Objective The somatic cell count (SCC) in raw milk is an important basis for determining whether a cow is suffering from mastitis. Identifying cows with mastitis by testing the SCC, then isolating and treating them as early as possible, can effectively prevent the spread of bacteria in the herd to reduce consequential economic losses. However, traditional methods may lead to uneven distribution of somatic cells during milk sampling, such as cell adhesion settlement, and unrepresentative somatic cell count due to lack of matching imaging system. In this paper, a method is proposed which is based on the nine-cell grid microfluidic chip to make somatic cell evenly distributed and develop a two degree of freedom displacement platform equipped with a micro lens to improve the counting accuracy .
Methods Firstly, a simulation was performed to verify the uniformity of the somatic cell distribution within the chip observation cavities (Fig.1). And based on the simulation results, a nine palace grid microfluidic chip was prepared (Fig.2). Secondly, a two-degree-of-freedom displacement platform (Fig.6) equipped with a micro-camera lens is developed, which can automatically take images of the nine observation cavities of the chip, making image acquisition more convenient. Finally, somatic cells were counted by image processing (Fig.3), so as to verify the uniformity of somatic cell distribution, obtain the counting accuracy, and judge the health status of cow udder.
Results and Discussions 20 cows were randomly selected from local pastures to verify the performance of the proposed method. From the data in Tab.1, it can be seen that the standard deviation coefficient of the SCC in each group of nine images is less than or equal to 1.61%, which verifies the uniformity of the distribution of somatic cells in the nine observation cavities of the microfluidic chip. The system ensures the uniform distribution of somatic cells and renders the taken samples more representative. As can be seen from Fig.9(b), the maximum absolute value of the relative error of the system of automatic counting is 2.93%, the minimum value is 0.53%, and the average value is 1.72%. The maximum relative count error of the automatic counting is obtained as ±2.93%. The system has a very high accuracy for automatic counting.
Conclusions The experimental results show that the somatic cell counting system developed in this paper can make the somatic cell distribution in fresh milk more uniform and count more accurately. The standard deviation coefficient of the number of somatic cells in each group of nine images was less than or equal to 1.61%, and the smaller the standard deviation coefficient is, the more uniform the distribution of somatic cells is. The accuracy of the automatic system counts ranged 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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