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- W2480451102 abstract "Abstract Three portable electronic noses (PEN3, Cyranose 320, and InSniff) combined with multivariate analysis were attempted to classify rice according to four groups. The interest was to classify rice samples to their actual group based on odor sample. Signals based on the instruments’ sensor responses were acquired and further analyzed using four classification techniques such as support vector machine (SVM), k -nearest–neighbor ( k -NN), multilayer perceptron (MLP), and radial basis function (RBF). Correct classification rate for each of the applied techniques were assessed using three error estimation approaches; leave-one-out (LOO), holdout, and k -fold. From the experiment results, we concluded that SVM is the best classification technique that can be applied for the rice samples, whereas LOO is the most suitable error estimation approach for the classification. The overall results showed that all the employed instruments are able to identify different types of rice with good classification performances." @default.
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- W2480451102 date "2016-01-01" @default.
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- W2480451102 title "Rice and the Electronic Nose" @default.
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- W2480451102 doi "https://doi.org/10.1016/b978-0-12-800243-8.00011-1" @default.
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