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United States Department of Agriculture

Agricultural Research Service

Title: Application of Chemometrics and Ann in the Analysis of Ethanol-Glucose Mixtures by Two Microbial Sensors

item Lobanov, Alexei - PUSHCHINO STATE UNIV
item Borisov, Ivan - PUSHCHINO STATE UNIV
item Gordon, Sherald
item Greene, Richard
item Leathers, Timothy
item Reshetilov, Anatoly - RUSSIAN ACAD OF SCIENCES

Submitted to: Biosensors World Congress
Publication Type: Abstract Only
Publication Acceptance Date: February 2, 2000
Publication Date: N/A

Technical Abstract: 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 presentation demonstrates that it is possible to use sensors based entirely 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 to 8.0 mM (coefficient of determination, R**2 = 0.99 for ethanol and 0.98 for glucose). When data were processed using an artificial neural network (ANN), glucose and ethanol were accurately estimated over a range of 1.0 to 10.0 mM (R**2 = 0.99 for both substrates). The proposed method should extend the sphere of useful microbial sensor applications.

Last Modified: 4/22/2015
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