Multivariate Optical Computing System for Spectral Component Selection
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Solution Overview
Problem
Existing optical systems face challenges in accurately measuring light intensity due to interference from various factors, making it difficult to derive information from light signals, especially in applications like polymer and gasoline analysis, where factors other than ethylene content or octane rating affect wavelength bands, leading to inaccurate estimates.
Innovation Solution
The implementation of multivariate optical computing (MOC) systems that utilize spectral weighting and principal component analysis to decompose light signals into orthogonal components, allowing for precise measurement of chemical properties by selecting optimal spectral elements and system components based on calibration data and performance modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple light intensity measurement is used, then measurement simplicity is maintained, but measurement precision deteriorates due to interfering data from multiple factors
Solution Approach 1:
The patent segments the light signal into multiple wavelength bands using bandpass filters, allowing separate measurement of different spectral components. This segmentation enables the system to isolate the signal of interest from interfering factors by measuring intensity across multiple wavelength regions rather than a single broad band.
Solution Approach 2:
The patent changes the measurement parameter from single intensity measurement to multi-wavelength intensity measurements. By measuring light intensity across multiple wavelength bands and applying multiple linear regression analysis, the system transforms a simple intensity measurement into a multidimensional measurement that can distinguish between different contributing factors.
2Measurement precision
If multiple linear regression with bandpass filters is used, then measurement precision improves, but device complexity increases due to multiple filters and detectors
Solution Approach 1:
The patent designs the optical system with universal components that can serve multiple functions. The same set of bandpass filters and detectors used for spectral analysis can also be used for the actual measurement, eliminating the need for separate measurement paths. The system performs both spectral decomposition and intensity measurement using the same hardware infrastructure.
Solution Approach 2:
The patent performs preliminary spectral decomposition using bandpass filters before the actual measurement. By pre-separating the light into wavelength bands, the system prepares the signal in advance, allowing the detectors to directly measure the decomposed components without requiring additional complex processing hardware during the measurement phase.
3Measurement precision
If principal component analysis is used to compress data, then measurement precision improves by reducing noise, but computational complexity increases
Solution Approach 1:
The patent extracts the principal components from the spectral data using singular value decomposition. By identifying and extracting only the most significant variance-carrying components, the system separates the meaningful signal from noise and interfering factors. This extraction process reduces the dimensionality of the data while preserving the essential information needed for accurate measurement.
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 enhances measurement precision by reducing noise and interference, enabling accurate estimation of chemical properties like ethylene content and octane rating, even in complex light signals, with improved signal-to-noise ratios and reduced instrumentation complexity.
Implementation Method 1
When light interacts with matter, for example, it carries away information about the physical and chemical properties of the matter. A property of the light, for example, its intensity, may be measured and interpreted to provide information about the matter with which it interacted.
Data Source
AI summary
Methods of selecting spectral elements and system components for a multivariate optical analysis system include providing spectral calibration data for a sample of interest; identifying a plurality of combinations of system components; modeling performance of a pilot system with one of the combinations of system components; determining optimal characteristics of the pilot system; and selecting optimal system components from among the combinations of system components.