IDENTIFICATION OF SALMONELLA‐INOCULATED BEEF USING A PORTABLE ELECTRONIC NOSE SYSTEM

Abstract

ABSTRACT A commercially available Cyranose‐320TM (Cyrano Sciences, Pasadena, CA) conducting polymer–based electronic nose system was used to analyze the volatile organic compounds emanating from vacuum‐packaged beef strip loins (Longissimus Lamborum) that were repackaged simulating retail store conditions and stored at 4 and 10°C after inoculating them with Salmonella Typhimurium. 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 models were developed for the captured signals, which were scaled and not scaled. The performances of the developed models were validated by two different methods, leave‐one‐out cross validation and bootstrapping. The developed models classified the meat samples based on the Salmonella population into “No Salmonella” (microbial counts < 0.7 log10 cfu/g) and “Salmonella” (microbial counts ≥ 0.7 log10 cfu/g). Overall, scaling the signals improved the classification accuracies obtained. The quadratic discriminant‐based classification models validated by the bootstrapping technique performed better than the LDA‐based models. For the meat samples stored at 10°C, the highest classification accuracy obtained by the QDA method with bootstrapping validation was 80.5% when the signals were scaled. For the samples stored at 4°C, the QDA method provided the highest classification accuracy of 87.3% using bootstrapping validation. The results obtained prove that the electronic nose system could identify meat samples contaminated with S. Typhimurium at a population concentration level of 0.7–2.6 log10 cfu/g.