A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry.

ABSTRACT: The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. Its classification accuracy was compared with the accuracies of the k-NN with 66 alternative distance metrics. In addition, 18 other classifiers were tested with raw eNose data. For each classifier var

SUBMITTER: Minaev G 

PROVIDER: S-EPMC7825773 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature