Multi-Angle Optical Pathogen Detection with Machine Learning
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Solution Overview
Problem
Current methods for detecting pathogens and chemicals are often inaccurate and inefficient, leading to false positives and false negatives, and are not scalable, which poses health and economic risks, particularly in foodborne illness outbreaks.
Innovation Solution
A system utilizing incoherent light sources with machine learning and artificial intelligence models to analyze light intensity measurements from multiple angles, combining absorption and scattered radiation data to accurately detect pathogens and chemicals, providing real-time notifications and reducing the Limit of Detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used, then the detection process is simple, but the accuracy is low leading to false positives and false negatives
Solution Approach 1:
The system segments the detection task into multiple components: light source generation, light transmission through sample, light detection at multiple angles, and multi-model analysis. Each component is handled by specialized modules, with trained models processing specific aspects of the data and a multi-model integrating results to reduce false positives and negatives.
Solution Approach 2:
The system adds angular dimension to light detection by measuring light intensity at multiple angles (including transmitted and scattered light). This multi-angle approach provides additional information dimensions that improve detection accuracy and enable better differentiation between pathogen particles and other particles.
2Productivity
If traditional detection methods are used, then the equipment is simple, but the scalability is poor
Solution Approach 1:
The system replaces complex mechanical detection mechanisms with optical-based detection using light sources and sensors. This substitution enables automated, high-speed detection that can be scaled across multiple sampling locations without proportionally increasing mechanical complexity.
Solution Approach 2:
The system changes detection parameters by using multiple light angles and trained machine learning models to process the data. These parameter changes enable the system to handle multiple samples simultaneously and scale detection operations while maintaining accuracy through computational analysis rather than mechanical complexity.
3Measurement precision
If multi-angle light detection is implemented, then the detection accuracy improves, but the measurement time increases
Solution Approach 1:
The system performs preliminary actions by pre-training multiple specialized models on different aspects of light detection data before actual measurement. During operation, these pre-trained models quickly process their respective data streams and feed results to the multi-model, enabling rapid integration without requiring real-time complex computations.
Solution Approach 2:
The multi-model system operates autonomously by automatically integrating results from multiple trained models without requiring external intervention. The system self-manages the coordination of multi-angle measurements and computational analysis, reducing overhead time and enabling continuous operation.
4Reliability
If multiple trained models are used, then the false positive rate reduces, but the computational complexity increases
Solution Approach 1:
The computational task is segmented into multiple specialized trained models, each focusing on specific detection aspects. This segmentation allows parallel processing of different data features, reducing the computational burden on any single model while maintaining high detection reliability through collective analysis.
Solution Approach 2:
The multi-model acts as an intermediary that integrates results from multiple trained models. It coordinates the output from individual models, reconciles their findings, and produces the final detection result, thereby managing computational complexity through structured information flow and reducing redundant calculations.
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 accurate and rapid detection of pathogens and chemicals, reducing false positives and negatives, enabling early intervention in foodborne illness outbreaks and minimizing economic and health impacts.
Implementation Method 1
measuring absorption and scattered radiation at different angles
Implementation Method 2
measuring absorption and scattered radiation at different angles
Data Source
AI summary
An example method comprising: receiving a first set of intensity values based on a first set of intensity measurements for a set of wavelengths, the set of intensity measurements obtained by an apparatus configured to generate light and another light, detect the light that has passed through a portion of a sample, and measure intensity of the light by optical sensor, an angle separating the optical sensor and apparatus; applying a trained model to obtain a result; based on the result, determining a subset of wavelengths; receiving another set of intensity values, another set of intensity values being based on another set of intensity measurements for the set of wavelengths, the other set of intensity measurements obtained by the other light, detect the other light that has passed through another portion of the sample, and measure intensity by the optical sensor, another angle separating the optical sensor and the apparatus, the angles being different, applying another trained model to obtain another result, trained models being different from each other; applying the results to a trained multi-model to determine a positive or a negative pathogen detection for the pathogen in the sample, generating a notification and providing the notification.