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Spoilage Identification of Beef Using an Electronic Nose System
Transactions of the ASAE
  • Sundar Balasubramanian, North Dakota State University
  • Suranjan Panigrahi, North Dakota State University
  • Catherine M. Logue, North Dakota State University
  • Martin Marchello, North Dakota State University
  • Curt Doetkott, North Dakota State University
  • Huanzhong Gu, North Dakota State University
  • Julie S. Sherwood, North Dakota State University
  • Lisa K. Nolan, Iowa State University
Document Type
Article
Publication Date
1-1-2004
Abstract

A commercially available Cyranose-320. conducting polymer-based electronic nose system was used to analyze the volatile organic compounds emanating from fresh beef strip loins (M. Longisimmus lumborum) stored at 4°C and 10°C. Two statistical techniques, i.e., linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), were used to develop classification models from the collected sensor signals. The performances of the developed models were validated by two different methods: leave-1-out cross-validation, and bootstrapping. The developed models classified meat samples based on the microbial population into “unspoiled” (microbial counts <6.0 log10 cfu/g) and “spoiled” (microbial counts > 6.0 log10 cfu/g). Overall, quadratic discriminant-based classification models performed better than linear discriminant analysis based models. For the meat samples stored at 10°C, the highest classification accuracies obtained by the LDA method with leave-1-out and bootstrapping validations were 87.10% and 85.87%, respectively. On the other hand, classification by QDA and subsequent validation by leave-1-out and bootstrapping provided highest accuracies of 87.5% and 97.38%, respectively. For samples stored at 4°C, the LDA method provided highest classification accuracies of 79.17% and 85.64% using leave-1-out and bootstrapping validation, respectively. When the QDA method was used, the highest classification accuracies obtained for the samples stored at 4°C were 87.50% and 98.48%, respectively, with leave-1-out and bootstrapping validations.

Comments

This article is from Transactions of the ASAE 47, no. 5 (2004): 1625–1633.

Copyright Owner
American Society of Agricultural Engineers
Language
en
Date Available
May 7, 2013
File Format
application/pdf
Citation Information
Sundar Balasubramanian, Suranjan Panigrahi, Catherine M. Logue, Martin Marchello, et al.. "Spoilage Identification of Beef Using an Electronic Nose System" Transactions of the ASAE Vol. 47 Iss. 5 (2004) p. 1625 - 1633
Available at: http://works.bepress.com/lisa_nolan/37/