Flow Cytometer Photodetector Noise Tracking With Moving Mean-Squared Error
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Solution Overview
Problem
Existing flow cytometry systems face challenges in accurately determining baseline noise in photodetectors, which affects the signal-to-noise ratio and the characterization of sample components due to variations in light scattering and emission caused by morphologies, absorptivity, and the presence of fluorescent labels.
Innovation Solution
A method and system for determining baseline noise in photodetectors by irradiating a sample in a flow stream, detecting light, generating data signals, and calculating a moving average mean squared error to quantify baseline noise, using integrated circuits and non-transitory computer readable storage media to process the signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional baseline noise measurement methods are used in flow cytometry, then the measurement process is simple, but the signal-to-noise ratio is poor and measurement precision is low
Solution Approach 1:
The system performs preliminary actions by continuously measuring baseline noise from particle-free regions of the flow stream before actual sample analysis. This preliminary baseline measurement is stored and used for subsequent signal processing, allowing the system to establish a reference noise level that accounts for laser drift, electronic noise, and optical fluctuations occurring during the measurement process.
Solution Approach 2:
The system implements feedback by continuously monitoring the baseline noise signal from the particle-free flow stream and using this information to dynamically adjust and subtract noise from the actual particle measurement signals. The baseline noise measurement feeds into the signal processing algorithm, which subtracts the measured baseline from the total signal to improve measurement precision.
2Reliability
If baseline noise is not continuously measured and corrected, then the system operation is simple, but the signal-to-noise ratio decreases and component characterization accuracy is poor
Solution Approach 1:
The system maintains continuity of useful action by continuously measuring baseline noise throughout the entire measurement process rather than performing discrete measurements. The photodetector continuously monitors the particle-free flow stream, and the baseline noise is continuously updated and applied to correct particle measurements in real-time, ensuring consistent signal-to-noise ratio improvement across all measurements.
Solution Approach 2:
The measurement process is segmented into distinct functional regions: a particle-free region for baseline noise measurement and a particle-containing region for sample analysis. By spatially separating these functions within the flow stream, the system can simultaneously perform baseline characterization and sample measurement without interference, maintaining both reliability and productivity.
3Measurement precision
If light detection is performed without baseline noise correction, then the detection process is fast, but variations in light scattering and emission cause measurement errors
Solution Approach 1:
The system merges the baseline noise measurement and sample detection processes into a single integrated measurement flow. Both the particle-free baseline signal and particle-containing sample signal are detected simultaneously by the same photodetector system, with the baseline measurement occurring in the same optical path but from a different spatial region. This merging eliminates the need for separate measurement steps, maintaining fast processing while improving precision through baseline correction.
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
Enhances the signal-to-noise ratio in flow cytometry by up to 99% and provides real-time, sample-specific measurements of baseline noise, accounting for factors like laser drift and thermal noise, improving the accuracy of component characterization.
Implementation Method 1
detecting light with the photodetector from the irradiated flow stream
Implementation Method 2
light can be scattered by the sample, transmitted through the sample as well as emitted by the sample
Implementation Method 3
Light from the light source can be detected as scatter or by transmission spectroscopy or can be absorbed by one or more components in the sample and re-emitted as luminescence
Data Source
Figure 1
Figure 2AA~2AD
Figure 2B
AI summary
Aspects of the present disclosure include methods for determining baseline noise of a photodetector (e.g., in a light detection system of a particle analyzer). Methods according to certain embodiments include irradiating a sample having particles in a flow stream, detecting light with the photodetector from the irradiated flow stream, generating data signals from the detected light and calculating a moving average mean squared error of the generated data signals to determine the baseline of the photodetector. Systems (e.g., particle analyzers) having a light source and a light detection system that includes a photodetector for practicing the subject methods are also described. Integrated circuits and non-transitory computer readable storage medium are also provided.