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

• Published in: Food and bioprocess technology Vol. 5; no. 4; pp. 1206 - 1219
• Main Authors: Balasubramanian, Sundar, Amamcharla, Jayendrakumar, Panigrahi, Suranjan, Logue, Catherine M., Marchello, Martin, Sherwood, Julie S.
• Format: Journal Article
• Language: English
• Published: New York Springer-Verlag 01.05.2012; Springer Nature B.V
• Subjects: Agriculture; Beef; Biotechnology; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Chromium; Classification; Conducting polymers; Contamination; Discriminant analysis; Electronic nose; Electronic noses; Food contamination; Food Science; Gas sensors; Meat; Metal oxides; Metals; Microorganisms; Model accuracy; Olfactory system; Original Paper; oxides; Polymers; Salmonella; Salmonella typhimurium; Screening; Sensors; System effectiveness; Temperature effects; Meat contamination; Sensor fusion; Discriminant analysis; Food safety; Intelligent sensors; Electronic nose
• ISSN: 1935-5130; 1935-5149
• DOI: 10.1007/s11947-010-0444-z

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 .