Classification of Three Volatiles Using a Single-Type eNose with Detailed Class-Map Visualization.
MOX gas sensors
PCA and LDA analysis
array of gas sensors
eNose
electronic nose
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
14 Jul 2022
14 Jul 2022
Historique:
received:
15
06
2022
revised:
07
07
2022
accepted:
11
07
2022
entrez:
27
7
2022
pubmed:
28
7
2022
medline:
29
7
2022
Statut:
epublish
Résumé
The use of electronic noses (eNoses) as analysis tools are growing in popularity; however, the lack of a comprehensive, visual representation of how the different classes are organized and distributed largely complicates the interpretation of the classification results, thus reducing their practicality. The new contributions of this paper are the assessment of the multivariate classification performance of a custom, low-cost eNose composed of 16 single-type (identical) MOX gas sensors for the classification of three volatiles, along with a proposal to improve the visual interpretation of the classification results by means of generating a detailed 2D class-map representation based on the inverse of the orthogonal linear transformation obtained from a PCA and LDA analysis. The results showed that this single-type eNose implementation was able to perform multivariate classification, while the class-map visualization summarized the learned features and how these features may affect the performance of the classification, simplifying the interpretation and understanding of the eNose results.
Identifiants
pubmed: 35890951
pii: s22145262
doi: 10.3390/s22145262
pmc: PMC9320711
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
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