Particle Sorting Using ML Spectral Discrimination in Flow Cytometry
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Solution Overview
Problem
The spectral type analysis method in flow cytometry requires significant computational resources and time, making it unsuitable for real-time particle sorting.
Innovation Solution
An information processing apparatus and method that utilizes machine learning to quickly discriminate between particles based on fluorophore information, allowing for immediate sorting without the need for extensive computational analysis during the sorting phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral type analysis method is used to accurately analyze fluorescence with high wavelength resolution, then measurement precision is improved, but computation time increases making real-time particle sorting impossible
Solution Approach 1:
The patent pre-calculates and stores reference fluorescence spectra for multiple fluorophores before the actual particle analysis. During real-time sorting, the system compares detected spectra against these pre-stored references using unmixing calculations, avoiding the need to perform full spectral decomposition during the time-critical sorting phase. This preliminary preparation enables rapid identification of fluorophore combinations while maintaining high measurement precision.
2Productivity
If spectral type analysis with unmixing calculation is performed in real-time, then productivity for particle sorting is improved, but device complexity increases due to additional computational requirements
Solution Approach 1:
The patent creates simplified spectral signatures or characteristic patterns from full spectral data during a calibration phase. These copied representations are stored and used for rapid pattern matching during real-time particle sorting. Instead of performing complex unmixing calculations on every particle, the system compares against pre-extracted spectral fingerprints, significantly reducing computational complexity while maintaining sorting speed and 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
Enables real-time particle sorting by leveraging machine learning to distinguish process targets, reducing computational load and enabling immediate sorting decisions based on fluorescence detection.
Implementation Method 1
detecting fluorescence emitted from the particles irradiated with excitation light
Implementation Method 2
detecting fluorescence emitted from the particles irradiated with excitation light
Data Source
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AI summary
[Object] To provide an information processing apparatus, a particle sorting system, a program, and a particle sorting method that practice a spectral type analysis usable for sorting particles. [Solving Means] The information processing apparatus according to an aspect of the present technology includes: an analysis unit; a learning unit; and a discrimination unit. The analysis unit calculates fluorophore information indicating respective amounts of luminescence of a plurality of types of fluorophores on the basis of detection data indicating amounts of luminescence of fluorescence at respective wavelength bands, the fluorescence having been emitted from a particle irradiated with excitation light, discriminates whether or not to treat the particle as a process target in accordance with the fluorophore information, and generates teaching data by associating a result of the discrimination with the detection data. The learning unit applies a machine learning algorithm to the teaching data, learns a characteristic of the detection data discriminated as the process target, and generates dictionary data including a result of the learning. The discrimination unit discriminates whether or not the particle whose detection data has been acquired is the process target on the basis of the dictionary data when the detection data is supplied.