Detection and Identification of Microorganisms in Mixed Cultures by Nanoparticle-Induced Nanospr Enhanced FTIR Spectroscopy and Chemometrics
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Since 1905, the Department of Agricultural Engineering, now the Department of Agricultural and Biosystems Engineering (ABE), has been a leader in providing engineering solutions to agricultural problems in the United States and the world. The department’s original mission was to mechanize agriculture. That mission has evolved to encompass a global view of the entire food production system–the wise management of natural resources in the production, processing, storage, handling, and use of food fiber and other biological products.
History
In 1905 Agricultural Engineering was recognized as a subdivision of the Department of Agronomy, and in 1907 it was recognized as a unique department. It was renamed the Department of Agricultural and Biosystems Engineering in 1990. The department merged with the Department of Industrial Education and Technology in 2004.
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1905–present
Historical Names
- Department of Agricultural Engineering (1907–1990)
Related Units
- College of Agriculture and Life Sciences (parent college)
- College of Engineering (parent college)
- Department of Industrial Education and Technology, (merged, 2004)
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Abstract
Routine identification of pathogenic microorganisms predominantly based on nutritional and biochemical tests is a time-consuming process; the delay may lead to fatal consequences at times. In this work, nanoparticle-induced nanoSPR enhanced IR spectroscopy was used in conjunction with a background elimination data processing algorithm to directly identify microorganisms in mixed cultures. It was demonstrated that the microbial composition of mixtures of different E. coli strains could be identified with 100% accuracy. The procedure was also applied to determine the presence or absence of pathogenic microorganisms in a simple but real food matrix (apple juice). Results indicated that microorganisms in a cocktail of up to eight different species suspended in an apple juice matrix could be identified for its presence or absence with 100% accuracy.