Blood corpuscle classification system based on flow-type super-continuum spectrum ringdown spectroscopy and method thereof

Abstract

The invention discloses a blood corpuscle classification system based on a flow-type super-continuum spectrum ringdown spectroscopy and a method thereof. The method comprises the following steps: preseting the detection cell number value M by the system, starting counting, then performing detection and analysis on a ringdown spectroscopy signal of a super-continuum spectrum segment in each single cell in cell flow to obtain the ringdown time corresponding to each wavelength of the whole spectrum segment in each single cell, stopping the ringdown spectrum detection when the resistance number reaches the preset value M, calculating the ringdown time corresponding to each wavelength in whole spectrum segment of M cells and taking as the cell classification characteristic, and performing the discrimination for different kinds animal blood corpuscles. According to the invention, a flow-type cell analysis and a laser super-continuum spectrum ringdown spectroscopy technology are combined for providing enough characteristic parameter for classification, and can improve the discriminating classification effect of routine animal blood corpuscles.

Description

technical field [0001] The invention relates to a blood cell classification technology, in particular to a blood cell classification system and method based on flow supercontinuum ring-down spectrum. Background technique [0002] Flow cytometry is a technique for rapidly measuring the biological properties of individual cells or organelles in a liquid flow system, and separating and collecting characteristic cells or organelles. Resistance method, fluorescence method, light scattering method, light absorption method, etc. are often used in flow cytometry. These conventional methods do not make use of laser spectrum analysis. In analyzing the characteristics of cells, they mainly rely on the differences in their electrical properties and light scattering properties for classification. It is difficult to distinguish the same kind of cells from different animals. Example: When distinguishing and identifying red blood cells of different animals, it is difficult to establish suf...

AI Technical Summary

Problems solved by technology

These conventional methods do not make use of laser spectrum analysis, and in the analysis of cell characteristics, they mainly rely on the differences in their electrical properties and light scattering properties to classify, and it is difficult to distinguish the same kind of cells from different animals.

Example: When distinguishing and identifying red blood cells from different animals, it is difficult to establish sufficient distinguishing and identifying features due to the similar electrical properties and light scattering properties of red blood cells

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