Microbial Colony Tracking via Laser Diffraction and Orientation Maintenance
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Solution Overview
Problem
Current methods for rapid identification and classification of microbial organisms, such as pathogenic microorganisms like Escherichia coli, Listeria, Salmonella, and Staphylococcus, are inefficient and lack the ability to track colonies over time and changes in orientation, which is crucial for biosurveillance, biosecurity, and food safety.
Innovation Solution
A computer-based method using laser scattering technology (BARDOT) to image microbial colonies, which generates diffraction patterns that can be analyzed using machine-learning techniques to identify and classify bacteria, and a system to track colonies over time and maintain substrate orientation, utilizing barcodes and image processing to correlate and locate colonies accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional microbial identification methods are used, then reliability of identification is maintained, but productivity is low and time consumption is high
Solution Approach 1:
The patent replaces manual mechanical identification methods with automated laser scattering-based optical measurement systems. The BARDOT system uses laser diffraction patterns to automatically identify and classify microbial colonies, eliminating the need for manual examination and significantly reducing identification time while maintaining reliability.
Solution Approach 2:
The patent creates optical copies (diffraction patterns) of microbial colonies using laser scattering. These diffraction patterns serve as unique identifiers that can be rapidly captured, stored, and compared, enabling fast automated identification without requiring physical manipulation or time-consuming traditional analysis methods.
2Measurement precision
If colonies are tracked over time with orientation changes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal image processing system that simultaneously performs multiple functions: capturing colony images, detecting orientation changes, tracking colony positions, and identifying colonies. This multi-functional approach consolidates what would otherwise require separate complex subsystems into a single integrated platform, reducing overall device complexity while maintaining high measurement precision.
Solution Approach 2:
The patent uses fiducial markers as intermediary reference objects placed on the culture plate. These markers serve as mediators between the imaging system and the colonies, providing stable reference points that enable accurate orientation detection and colony location tracking even when the plate is moved or rotated, without requiring complex direct measurement systems.
3Productivity
If automated image processing is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service automation where the system automatically performs image acquisition, processing, colony identification, and result generation without requiring manual intervention. The automated workflow includes automatic focus adjustment, image capture, diffraction pattern analysis, and classification, which increases throughput while the integration of these functions into a unified system minimizes the operational complexity burden.
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 fast, accurate, and cost-effective identification and classification of microbial colonies without reagents, allowing for longitudinal analysis and efficient tracking of colony changes, improving biosurveillance and food safety by providing reliable identification and monitoring capabilities.
Implementation Method 1
A computer-based method using laser scattering technology (BARDOT) to image microbial colonies, which generates diffraction patterns
Implementation Method 2
which generates diffraction patterns that can be analyzed using machine-learning techniques to identify and classify bacteria
Data Source
AI summary
A computer method for correlating depictions of colonies of microorganisms includes receiving an image of a substrate associated with a first time and showing a colony of microorganisms. A second image of the same substrate and associated with a second time shows a candidate colony of microorganisms. A region of the second image that shows the candidate colony of microorganisms is located. The first region of the first image is compared to the second region of the second image. Based on the comparison of the images, the candidate colony of microorganism is determined to be the same colony as the first colony of microorganisms. Systems for moving substrates having colonies of microorganisms and maintaining orientation of the substrates before and after movement are also described.