Sub-visible Particle Purity Quantification via Electron Microscopy
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Solution Overview
Problem
Current methods for quantitatively assessing the purity of liquid samples containing sub-visible particles are not accurate and often involve manual steps that can distort results, making it difficult to reliably differentiate primary particles from debris and contaminants.
Innovation Solution
An automated method using electron microscopy that enhances and analyzes image edges and shapes to calculate a purity ratio based on the area of primary particles versus debris, employing techniques like the difference of Gaussians, thresholding, and texture analysis to objectively measure sample purity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to assess purity of sub-visible particle samples, then the process is simple to perform, but the measurement accuracy is poor and results are distorted
Solution Approach 1:
The patent replaces manual visual inspection and mechanical measurement methods with an automated image analysis system using electron microscopy. The system captures images of sub-visible particles and uses computer algorithms to automatically detect, classify, and measure particle properties, eliminating human subjectivity and manual errors while achieving high measurement precision through objective digital analysis
Solution Approach 2:
The patent creates digital copies (images) of the physical particle samples using electron microscopy. These digital representations are then analyzed through image processing algorithms that detect edges, enhance contrast, and classify particles based on their visual characteristics. This copying approach allows repeated analysis without altering the original sample and enables precise automated measurement
2Measurement precision
If current automated methods are used to differentiate debris from primary particles, then the process is automated, but the differentiation accuracy is insufficient
Solution Approach 1:
The patent applies local quality enhancement by analyzing specific local characteristics of particles such as edge sharpness, internal texture patterns, and local contrast variations. The image processing algorithms examine localized regions within each particle image to identify distinctive features that differentiate primary particles from debris, rather than relying solely on overall particle size or shape
Solution Approach 2:
The patent performs preliminary image processing actions including edge enhancement, contrast adjustment, and noise reduction before the actual particle classification. These preparatory steps improve the quality of the input data for the classification algorithms, enabling more accurate differentiation between primary particles and debris by enhancing relevant visual features
3Reliability
If manual steps are involved in purity assessment, then the method is easier to implement, but the results are biased and unreliable
Solution Approach 1:
The patent implements self-service through automated image analysis where the system performs all measurement and classification tasks without human intervention. The software automatically detects particles, classifies them as primary particles or debris based on image characteristics, calculates purity metrics, and generates reports. This eliminates operator bias and ensures consistent, reproducible results across different samples and operators
Solution Approach 2:
The patent incorporates feedback mechanisms where the image analysis system continuously refines its classification based on detected patterns and characteristics. The system uses the visual information from electron microscopy images to automatically adjust its classification criteria and improve accuracy, providing objective feedback that eliminates subjective bias in purity assessment
Data Source
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AI summary
The method is for quantification of purity of sub-visible particle samples. A sample to be analyzed is place in an electron microscope to obtain an electron microscopy image (100) of the sample. The sample contains objects (114). The objects (114) that have sizes being different from a size range of primary particles (120) and sizes being within the size range of primary particles (120) are enhanced. The objects (114) are detected as being primary particles (120) or debris (106). The detected primary particles (120) are excluded from the objects (114) so that the objects (114) contain debris (106) but no primary particles (120). A first total area (T1) of the detected debris (106) is measured. A second total area (T2) of the detected primary particles (120) is measured.