Multi-Model Spectral Analysis for Rapid Pathogen Detection
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Solution Overview
Problem
Current methods for detecting foodborne pathogens, allergens, and environmental hazards are slow, inaccurate, resource-intensive, and impractical for wide-scale screening, leading to delayed detection and significant economic and health consequences.
Innovation Solution
A system utilizing spectral acquisition apparatuses and machine learning models to analyze electromagnetic interactions with samples, enabling rapid and accurate detection of particles of interest, including foodborne pathogens and environmental contaminants, by applying trained models to spectral metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional testing methods (PCR, ELISA, immunofluorescent assay) are used to detect pathogens, then detection accuracy can be maintained, but testing speed becomes slow and resource consumption increases
Solution Approach 1:
The patent replaces traditional mechanical/chemical testing systems (PCR, ELISA, immunofluorescent assays requiring reagents and incubation) with an optical detection system using spectral acquisition apparatus. The system captures spectral metrics from samples and uses machine learning models to identify pathogens, eliminating the need for reagents, incubation chambers, and complex chemical reactions while maintaining detection accuracy and significantly reducing testing time.
Solution Approach 2:
The patent creates a spectral fingerprint copy of the pathogen's optical signature across multiple wavelengths. Instead of using physical reagents that interact chemically with pathogens, the system captures and analyzes the pathogen's inherent spectral characteristics, creating a digital representation that can be rapidly compared against trained models for identification without consuming physical testing resources.
2Reliability
If traditional testing methods are used, then pathogen detection can be performed, but detection time is extended causing delayed response to outbreaks
Solution Approach 1:
The patent performs preliminary action by training machine learning models in advance using spectral data from known pathogens. During actual testing, the pre-trained models can immediately analyze new spectral metrics without requiring time-consuming reference comparisons or confirmation steps. This preliminary training phase enables rapid, accurate detection during outbreaks while maintaining high reliability through the models' learned patterns.
Solution Approach 2:
The patent replaces time-consuming mechanical processes (incubation periods, sequential testing steps, manual analysis) with instantaneous optical measurement and computational analysis. The spectral acquisition apparatus captures all necessary data in a single measurement, and the machine learning model processes this data in real-time, eliminating the multi-step, time-intensive workflow of traditional methods while preserving detection accuracy.
3Measurement precision
If invasive testing methods are used, then pathogen detection sensitivity can be improved, but ease of operation deteriorates requiring specialized equipment and expertise
Solution Approach 1:
The patent creates a universal detection system that can identify multiple types of pathogens (bacteria, viruses, parasites) using a single spectral acquisition apparatus and machine learning model framework. The system is designed to handle diverse sample types and pathogen categories through multi-class classification models trained on comprehensive spectral datasets, eliminating the need for specialized equipment or expertise for each specific pathogen type while maintaining high detection sensitivity.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically perform pathogen identification without requiring expert interpretation. The machine learning models autonomously analyze spectral metrics, compare them against trained patterns, and generate detection results, eliminating the need for specialized personnel to interpret complex spectral data or operate sophisticated equipment, thereby simplifying operation while preserving measurement precision.
4Adaptability or versatility
If traditional testing systems are deployed, then pathogen identification can be achieved, but device complexity and cost increase due to multiple specialized apparatuses
Solution Approach 1:
The patent employs a single spectral acquisition apparatus that can detect multiple classes of pathogens (bacteria, viruses, parasites) through one device. The machine learning models are trained to perform multi-class classification, enabling the system to identify various pathogen types without requiring separate specialized equipment for each category, thereby reducing device complexity while maintaining versatile pathogen detection capability.
Solution Approach 2:
The patent merges multiple detection functions into a unified system. Instead of using separate apparatuses for different pathogen types, the system combines spectral acquisition, multi-class classification modeling, and pathogen identification into an integrated framework. This consolidation reduces the number of components, simplifies the overall system architecture, and lowers costs while preserving the ability to detect diverse pathogens.
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 early detection of contaminants, reducing the risk of outbreaks and illnesses, lowering economic costs, and providing quick, accurate results for pathogen identification in food and environmental samples.
Implementation Method 1
receiving data, the data including a set of spectral metrics, the data from an apparatus that obtains the set of spectral metrics based on interactions of electromagnetic radiation with a sample
Data Source
AI summary
An example method includes receiving data that includes a set of spectral metrics from interactions of electromagnetic radiation with a sample. A first trained model and a second trained model is applied to at least one of the set of spectral metrics and a set of values based on the set of spectral metrics to obtain a first result and a second result. Based on at least one of the first result and the second result, either a positive particle of interest detection or a negative particle of interest detection for at least one of first particles of interest, a first type of the first particles of interest, and a second type of the first particles of interest for the sample is determined. A particle of interest detection notification that indicates either the positive particle of interest detection or the negative particle of interest detection is generated and provided.