Particle Analyzer Coincidence Detection and Handling
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Solution Overview
Problem
Particle analyzers face challenges in accurately analyzing data when multiple particles coincide within the measurement region, leading to distorted signals and compromised analytic results due to the superposition of individual particle signals, which existing methods struggle to accurately compensate for.
Innovation Solution
A method and system for identifying and handling coincident events in particle analyzers by analyzing signal parameters such as peak, center-point, and area to determine the presence of coincident particles, adjusting particle counts, and discarding or accounting for coincident events to produce more accurate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple particles pass through the measurement region simultaneously (coincidence), then the measurement region can process more particles, but the signal becomes distorted and measurement precision deteriorates
Solution Approach 1:
The patent segments the measurement process by dividing the measurement region into multiple sub-regions or time intervals, allowing particles to be measured in separate segments rather than simultaneously as a whole group. This segmentation prevents signal superposition and distortion while maintaining the ability to process multiple particles through sequential measurement.
Solution Approach 2:
The patent applies preliminary action by detecting and identifying coincident particles before they fully interact with the measurement region. The system uses preliminary sensors or pre-processing to recognize when multiple particles are approaching simultaneously, and then takes corrective action such as excluding those events or adjusting measurement parameters to prevent signal distortion.
2Reliability
If statistical methods are used to compensate for coincidence errors, then coincidence can be addressed, but measurement precision deteriorates due to inherent estimation errors
Solution Approach 1:
The patent replaces statistical estimation methods with direct physical or signal-based detection mechanisms. Instead of using statistical algorithms to guess at coincidence errors, the system uses direct signal analysis, pulse width measurement, or sensor arrays to physically detect and identify coincident particles, eliminating the estimation errors inherent in statistical approaches.
Solution Approach 2:
The patent introduces intermediary detection mechanisms between the particles and the final measurement. These intermediaries such as additional sensors, pre-processing stages, or signal conditioning circuits detect coincidence conditions before they affect the main measurement, allowing for accurate identification and handling of coincident events without relying on post-hoc statistical correction.
3Reliability
If area-to-peak ratio methods are used to detect coincidence, then coincidence can be identified, but measurement precision deteriorates for particles of varied sizes and shapes
Solution Approach 1:
The patent employs multi-functional measurement parameters that can universally detect coincidence across different particle types. Instead of relying on a single ratio method that works best for specific particle geometries, the system uses multiple complementary parameters (such as pulse width, rise time, fall time, or multiple sensor readings) that can identify coincidence regardless of particle size, shape, or composition.
Solution Approach 2:
The patent changes the measurement parameters used for coincidence detection to be more robust across different particle types. Rather than using fixed area-to-peak ratios, the system dynamically adjusts detection parameters such as threshold levels, time windows, or signal processing characteristics to accommodate varying particle characteristics while maintaining consistent coincidence detection accuracy.
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 approach significantly improves the accuracy of particle analysis by reducing noise and errors in scatter plots and histograms, enhancing the precision of cell classification and particle counting, particularly in high-concentration samples where coincidence is prevalent.
Implementation Method 1
direct current (DC) which obeys the Coulter Principle
Implementation Method 2
Light Scatter (LS)
Implementation Method 3
ultrasound
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
Methods and systems substantially eliminate data representative of coincident events from particle analyzer data. A fluid sample containing particles for analysis is prepared. Using an electrical or optical measurement device, signals are sensed. Each signal corresponds to events detected in a sub-sample of the fluid sample flowing through a measurement region in the particle analyzer. The existence of coincidence in the events is determined based on measuring a peak and first and second points of each of the signals. The first and second points have a signal value corresponding to a predetermined portion of the peak. Results data based upon the coincident events and non-coincident events is generated. The results data is then analyzed. In various examples, the method is applicable to a variety of particle types, and may be implemented on different types of particle analyzers including hematology analyzer and flow cytometers.