiation may be used to determine and/or define a chemical composition of a material of interest
Data Source
AI summary
The methods, apparatuses, computer-readable media, and systems described enable regions of electromagnetic spectra that may distinguish different biological specimens to be determined. Regions of electromagnetic spectra that distinguish known biological specimens then become candidates for methods to classify unknown specimens and/or make a medical diagnosis. A fraction-product, determined from two arrays associated with two groups, may be used to determine optimal discriminants for the two groups given numerical measurements of particular properties of the members of both groups.