RAPID AND NONINVASIVE QUANTIFICATION OF METABOLIC SUBSTRATES IN BIOLOGICAL CELL-SUSPENSIONS USING NONLINEAR DIELECTRIC-SPECTROSCOPY WITH MULTIVARIATE CALIBRATION AND ARTIFICIAL NEURAL NETWORKS - PRINCIPLES ANDAPPLICATIONS

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 t he production of data sets suitable for use with supervised multivaria te analysis methods, in order to allow quantitative prediction of anal yte concentrations in unknown samples, again using the alteration in t he non-linear dielectric profile produced by these analytes via the me tabolism of the cell (as effected via the operation of their membranou s enzymes). Non-stationarity in the extent of non-linear electrode pol arization can interfere with the measurement of non-linear dielectric spectra; various electrode materials and configurations have been test ed for their suitability for use in non-linear dielectric spectroscopy . We exploit partial least-squares regression and artificial neural ne tworks for the multivariate analysis of non-linear dielectric data rec orded 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 measu rement of process variables during a microbial fermentation.