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    Título : Develop of a Sample Classifier Through Multivariate Analysis for Caffeine as a Bitter Taste Generator
    Autor : Fuentes Pérez, Esteban
    Varela-Aldáz, José
    Verdú, Samuel
    Grau Meló, Raúl
    Barat, José Manuel
    Alcañiz, Miguel
    Fecha de publicación : 2022
    Editorial : Communications in Computer and Information Science. Volume 1583 CCIS, Pages 20 - 24. 24th International Conference on Human-Computer Interaction, HCI International, HCII 2022. Virtual, Online. 26 June 2022 through 1 July 2022
    Resumen : The use of voltammetric electronic tongues (VET) in the food industry is becoming more frequent, being used both for the identification of substances such as antioxidants or phenols, as well as in the case of sensory analysis simulation applications to help with quality control analysis or development of new products. In this work VET developed by the IDM of the UPV were employed, to get the data and then classify caffeine with different levels of intensity. The data was processed by multiclass analysis through a supervised learning algorithm which uses a vector support machine, with binary learners using the one-versus-all coding design, choosing a linear function as classifying element, the aim of this work was to verify if the VET can differentiate between different samples of caffeine which is the main reference for the bitter flavor. The results showed a concordance of 67.19% in the separation of samples, allowing to conclude as regular the performance of the classifier and therefore the data acquired through the VET
    URI : https://link.springer.com/chapter/10.1007/978-3-031-06394-7_3
    http://repositorio.uti.edu.ec//handle/123456789/3642
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