A hybrid Genetic Algorithm and Levenberg–Marquardt (GA-LM) method for cell suspension measurement with electrical impedance spectroscopy

Li Wang, Songpei Hu, Kai Liu, Bai Chen, Hongtao Wu, Jiabin Jia, Jiafeng Yao

Research output: Contribution to journalArticlepeer-review

Abstract

A hybrid Genetic Algorithm (GA) and Levenberg–Marquardt (GA–LM) method is proposed for cell suspension measurement with electrical impedance spectroscopy. This algorithm combines the GA with global search ability and Levenberg–Marquardt (LM) algorithm with local search ability, which has the advantages of high accuracy and high robustness. First, GA–LM is compared with GA and LM algorithm separately by ideal simulation. Second, Gaussian noise is added to the ideal simulation data. The anti-noise ability of the GA–LM is discussed. Finally, experiments are conducted to verify the practicability of the proposed GA–LM method. In the experiment, GA–LM is used to fit the impedance spectrum of yeast suspensions with different volume fractions and active states. The results show that the GA–LM algorithm can converge to the real value that is set in the simulation under ideal numerical simulation conditions. In the simulation within 2% noise level, the mean relative error of the parameter solution is less than 4%, and the root mean square error of the fitting is less than 0.4. This method also performs well in fitting of the experimental data. In addition, the electric double layer resistance and cell membrane capacitance are selected as the main indicators for the identification of yeast suspension concentration and activity, respectively.
Original languageEnglish
JournalReview of Scientific Instruments
Volume19
Issue number12
Publication statusPublished - 2020

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