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:

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... ave-1-out crossvalidation, 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. ABSTRACT. A commercially available Cyranose−320t 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 log 10 cfu/g) and "spoiled" (microbial counts > 6.0 log 10 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.