Measurement and characterization of glucose in NaCl aqueous solutions by electrochemical impedance spectroscopy

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5 Citations (Scopus)


Electrochemical impedance spectroscopy (EIS) allows measuring the properties of the system as a function of the frequency as well as distinguishing between processes that could be involved: resistance, reactance, relaxation times, amplitudes, etc. Although it is possible to find related literature to in vitro and in vivo experiments to estimate glucose concentration, no clear information regarding the condition and precision of the measurements are easily available. This article first address the problem of the condition and precision of the measurements, as well as the effect of the glucose over the impedance spectra at some physiological (normal and pathological) levels. The significance of the measurements and the glucose effect over the impedance are assessment regarding the noise level of the system, the experimental error and the effect of using different sensors. Once the data measurements are analyzed the problem of the glucose estimation is addressed. A rational parametric model in the Laplace domain is proposed to track the glucose concentration. The electrochemical spectrum is measured employing odd random phase multisine excitation signals. This type of signals provides short acquisition time, broadband measurements and allows identifying the best linear approximation of the impedance as well as estimating the level of noise and non-linearities present in the system. All the experiments were repeated five times employing three different sensors from the same brand in order to estimate the significance of the experimental error, the effects of the sensors and the effect of the glucose over the impedance.
Original languageEnglish
Pages (from-to)9-18
Number of pages10
JournalBiomedical Signal Processing and Control
Publication statusPublished - 1 Nov 2014


  • Modeling
  • Electrochemical impedance spectroscopy
  • Odd random phase multisine
  • Glucose sensor
  • Best linear approximation
  • Parametric model
  • Nonparametric model
  • Rational model


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