Bacterial Gram Type Identification via Optical Scanning
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Solution Overview
Problem
Current methods for determining the Gram type and fermentative character of bacteria require manual techniques, chromogenic or fluorogenic substrates, and modifications to the bacteria, making them unsuitable for subsequent characterization tests and inefficient for rapid identification.
Innovation Solution
A process involving illumination in the wavelength range of 390 nm-900 nm to acquire light intensity from bacterial colonies grown in a non-chromogenic medium, allowing for the automatic determination of Gram type and fermentative character without labeling or staining, using hyperspectral or multispectral imaging and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual Gram staining techniques are used to determine Gram type, then identification accuracy is achieved, but the process is time-consuming and requires multiple manual steps
Solution Approach 1:
The patent replaces manual mechanical staining procedures with an automated optical system that uses light scattering measurements. A machine learning model analyzes the optical properties of bacterial colonies to determine Gram type, eliminating the need for manual staining steps while maintaining identification accuracy and significantly reducing processing time.
Solution Approach 2:
The system enables bacteria to be automatically characterized through their inherent optical properties without requiring external staining agents or manual intervention. The machine learning model processes the natural light scattering patterns of bacterial colonies to self-determine Gram type, making the process autonomous and rapid.
2Ease of operation
If chromogenic or fluorogenic substrates are used to label bacteria, then Gram type and fermentative character become easily observable, but the bacteria are modified and cannot be used for subsequent characterization tests
Solution Approach 1:
The patent substitutes chemical labeling methods with physical optical measurement. By analyzing light scattering patterns in the visible spectrum, the system determines Gram type and fermentative character without introducing any chemical modifiers to the bacteria, preserving their natural state for subsequent tests.
Solution Approach 2:
The system creates an optical signature or fingerprint of the bacterial colony's light scattering properties. This optical copy contains all necessary information for identification without physically altering the bacteria, allowing multiple analyses to be performed on the same sample.
3Ease of operation
If specialized chromogenic media are used to differentiate bacterial types, then identification is simplified, but additional consumables and cost are required
Solution Approach 1:
The patent develops a universal imaging system that can identify Gram type and fermentative character using standard visible light microscopy without requiring specialized chromogenic media. The machine learning model processes optical images to perform multiple identification functions simultaneously, eliminating the need for different colored substrates for different bacterial types.
Solution Approach 2:
The system changes the measurement parameter from chemical reaction products (color changes in chromogenic media) to physical optical properties (light scattering patterns). This parameter change allows identification using standard media and equipment, reducing consumable requirements while maintaining identification capability.
4Loss of information
If multiple dedicated techniques are used for each bacterial property, then comprehensive characterization is achieved, but the overall process complexity increases
Solution Approach 1:
The patent merges multiple identification functions into a single integrated system. The machine learning model simultaneously determines Gram type, fermentative character, and other bacterial properties from a single optical image, eliminating the need for separate dedicated techniques for each property and reducing overall process complexity.
Solution Approach 2:
The imaging system serves multiple functions: it captures optical images for Gram type determination, identifies fermentative character, and provides data for other bacterial characterizations. This multi-functional approach comprehensively characterizes bacteria using a single technique rather than multiple separate methods.
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, automated identification of Gram type and fermentative character, optimizing microbiological processes and reducing the need for additional testing, while preserving the natural state of bacteria for further characterization.
Implementation Method 1
illumination in the wavelength range of 390 nm-900 nm
Implementation Method 2
acquire, within said range, a light intensity reflected from or transmitted through said illuminated bacterium
Implementation Method 3
having a natural electromagnetic response in said range
Data Source
AI summary
The invention relates to a method for detecting the Gram type and the fermenting character of a bacterial strain, said method comprising: illuminating, in the wavelength range of 390 nm to 900 nm, at least one bacterium from said strain which has a natural electromagnetic response within said range; acquiring, within said range, a light intensity reflected by, or transmitted through, said illuminated bacterium; and determining the Gram type and the fermenting character of the bacterial strain according to the light intensity acquired within said range.