基于改进电阻抗技术的酵母菌细胞活性检测

Yeast cell activity detection based on improved electrical impedance technology

摘要:

目的 为了使用神经网络快速预测并探究酵母菌细胞活性与电阻抗之间的关系。方法 选用电阻抗技术对不同浓度活性酵母菌细胞进行测试，获得死亡细胞的阻抗值。基于灰狼算法优化的BP神经网络的预测模型，探究酵母菌活性与电阻抗在不同频率下的关系。结果 酵母菌活性与电阻抗存在复杂的非线性关系，在一定频率下随着酵母菌细胞浓度的增加，酵母菌细胞悬浮液的电阻抗也随之增加。发现活性细胞在相对频率下的电阻抗要高于死亡细胞。基于灰狼算法优化的BP神经网络预测模型误差明显小于BP神经网络，且拟合值更加接近真实值。结论 该文所提方法能有效解决电阻抗与酵母菌活性的非线性关系，能够为电阻抗技术在细胞检测领域的应用提供参考。

关键词:酵母菌;细胞检测;电化学阻抗谱;BP神经网络;灰狼优化算法

Abstract:

Objective To rapidly predict and explore the relationship between yeast cell activity and impedance using neural networks.Methods The impedance values of dead yeast cells were obtained by testing yeast cells with different concentrations using impedance technology. A prediction model based on BP neural networks optimized by grey wolf algorithm was employed to explore the relationship between yeast cell activity and impedance at different frequencies.Results There existed a complex non-linear relationship between yeast cell activity and impedance, where the impedance of yeast cell suspension increased with the increase of yeast cell concentration at certain frequencies. It was observed that the impedance of active cells was higher than that of dead cells at relative frequencies. The error of the BP neural network prediction model optimized by the grey wolf algorithm was significantly smaller than that of the BP neural network, and the fitted values were closer to the actual values.Conclusions The methods proposed in this study effectively address the non-linear relationship between impedance and yeast cell activity, providing valuable insights for the application of impedance technology in the field of cell detection.

Key words:yeast;cell detection;electrochemical impedance spectroscopy;BP neural network;grey wolf optimization algorithm