Bacteria Classification via Morphology and Motility Signatures
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Solution Overview
Problem
Current methods for identifying bacteria in the food industry, such as PCR and DNA sequencing, are inefficient and time-consuming, requiring an enrichment process that allows bacterial counts to grow, posing a risk due to the rapid multiplication of pathogens like E. Coli, Salmonella, and Campylobacter.
Innovation Solution
A method using artificial intelligence algorithms to extract morphology and motility signatures from bacteria, merging them into a vector signature for classification, allowing for rapid and accurate identification using a low-cost microscope and deep learning neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PCR or DNA sequencing methods are used to identify bacteria, then bacteria type identification is achieved, but the process is time-consuming and inefficient due to requiring 8-24 hours enrichment process
Solution Approach 1:
The patent extracts and analyzes individual bacterial cells directly from the sample without requiring bulk enrichment. By using automated microscopy and image analysis to examine single cells, the system obtains identification results in minutes rather than waiting 8-24 hours for bacterial colonies to grow to detectable levels through traditional enrichment processes.
Solution Approach 2:
The patent creates digital images and analytical models of bacterial cells instead of relying on physical colony growth. By capturing optical images of individual cells and analyzing their morphological features through computer algorithms, the system produces identification results without the time-consuming physical enrichment step required by conventional methods.
2Quantity of substance
If enrichment process is used to increase bacterial count to 10^4 cfu/ml, then sufficient bacteria are available for testing, but bacterial count grows too rapidly posing contamination risk
Solution Approach 1:
The patent extracts and analyzes individual bacterial cells directly from the original sample at low concentrations (10 cfu/ml). By examining single cells rather than requiring bulk enrichment to 10^4 cfu/ml, the system obtains sufficient material for identification without initiating the rapid bacterial multiplication that creates contamination risks in traditional enrichment processes.
3Measurement precision
If traditional PCR or DNA sequencing methods are used, then bacteria identification is achieved, but the process lacks efficiency and ease of operation
Solution Approach 1:
The patent implements an automated system where the microscopy apparatus and image analysis algorithms perform the identification process autonomously. The system automatically captures images, processes them through morphological and motility analysis algorithms, and generates identification results without requiring manual intervention for each step, thereby significantly improving ease of operation compared to traditional PCR methods that require multiple manual processing steps.
Solution Approach 2:
The patent replaces complex biochemical processes (PCR, DNA extraction, sequencing) with optical imaging and computational analysis. By substituting mechanical and chemical operations with automated microscopy and image processing algorithms, the system simplifies the workflow while maintaining identification accuracy, making the process easier to operate and faster to complete.
Data Source
AI summary
A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.