Spectral Particle Detection Using Time-Domain Noise Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting foodborne pathogens and environmental hazards are slow, inaccurate, resource-intensive, and often require invasive testing, leading to delayed detection and significant economic and health impacts.
Innovation Solution
A method involving spectral data transformation and machine learning models is used to analyze spectral metrics from electromagnetic interactions with samples, allowing for rapid and accurate detection of particles of interest, including foodborne pathogens and environmental contaminants, by converting spectral data between frequency and time domains and applying trained models to identify specific signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional testing methods (PCR, ELISA, etc.) are used to detect pathogens, then detection accuracy can be achieved, but detection speed is slow and resource consumption is high
Solution Approach 1:
The patent replaces traditional mechanical/biological testing methods (PCR, ELISA) with optical spectroscopy and machine learning analysis. The system uses spectral data acquisition and processing to detect pathogens, eliminating the need for complex laboratory procedures and reagents, thereby achieving both rapid detection and maintained accuracy
Solution Approach 2:
The patent transforms spectral data from the frequency domain to the time domain using inverse Fast Fourier Transform, and applies machine learning models to analyze spectral metrics. This parameter transformation and advanced analysis enables faster detection while maintaining or improving accuracy compared to traditional methods
2Loss of time
If traditional pathogen detection methods are used, then specific pathogen identification can be achieved, but testing time is long (days)
Solution Approach 1:
The system performs preliminary spectral data acquisition and processing in real-time during food processing operations. By continuously monitoring spectral characteristics and using machine learning models to identify pathogens early in the process, the system eliminates the need for waiting days for test results, enabling immediate detection and action
Solution Approach 2:
The patent implements continuous spectral monitoring throughout the food processing supply chain. The system operates continuously without interruption, providing ongoing detection capability that maintains productivity while reducing testing time from days to minutes or seconds
3Measurement precision
If invasive testing methods are used to detect pathogens, then detection specificity can be achieved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces invasive biological testing methods with non-invasive optical spectroscopy. The system analyzes spectral characteristics of food samples without requiring tissue biopsy, blood draws, or other invasive procedures, thereby maintaining detection specificity while dramatically improving ease of operation
Solution Approach 2:
The system uses spectral data as an intermediary to detect pathogens. Instead of directly contacting or invading the sample for detection, the system measures optical properties (spectral metrics) that serve as indirect indicators of pathogen presence, achieving specificity without invasiveness
4Reliability
If government agencies wait for test results before declaring outbreaks and issuing recalls, then detection accuracy can be maintained, but loss of time increases
Solution Approach 1:
The system enables preliminary detection of pathogens during food processing and distribution before consumption. By continuously monitoring spectral characteristics and identifying pathogens early in the supply chain, the system allows food safety professionals to take action before outbreaks occur, dramatically reducing response time while maintaining accuracy
Solution Approach 2:
The patent implements real-time feedback through continuous spectral monitoring and machine learning analysis. The system provides immediate feedback on pathogen detection status, enabling rapid decision-making for food safety professionals to issue recalls or take corrective action without waiting for lengthy test results
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 pathogens and contaminants with reduced enrichment and incubation times, providing rapid, accurate, and cost-effective results, reducing health risks and economic losses associated with recalls and outbreaks.
Implementation Method 1
a spectral acquisition apparatus that causes electromagnetic radiation to interact with a sample and obtains a set of spectral metrics
Data Source
AI summary
An example method includes receiving first spectral data in a frequency domain, the first spectral data including a set of spectral metrics, the first spectral data being from an apparatus that obtains the set of spectral metrics based on interactions of electromagnetic radiation with a sample, transforming the first spectral data from the frequency domain to a time domain, removing background noise from the first spectral data in the time domain to create enhanced spectral data, transforming the enhanced spectral data to the frequency domain, detecting a particular particle of interest in the sample based on a comparison of the enhanced spectral data in the frequency domain to a spectral signature of the particular particle of interest, and providing the particle of interest detection notification.