Multivariate classification of Southern Brazilian table wines
Abstract In this work, we evaluated the performance of several classifiers (supervised Kohonen self‐organizing maps (KSOMs), soft independent modelling of class analogy (SIMCA), k‐nearest neighbors (kNN), and partial least squares with discriminant analysis (PLS‐DA) in the multiclass classification of Southern Brazilian table wines based on their physicochemical data. We also employed an unsupervised KSOM for the exploratory analysis of our data and compared its performance to that of PCA in this same task. All methods tested here presented a non‐error rate (NER) and accuracy equal to or higher than 67% in the classification of the samples, having PLS‐DA achieved an NER of 86% in classifying the samples from the test set and accuracy of 83% in classifying the samples from the training set. However, the best overall classification performance (when classification performances in training, cross‐validation, and test sets are taken into account) in terms of NER and accuracy was that of SIMCA. Regarding the unsupervised analysis of the data, principal component analysis (PCA) provided a better separation of the samples and more convenient visualization of relationships between variables, and between variables and samples, than unsupervised KSOMs..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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| Enthalten in: |
Journal of Chemometrics - 34(2020), 12 |
| Beteiligte Personen: |
Hansen, Lucas [VerfasserIn] |
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| BKL: |
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| Anmerkungen: |
© 2020 John Wiley & Sons, Ltd. |
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| Umfang: |
11 |
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| doi: |
10.1002/cem.3302 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract In this work, we evaluated the performance of several classifiers (supervised Kohonen self‐organizing maps (KSOMs), soft independent modelling of class analogy (SIMCA), k‐nearest neighbors (kNN), and partial least squares with discriminant analysis (PLS‐DA) in the multiclass classification of Southern Brazilian table wines based on their physicochemical data. We also employed an unsupervised KSOM for the exploratory analysis of our data and compared its performance to that of PCA in this same task. All methods tested here presented a non‐error rate (NER) and accuracy equal to or higher than 67% in the classification of the samples, having PLS‐DA achieved an NER of 86% in classifying the samples from the test set and accuracy of 83% in classifying the samples from the training set. However, the best overall classification performance (when classification performances in training, cross‐validation, and test sets are taken into account) in terms of NER and accuracy was that of SIMCA. Regarding the unsupervised analysis of the data, principal component analysis (PCA) provided a better separation of the samples and more convenient visualization of relationships between variables, and between variables and samples, than unsupervised KSOMs. | ||
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