Miniature Multi-Spectral Pathogen Detection System
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Solution Overview
Problem
Standard spectrometer techniques face challenges in detecting pathogens, such as viruses in biological fluids, at low concentrations amidst a complex mixture of substances, which is time-consuming and costly.
Innovation Solution
A miniature multi-spectral system comprising multiple miniature spectrometers and a processor that performs data fusion and applies artificial intelligence to identify pathogens, biomarkers, or compounds from a sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard spectrometer techniques are used to detect pathogens in complex mixtures, then detection capability is provided, but detection accuracy and sensitivity deteriorate when target substance is present at low concentration
Solution Approach 1:
The system segments the detection task by using multiple spectrometers (e.g., UV, visible, NIR) to capture different spectral channels simultaneously. Each spectrometer provides specialized detection for specific wavelength ranges, and the segmented spectral data are fused to achieve high sensitivity detection of low-concentration pathogens in complex mixtures
Solution Approach 2:
The system merges data from multiple spectrometers through data fusion techniques, combining UV absorption spectra, visible spectra, and NIR spectra into a unified detection result. This merging of multiple spectral data sources enhances the signal-to-noise ratio and enables accurate detection of low-concentration targets that would be undetectable by a single spectrometer
2Reliability
If standard spectrometer techniques are used for substance identification, then detection is provided, but time consumption increases
Solution Approach 1:
The system employs periodic action by simultaneously acquiring multiple spectral channels in rapid succession using multiple spectrometers. The synchronized periodic sampling of UV, visible, and NIR spectra enables complete spectral characterization within minutes, maintaining high detection reliability while significantly reducing total analysis time compared to sequential single-spectrometer methods
3Measurement precision
If multiple spectrometers are used to improve detection sensitivity, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The system achieves universality by designing a multi-spectral platform where multiple spectrometers share common control and processing architecture. The same data fusion and AI analysis algorithms process data from different spectral channels, allowing the system to detect various pathogens and compounds across different wavelength ranges without requiring separate specialized systems for each application
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
The system achieves high accuracy and sensitivity in detecting pathogens within minutes at a low cost, capable of identifying substances down to very low concentrations in complex mixtures.
Implementation Method 1
The most common arrangement is to direct a generated beam of radiation at a sample and detect the intensity of the radiation that passes through it. The transmitted energy can be used to calculate the wavelength-dependent absorption.
Implementation Method 2
Other analytical methods involve absorption of certain wavelengths and not other wavelengths as a substance is illuminated with ultraviolet energy
Implementation Method 3
The processor is configured to execute instructions to perform data fusion of the first and second spectral outputs to generate fused data
Implementation Method 4
to apply artificial intelligence (AI) of an AI module to the fused data to identify a pathogen, biomarker, or any compound from the sample
Data Source
AI summary
Embodiments of this invention relate generally to a miniature multi-spectral system to detection pathogen, biomarkers, or any compound from a sample. In one example, a miniature multi-spectral system comprises a first miniature spectrometer to generate a first spectral output based on a sample, a second miniature spectrometer to generate a second spectral output based on the sample, and a processor coupled to the first and the second miniature spectrometers. The processor is configured to execute instructions to perform data fusion of the first and second spectral outputs to generate fused data, and to apply artificial intelligence (AI) of an AI module to the fused data to identify a pathogen, biomarker, or any compound from the sample.