SPOILAGE IDENTIFICATION OF BEEF USING AN ELECTRONIC NOSE SYSTEM

Abstract

A commercially available Cyranose-320. conducting polymer-based electronic nose system was used to analyzethe volatile organic compounds emanating from fresh beef strip loins (M. Longisimmus lumborum) stored at 4C and 10C.Two statistical techniques, i.e., linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), were usedto develop classification models from the collected sensor signals. The performances of the developed models were validatedby two different methods: leave-1-out cross-validation, and bootstrapping. The developed models classified meat samplesbased 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 discriminantanalysis based models. For the meat samples stored at 10C, the highest classification accuracies obtained by the LDAmethod 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 4C, 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 highestclassification accuracies obtained for the samples stored at 4C were 87.50% and 98.48%, respectively, with leave-1-outand bootstrapping validations.