Please use this identifier to cite or link to this item: https://repositorio.uti.edu.ec//handle/123456789/3238
Title: Voltammetric Electronic Tongues Applied to Classify Sucrose Samples Through Multivariate Analysis
Authors: Fuentes Pérez, Esteban
Varela-Aldás, José
Verdú, Samuel
Meló, Raúl
Alcañiz, Miguel
Issue Date: 2021
Publisher: Lecture Notes in Networks and Systems. Volume 271, Pages 216 – 222. AHFE Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, 2021. Virtual, Online. 25 July 2021 through 29 July 2021.
Abstract: The aim of the present study was to classify samples of sugar with different concentrations through a Voltammetric Electronic tongues (VET), with a generic pulse sequence consisted of 22 pulses ranging from –1000 mV to + 1000 mV with a duration of 20 ms/pulse over different samples such as 1.25mM, 2.5mM, 5mM and 10mM, of sucrose concentration, these were measured 4 times each concentration and the test was developed 4 times, giving a total number of 506.880 data supervised learning algorithm using support vector machine was employed, choosing a linear function as a classifying element. In the training, 75% of the data was used to determine the coefficients of the classification function, and the remaining (25%) was used to evaluate the performance of the proposal. The results showed a concordance of more than 80% in the separation of sample, allowing to conclude as acceptable the performance of the classifier and the data acquired through the voltammetric tongue.
URI: https://link.springer.com/chapter/10.1007/978-3-030-80624-8_27
http://repositorio.uti.edu.ec//handle/123456789/3238
Appears in Collections:Artículos Científicos Indexados

Files in This Item:
There are no files associated with this item.


This item is licensed under a Creative Commons License Creative Commons