Fluorescence-Cued Raman Identification of Viable Organisms
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Solution Overview
Problem
Conventional methods for identifying viable but non-culturable bacterial organisms are inadequate, as they rely on colony formation and are not effective in detecting organisms in a viable but non-dividing state, posing challenges in public health risk assessments.
Innovation Solution
A fluorescence-cued Raman identification system that collects samples, treats them with reagents to mark viable organisms, uses fluorescence imaging to locate and Raman spectroscopy to analyze these organisms, and integrates visual microscopy for confirmation, enabling rapid and objective identification of viable microorganisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional microbiological methods relying on colony formation are used, then the detection process is simple and inexpensive, but viable but non-culturable bacteria cannot be detected
Solution Approach 1:
The system segments the detection process into multiple specialized subsystems: fluorescence imaging subsystem for locating viable organisms, Raman spectroscopy subsystem for molecular identification, and automated stage subsystem for sample navigation. Each subsystem performs a specific function, collectively achieving reliable detection of viable but non-culturable bacteria while managing complexity through modular architecture
Solution Approach 2:
The patent introduces fluorescence dyes as intermediary agents that bind to viable bacteria and emit detectable signals. This intermediary mechanism enables detection of viable but non-culturable organisms by converting their metabolic activity into optical signals that can be captured and analyzed by the imaging and spectroscopy subsystems
2Productivity
If manual inspection methods are used, then the equipment required is simple, but the identification process is time-consuming and subjective
Solution Approach 1:
The system implements automated image processing algorithms that automatically locate, identify, and classify bacteria based on their fluorescence and Raman spectral characteristics. The automated stage subsystem navigates the sample independently, and the software performs objective classification without manual intervention, enabling rapid identification while maintaining system manageability through automation
Solution Approach 2:
The patent replaces manual mechanical inspection with automated optical and computational systems. Fluorescence imaging and Raman spectroscopy subsystems automatically capture and analyze bacterial characteristics, while image processing algorithms replace human visual inspection, dramatically increasing identification speed and objectivity
3Reliability
If conventional plate count methods are used, then the methodology is well-established and simple, but it cannot detect organisms in viable but non-culturable state
Solution Approach 1:
The system performs preliminary staining with fluorescence dyes that target viable bacteria before detection. This preliminary action marks viable organisms (including viable but non-culturable ones) with fluorescent signals, enabling their rapid identification and eliminating the need for time-consuming culture incubation periods
Solution Approach 2:
The patent replaces the time-consuming biological culture process with direct optical detection methods. Fluorescence imaging and Raman spectroscopy provide immediate detection of viable bacteria based on their metabolic activity and molecular composition, reducing detection time from days to minutes while improving reliability
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
This system allows for the rapid and objective identification of viable microorganisms, reducing false positives and negatives, and providing a more accurate assessment of potential pathogens, even when they are in a non-culturable state.
Implementation Method 1
a fluorescence imaging subsystem that receives the treated sample from the reagent treatment subsystem and that automatically takes at least one image of the collected sample
Implementation Method 2
a Raman spectroscopy subsystem that receives the sample from the fluorescence subsystem and that measures the spectrum of viable microorganisms
Data Source
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AI summary
A fluorescence-cued Raman identification system comprises a collection subsystem to collect samples, a reagent treatment subsystem to treat collected samples and a fluorescence imaging subsystem that automatically takes at least one image of the collected sample. The subsystems further include a Raman spectroscopy subsystem that measures the spectrum of viable organisms located from at least one collected image, a visible imaging microscope subsystem that provides a visual image of the particle analyzed by the Raman spectroscopy subsystem and a processor configured to perform image processing to locate viable organisms within the sample, which are targeted by the Raman spectroscopy subsystem. The processor further analyzes the spectrum recorded by the Raman spectroscopy subsystem to make a preliminary identification of the targeted organisms, which can be verified by an operator using the visible imaging microscope subsystem.