Support Vector Machine as Tool for Classifying Coffee Beverages

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


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.


Classifier Voltammetric tongue Supervised learning Support Vector Machine 


  1. 1.
    Salah, K., Rehman, M.H.U., Nizamuddin, N., Al-Fuqaha, A.: Blockchain for AI: review and open research challenges. IEEE Access (2019). Scholar
  2. 2.
    Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Mob. Networks Appl. (2018). Scholar
  3. 3.
    Çaliş, B., Bulkan, S.: A research survey: review of AI solution strategies of job shop scheduling problem. J. Intell. Manuf. (2015). Scholar
  4. 4.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs (2016). Scholar
  5. 5.
    Jara Estupiñan, J., Giral, D., Martínez Santa, F.: Implementación de algoritmos basados en máquinas de soporte vectorial (SVM) para sistemas eléctricos: revisión de tema. Rev. Tecnura. 20, 149–170 (2016).
  6. 6.
    Ghahramani, Z.: Probabilistic machine learning and artificial intelligence (2015). Scholar
  7. 7.
    Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data (2018).
  8. 8.
    Zendehboudi, A., Baseer, M.A., Saidur, R.: Application of support vector machine models for forecasting solar and wind energy resources: a review (2018). Scholar
  9. 9.
    Yang, D., Liu, Y., Li, S., Li, X., Ma, L.: Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mech. Mach. Theory (2015). Scholar
  10. 10.
    He, H., Kong, F., Tan, J.: DietCam: multiview food recognition using a multikernel SVM. IEEE J. Biomed. Health Inform. (2016). Scholar
  11. 11.
    Guevara, C., Sanchez-Gordon, S., Arias-Flores, H., Varela-Aldás, J., Castillo-Salazar, D., Borja, M., Fierro-Saltos, W., Rivera, R., Hidalgo-Guijarro, J., Yandún-Velasteguí, M.: Detection of student behavior profiles applying neural networks and decision trees. In: Advances in Intelligent Systems and Computing, pp. 591–597 (2020). Scholar
  12. 12.
    Fuentes, E., Alcañiz, M., Contat, L., Baldeón, E.O., Barat, J.M., Grau, R.: Influence of potential pulses amplitude sequence in a voltammetric electronic tongue (VET) applied to assess antioxidant capacity in aliso. Food Chem. (2017). Scholar
  13. 13.
    Ciocca, G., Napoletano, P., Schettini, R.: Food recognition: a new dataset, experiments, and results. IEEE J. Biomed. Health Inform. (2017). Scholar
  14. 14.
    Loutfi, A., Coradeschi, S., Mani, G.K., Shankar, P., Rayappan, J.B.B.: Electronic noses for food quality: a review (2015). Scholar
  15. 15.
    Arrieta, Á.A., Rodríguez-Méndez, M.L., De Saja, J.A.: Aplicación de una lengua electrónica voltamétrica para la clasificación de vinos y estudio de correlación con la caracterización química y sensorial. Quim. Nova 33(4), 787–793 (2010)CrossRefGoogle Scholar
  16. 16.
    Gamboa, A.A., Cáceres, P.A., Lamos, H., Zárate, D.A., Puentes, D.E.: Predictive model for cocoa yield in Santander using Supervised Machine Learning. In: 2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings (2019).
  17. 17.
    Chen, M.Y., Yang, Y.H., Ho, C.J., Wang, S.H., Liu, S.M., Chang, E., Yeh, C.H., Ouhyoung, M.: Automatic Chinese food identification and quantity estimation. In: SIGGRAPH Asia 2012 Technical Briefs, SA 2012 (2012).
  18. 18.
    Rodrigues, D.R., de Oliveira, D.S.M., Pontes, M.J.C., Lemos, S.G.: Voltammetric e-tongue based on a single sensor and variable selection for the classification of teas. Food Anal. Methods (2018). Scholar
  19. 19.
    de Morais, T.C.B., Rodrigues, D.R., de Carvalho Polari Souto, U.T., Lemos, S.G.: A simple voltammetric electronic tongue for the analysis of coffee adulterations. Food Chem. (2019). Scholar
  20. 20.
    Peris, M., Escuder-Gilabert, L.: Electronic noses and tongues to assess food authenticity and adulteration (2016). Scholar
  21. 21.
    Alcañiz Fillol, M.: Diseño de un sistema de lengua electrónica basado en técnicas electroquímicas voltamétricas y su aplicación en el ámbito agroalimentario, p. 295 (2011)Google Scholar
  22. 22.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2013).

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

Personalised recommendations