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Support Vector Machine as Tool for Classifying Coffee Beverages

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Book cover Information Technology and Systems (ICITS 2020)

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

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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.

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Authors and Affiliations

  1. SISAu Research Group, Universidad Indoamérica, 180103, Ambato, Ecuador

    José Varela-Aldás, Esteban M. Fuentes & Jorge Buele

  2. Departamento de Tecnología de Alimentos, Grupo CUINA, Universidad Politécnica de Valencia, Valencia, Spain

    Esteban M. Fuentes, Raúl Grau Meló & José Manuel Barat

  3. Instituto de Reconocimiento Molecular y Desarrollo Tecnológico (IDM), Centro Mixto Universitat Politècnica de València, Universidad de Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    Miguel Alcañiz

Authors
  1. José Varela-Aldás
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  2. Esteban M. Fuentes
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  3. Jorge Buele
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  4. Raúl Grau Meló
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  5. José Manuel Barat
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  6. Miguel Alcañiz
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Corresponding authors

Correspondence to José Varela-Aldás or Esteban M. Fuentes .

Editor information

Editors and Affiliations

  1. DEI/FCT, University of Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. Facultad de Geografia, University of Santiago de Compostela, Santiago de Compostela, Spain

    Carlos Ferrás

  3. Facultad de Ingeniería, Universidad Distrital Francisco José de, Bogota, Colombia

    Carlos Enrique Montenegro Marin

  4. Facultad de Ingeniería, Universidad Distrital Francisco José de, Bogota, Colombia

    Víctor Hugo Medina García

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Varela-Aldás, J., Fuentes, E.M., Buele, J., Meló, R.G., Barat, J.M., Alcañiz, M. (2020). Support Vector Machine as Tool for Classifying Coffee Beverages. In: Rocha, Á., Ferrás, C., Montenegro Marin, C., Medina García, V. (eds) Information Technology and Systems. ICITS 2020. Advances in Intelligent Systems and Computing, vol 1137. Springer, Cham. https://doi.org/10.1007/978-3-030-40690-5_27

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