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United States Department of Agriculture

Agricultural Research Service

Title: Analysis of Ethanol-Glucose Mixtures by Two Microbial Sensors: Application of Chemometrics and Artificial Neural Networks for Data Processing

item Gordon, Sherald
item Greene, Richard
item Leathers, Timothy
item Reshetilov, Anatoly - RUSSIAN ACADEMY OF SCI

Submitted to: Biosensors and Bioelectronics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 12, 2001
Publication Date: N/A

Interpretive Summary: Low-value agricultural residues such as corn fiber can be converted to sugars and fermented to such value-added products as ethanol. Useful new technologies are needed to detect and monitor these sugars and their fermentation products in real-time under field and factory conditions. Currently available methods suffer from slow response times, high maintenance and high cost. Biosensors (electronic instruments utilizing biological components) are rugged, inexpensive, reliable and simple to operate. However, biosensors are limited in their ability to discriminate between certain analytes of interest, such as glucose and ethanol. This work will be of interest to those developing value-added products from agricultural commodities and byproducts, and will in turn benefit farmers by promoting new and expanded markets for their products.

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 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 to 8.0 mM (coefficients of determination, R squared = 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 to 10.0 mM, (R squared = 0.99 for both substrates). The described methodology extends the sphere of utility for microbial sensors.

Last Modified: 4/19/2015
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