Rapid and non-invasive quantification of metabolic substrates in biological cell suspensions using non-linear dielectric spectroscopy with multivariate calibration and artificial neural networks. Principles and applications

Abstract

By studying the non-linear effects of membranous enzymes on an applied oscillating electromagnetic field, non-linear dielectric spectroscopy has previously been shown to produce qualitative information which is indicative of the metabolic state of a variety of organisms. In this study, we extend the method of non-linear dielectric spectroscopy to the production of data sets suitable for use with supervised multivariate analysis methods, in order to allow quantitative prediction of analyte concentrations in unknown samples, again using the alteration in the non-linear dielectric profile produced by these analytes via the metabolism of the cell (as effected via the operation of their membranous enzymes). Non-stationarity in the extent of non-linear electrode polarization can interfere with the measurement of non-linear dielectric spectra; various electrode materials and configurations have been tested for their suitability for use in non-linear dielectric spectroscopy. We exploit partial least-squares regression and artificial neural networks for the multivariate analysis of non-linear dielectric data recorded from yeast cell suspensions, and schemes for preprocessing these data to improve the precision of the prediction of analyte levels are developed and optimized. The resulting analytical methods are applied to the prediction of glucose levels in sheep and human blood, by both invasive and non-invasive measurements, and to the non-invasive measurement of process variables during a microbial fermentation.