Analysis of ethanol-glucose mixtures by two microbial sensors: application of 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 sensors is often their inability to distinguish between different substrates of interest. This paper demonstrates that it is possible to use sensors entirely based upon whole microbial cells to selectively measure ethanol and glucose in mixtures. Amperometric sensors were constructed using immobilized cells of either Gluconobacter oxydans or Pichia methanolica. The bacterial cells of G. oxydans were sensitive to both substrates, while the yeast cells of P. methanolica oxidized only ethanol. Using chemometric principles of polynomial approximation, data from both of these sensors were processed to provide accurate estimates of glucose and ethanol over a concentration range 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 network, 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.