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Title: DETECTION OF ETHANOL IN A TWO-COMPONENT GLUCOSE/ETHANOL MIXTURE USING A NONSELECTIVE MICROBIAL SENSOR AND A GLUCOSE ENZYME ELECTRODE

Author
item RESHETILOV, ANATOLY - RUSSIAN ACAD OF SCI
item LOBANOV, ALEX - MOSCOW STATE UNIVERSITY
item MOROZOVA, NATALIA - PUSCHINO STATE UNIVERSITY
item Gordon, Sherald
item Greene, Richard
item Leathers, Timothy

Submitted to: Biosensors and Bioelectronics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/25/1998
Publication Date: N/A
Citation: N/A

Interpretive Summary: Valuable sugars can be recovered from low-value agricultural residues such as corn fiber and fermented to value-added products such as ethanol. Practical 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 bacterial cells) are rugged, inexpensive, reliable and simple to operate. However, biosensors are limited in their ability to discriminate between certain biomaterials of interest, such as glucose and ethanol. This work will be of interest to those developing new uses and value-added products from agricultural commodities and byproducts and will in turn benefit farmers by fostering new and expanded markets for their products.

Technical Abstract: Chemometric theory was applied to a microbial sensor for determinations of ethanol in the presence of glucose. Microbial sensors, consisting of Gluconobacter oxydans cells immobilized on Clark-type amperometric oxygen electrodes, exhibited good sensitivity but low selectivity toward ethanol and glucose. An Eksan-G commercial glucose analyzer was used as a second sensor for multivariate calibration and analyses. Microbial sensors exhibited nearly complete additivity for total glucose plus ethanol concentrations from 0.0 to 0.6 mM. Within this linear range, chemometric analyses provided estimates of ethanol concentration with measurement errors of less than 8%. Multivariate calibration thus is a promising approach to enhance the usefulness of microbial sensors.