期刊
MICROMACHINES
卷 13, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/mi13122173
关键词
dairy mastitis; microfluidic chip; image processing; cell counting; standard deviation coefficient
类别
资金
- Hebei Provincial Natural Science Fund Project [B2021202038]
- Distinguish Young Scholars in Tianjin 2018 3rd Round [180191]
- Hebei Science and Technology Foundation [19271707D]
- Hebei Natural Science Foundation [E2020202101, F2021202001]
- Department of Human Resources and Social Security of Hebei Province [C20200314, C20210337]
This research proposes a method based on a microfluidic chip to obtain a uniform distribution of somatic cells and improve counting accuracy. Experimental results show the significance of this method for the detection and prevention of mastitis.
The somatic cell count (SCC) in raw milk is an important basis for determining whether a cow is suffering from mastitis. To address the problem of an uneven distribution of somatic cells due to cell-adherent sedimentation, among other reasons, during milk sampling, which in turn results in unrepresentative somatic cell counting, a method is proposed for obtaining a uniform distribution of somatic cells and improving the counting accuracy based on a nine-cell grid microfluidic chip. Firstly, a simulation was performed to verify the uniformity of the somatic cell distribution within the chip observation cavities. Secondly, a nine-cell grid microfluidic chip was prepared and a negative-pressure injection system integrating staining and stirring was developed to ensure that the somatic cells were uniformly distributed and free from air contamination during the injection process. As well as the structure of the chip, a microscopic imaging system was developed, and the nine chip observation cavities were photographed. Finally, the somatic cells were counted and the uniformity of the somatic cell distribution was verified using image processing. The experimental results show that the standard deviation coefficient of the SCC in each group of nine images was less than 1.61%. The automatic counting accuracy of the system was between 97.07% and 99.47%. This research method lays the foundation for the detection and prevention of mastitis in cows.
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