Voltammetric Electronic Tongues Applied to Classify Sucrose Samples Through Multivariate Analysis
Journal
Lecture Notes in Networks and Systems
Date Issued
2021
Author(s)
Fuentes, E.M.
Verdú S.
Meló R.G.
Alcañiz M.
Type
Conference Paper
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. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Subjects