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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 various parameter settings were tried and compared. Overall, 304 different classifier variations were tested, which differed from each other in at least one parameter value. The results showed that Quadratic Discriminant Analysis, MLPClassifier, C-Support Vector Classification (SVC), and several different single hidden layer Neural Networks yielded lower misclassification rates applied to the raw data than k-NN with Euclidean distance. Both MLP Classifiers and SVC yielded misclassification rates of less than 3% when applied to raw data. Furthermore, when applied both to the raw data and the data preprocessed by principal component analysis that explained at least 95% or 99% of the total variance in the raw data, Quadratic Discriminant Analysis outperformed the other classifiers. The findings of this study can be used for further algorithm development. They can also be used, for example, to improve the estimation of storage times of fruit.

SUBMITTER: Minaev G

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

REPOSITORIES: biostudies-literature

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

Minaev Georgy G Müller Philipp P Salminen Katri K Rantala Jussi J Surakka Veikko V Visa Ari A

Sensors (Basel, Switzerland) 20210107 2

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 <i>k</i>-Nearest Neighbour (<i>k</i>-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measu ...[more]

PMID: 33430310

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Project description:In this report, we explored the benefits of cyclic ion mobility (cIM) mass spectrometry in the analysis of isomeric post-transcriptional modifications of RNA. Standard methyl-cytidine samples were initially utilized to test the ability to correctly distinguish different structures sharing the same elemental composition and thus molecular mass. Analyzed individually, the analytes displayed characteristic arrival times (tD ) determined by the different positions of the modifying methyl groups onto the common cytidine scaffold. Analyzed in mixture, the widths of the respective signals resulted in significant overlap that initially prevented their resolution on the tD scale. The separation of the four isomers was achieved by increasing the number of passes through the cIM device, which enabled to fully differentiate the characteristic ion mobility behaviors associated with very subtle structural variations. The placement of the cIM device between the mass-selective quadrupole and the time-of-flight analyzer allowed us to perform gas-phase activation of each of these ion populations, which had been first isolated according to a common mass-to-charge ratio and then separated on the basis of different ion mobility behaviors. The observed fragmentation patterns confirmed the structures of the various isomers thus substantiating the benefits of complementing unique tD information with specific fragmentation data to reach more stringent analyte identification. These capabilities were further tested by analyzing natural mono-nucleotide mixtures obtained by exonuclease digestion of total RNA extracts. In particular, the combination of cIM separation and post-mobility dissociation allowed us to establish the composition of methyl-cytidine and methyl-adenine components present in the entire transcriptome of HeLa cells. For this reason, we expect that this technique will benefit not only epitranscriptomic studies requiring the determination of identity and expression levels of RNA modifications, but also metabolomics investigations involving the analysis of natural extracts that may possibly contain subsets of isomeric/isobaric species.

Dataset Information

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

Publications

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

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