Hyperspectral Petri Dish Imaging for Full-Surface Colony Detection
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Solution Overview
Problem
Existing methods and devices for detecting and analyzing bacterial, mold, or yeast cultures in Petri dishes are limited in their ability to image and analyze the entire inner surface, including the walls, leading to undetected colonies and requiring significant manual intervention.
Innovation Solution
A hyperspectral imaging device with a hole inspection lens array and a deep learning algorithm for accurate detection, identification, and counting of bacterial, mold, or yeast colonies, capable of illuminating and imaging the entire inner surface of Petri dishes, minimizing operator intervention and ensuring consistent imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging methods are used to detect bacterial colonies in Petri dishes, then the imaging process is simple and quick, but the ability to image and analyze the entire inner surface including walls is limited, leading to undetected colonies
Solution Approach 1:
The patent transitions from conventional 2D top-down imaging to 360-degree hyperspectral imaging that captures the entire inner surface of the Petri dish including walls and bottom, adding dimensional coverage to detect colonies in previously invisible areas
Solution Approach 2:
The patent employs hyperspectral imaging technology that captures spectral information across multiple wavelengths, transforming the imaging parameter from simple visible light to multi-spectral data, enabling enhanced detection precision through spectral analysis
2Productivity
If manual analysis of bacterial cultures is performed, then the method is simple to implement, but the process is time consuming and requires clean conditions to avoid contamination
Solution Approach 1:
The patent replaces manual mechanical analysis with automated hyperspectral imaging and machine learning algorithms, substituting human operators with an automated optical and computational system to achieve faster, contamination-free analysis
Solution Approach 2:
The system performs self-analysis through automated hyperspectral image acquisition and machine learning-based classification, eliminating the need for manual intervention and enabling continuous operation without contamination risk
3Ease of operation
If optical elements are placed outside the constant temperature incubator, then the equipment is easier to access and maintain, but the access of optical elements to the interior of the Petri dish is reduced
Solution Approach 1:
The patent integrates the imaging system within the incubator environment, nesting the optical elements and Petri dish holder inside the temperature-controlled chamber to maintain both accessibility and imaging precision under controlled conditions
4Measurement precision
If the sample holder is made movable to enable consistent imaging, then the imaging consistency is improved, but the positioning precision requirement increases
Solution Approach 1:
The patent uses a standardized Petri dish holder design that creates a consistent geometric reference frame, allowing the system to achieve imaging consistency through reproducible positioning geometry rather than relying solely on high-precision active positioning mechanisms
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 device achieves high accuracy and speed in detecting and counting colonies with reduced human error by using a deep learning algorithm, enabling faster processing and minimizing condensation issues, while allowing for higher sampling frequencies and automated data management.
Implementation Method 1
a light housing with a light source
Implementation Method 2
camera capable of hyperspectral image acquisition equipped with a lens array
Data Source
AI summary
An invention disclosing a device and method for the counting and identification of bacterial, mold or yeast colonies based on hyperspectral image capture and processing. A device for acquisition of hyperspectral images comprising camera capable of hyperspectral imaging, a hole inspection lens array and a light source arranged in or around a light housing is described. A method comprising a deep learning algorithm is made available for the processing of acquired hyperspectral data in order to detect, identify and count bacterial, mold or yeast samples contained within a Petri dish. In particular, the use of a hole inspection lens array allows acquisition of hyperspectral images of the complete inner surface of a Petri dish which, combined with the use of a deep learning algorithm ensure a high level of accuracy and sampling frequency.