CCE Faculty Articles
Researcher ID
0000-0003-4966-4265
Document Type
Article
Publication Title
Chemosensors
ISSN
2227-9040
Publication Date
6-21-2023
Abstract
This paper describes different E-Senses systems, such as Electronic Nose, Electronic Tongue, and Electronic Eyes, which were used to build several machine learning models and assess their performance in classifying a variety of Colombian herbal tea brands such as Albahaca, Frutos Verdes, Jaibel, Toronjil, and Toute. To do this, a set of Colombian herbal tea samples were previously acquired from the instruments and processed through multivariate data analysis techniques (principal component analysis and linear discriminant analysis) to feed the support vector machine, K-nearest neighbors, decision trees, naive Bayes, and random forests algorithms. The results of the E-Senses were validated using HS-SPME-GC-MS analysis. The best machine learning models from the different classification methods reached a 100% success rate in classifying the samples. The proposal of this study was to enhance the classification of Colombian herbal teas using three sensory perception systems. This was achieved by consolidating the data obtained from the collected samples.
DOI
10.3390/chemosensors11070354
Volume
11
Issue
7
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
NSUWorks Citation
Carrillo, Jeniffer Katerine; Durán, Cristhian Manuel; Cáceres, Juan Martin; Cuastumal, Carlos Alberto; Ferreira, Jordana; Ramos, José; Bahder, Brian; Oates, Martin; and Ruiz, Antonio, "Assessment of E-Senses Performance through Machine Learning Models for Colombian Herbal Teas Classification" (2023). CCE Faculty Articles. 532.
https://nsuworks.nova.edu/gscis_facarticles/532
Comments
The authors acknowledge the Multisensor Systems and Pattern Recognition Research group of the University of Pamplona.
© 2023 by the authors.