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Based on bioimpedance spectroscopy technology, a method of automatically identifying the cell suspension concentration is proposed. This method combines multiple linear regression algorithm and bioimpedance spectroscopy technology, which can identify the concentration of cell suspension quickly and accurately. Firstly, a strategy of random distribution of cell locations is proposed to simulate the true existence of cells. Secondly, 2400 groups of normal, cancerous and mixed cell models with different concentrations are generated by numerical simulation and their bioimpedance spectroscopy data are calculated.Thirdly, the multiple linear regression algorithm (MLR), support vector machine (SVM), and gradient boosting regression algorithm (GBR) are used to identify the concentration of cancerous cells. The simulation results show that the MLR is the best regression model for cell suspension concentration identification and its average goodness of fit and mean square error are 0.9997 and 0.0008respectively. Finally, the MLR is applied to the identification of red blood cell suspensions with different concentrations, the experimental results show that the average goodness of fit and mean square error are 0.9998 and 0.0079, respectively, indicating that this method has a greater ability to identify cell suspension concentrations.
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Keywords:
- cell concentration measurement/
- bioimpedance spectroscopy (BIS)/
- multiple linear regression (MLR)/
- random distribution strategy of cell
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] -
参数 正常B细胞 癌变B细胞 电导率σ/ (S·m–1) 背景溶液 0.6 0.6 细胞膜 5.6 × 10–5 9.1 × 10–6 细胞质 1.31 0.48 核膜 1.11 × 10–2 4.4 × 10–3 核质 2.04 1.07 相对介电常数ε 背景溶液 80 80 细胞膜 12.8 9.8 细胞质 60 60 核膜 106 60.3 核质 120 120 几何参数/μm 仿真区域L×L 20 × 20 20 × 20 电极长度l 4 4 细胞半径R1 3.3 5.2 细胞核半径R2 2.8 4.4 细胞膜厚d1 0.007 0.007 核膜厚度d2 0.04 0.04 
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MLR SVR GBR R2 MSE R2 MSE R2 MSE 1 0.9999 0.0006 0.0006 0.0257 0.9994 0.0066 2 0.9995 0.0009 0.0009 0.0313 0.9985 0.0171 3 0.9998 0.0006 0.0006 0.0336 0.9986 0.0179 4 0.9999 0.0012 0.0012 0.0267 0.9986 0.0156 5 0.9996 0.0008 0.0008 0.0335 0.9987 0.0176 平均数 0.9997 0.0008 0.0008 0.0302 0.9988 0.0150 DownLoad:
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浓度/% 0 10 15 20 25 30 40 50 平均绝对误差 0.0104 0.0124 0.0113 0.0134 0.0129 0.0165 0.0158 0.0161 DownLoad:
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1 2 3 4 5 平均数 R2 1.0000 0.9998 0.9999 0.9996 0.9999 0.9998 MSE 0.0015 0.0021 0.0152 0.0035 0.0173 0.0079 DownLoad:
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[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]
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