Fuentes Pérez, EstebanVarela-Aldás, JoséVerdú, SamuelMeló, RaúlAlcañiz, Miguel2022-06-202022-06-202021https://link.springer.com/chapter/10.1007/978-3-030-80624-8_27https://hdl.handle.net/20.500.14809/3238The 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.engopenAccesshttps://creativecommons.org/licenses/by/4.0/article