Investigation of Different Gas Sensor-Based Artificial Olfactory Systems for Screening Salmonella typhimurium Contamination in Beef

Sundar Balasubramanian, Jayendrakumar Amamcharla, Suranjan Panigrahi, Catherine M. Logue, Martin Marchello, Julie S. Sherwood

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Two different electronic nose systems (metal oxide and conducting polymer based) were used to identify Salmonella typhimurium contaminated beef strip loin samples (stored at two temperatures). The sensors present in the two systems were ranked based on their Fisher criteria of ranking to evaluate their importance in discriminant analysis. The most informative sensors were then used to develop linear discriminant analysis and quadratic discriminant analysis-based classification models. Further, sensor signals collected from both the sensor systems were combined to improve the classification accuracies. The developed models classified meat samples based on the Salmonella population into "No Salmonella" (microbial counts < 0. 7 log 10 cfu/g) and "Salmonella inoculated" (microbial counts ≥ 0. 7 log 10 cfu/g). The performances of the developed models were validated using leave-1-out cross-validation. Classification accuracies of 80% and above were observed for the samples stored at 10 °C using the sensor fusion approach. However, the classification accuracies were relatively low for the meat samples stored at 4 °C when compared to the samples stored at 10 °C. The results indicate that the electronic nose systems could be effectively used as a first stage screening device to identify the meat samples contaminated with S. typhimurium.

Original languageEnglish (US)
Pages (from-to)1206-1219
Number of pages14
JournalFood and Bioprocess Technology
Volume5
Issue number4
DOIs
StatePublished - May 2012
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements The authors would like to express their profound gratitude to the United States Department of Agriculture-Cooperative State Research, Education and Extension Service (USDA-CSREES) for their financial support for this research.

Keywords

  • Discriminant analysis
  • Electronic nose
  • Food safety
  • Intelligent sensors
  • Meat contamination
  • Salmonella typhimurium
  • Sensor fusion

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