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Support Vector Machine as Tool for Classifying Coffee Beverages

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1137)

Abstract

Classifiers are tools widely used nowadays to process data and obtain prediction models that are trained through supervised learning techniques; there is a wide variety of sensors that acquire the data to be processed, such as the voltammetric electronic tongue, as a device employed to analyze food compounds. This paper presents a normal and decaffeinated coffee beverage classifier using a Support Vector Machine with a linear separation function, detailing the classification function and the model optimization method; to train the model, the data measured by 4 electrodes of a voltammetric tongue that is excited by a predetermined sequence of positive pulses is used. In addition, the results graphically show the measurements obtained, the support vectors and the evaluation data, the values of the classifier parameters are also presented. Finally, the conclusions establish an acceptable error in the classification of coffee drinks according to caffeine presence at the sample analyzed.

Keywords

Classifier Voltammetric tongue Supervised learning Support Vector Machine 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.SISAu Research GroupUniversidad IndoaméricaAmbatoEcuador
  2. 2.Departamento de Tecnología de Alimentos, Grupo CUINAUniversidad Politécnica de ValenciaValenciaSpain
  3. 3.Instituto de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Centro Mixto Universitat Politècnica de ValènciaUniversidad de ValenciaValenciaSpain

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