Analysis of ethanol-glucose mixtures by two microbial sensors: applicationof chemometrics and artificial neural networks for data processing

Although biosensors based on whole microbial cells have many advantages in terms of convenience, cost and durability, a major limitation of these sens ors is often their inability to distinguish between different substrates of interest. This paper demonstrates that it is possible to use sensors entir ely based upon whole microbial cells to selectively measure ethanol and glu cose in mixtures. Amperometric sensors were constructed using immobilized c ells of either Gluconobacter oxydans or Pichia methanolica. The bacterial c ells of G. oxydans were sensitive to both substrates, while the yeast cells of P. methanolica oxidized only ethanol. Using chemometric principles of p olynomial approximation, data from both of these sensors were processed to provide accurate estimates of glucose and ethanol over a concentration rang e of 1.0-8.0 mM (coefficients of determination, R-2 = 0.99 for ethanol and 0.98 for glucose). When data were processed using an artificial neural netw ork, glucose and ethanol were accurately estimated over a range of 1.0-10.0 mM (R-2 = 0.99 for both substrates). The described methodology extends the sphere of utility for microbial sensors. Published by Elsevier Science B.V .