Gas Sensors and Machine Learning for Quality Evaluation of Grape Spirits (Pisco)

Renzo Bolivar, Edgar Sarmiento-Calisaya, Guina Sotomayor Alzamora

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Pisco is Peru’s national drink (The Peruvian state has the “Appellation of Origin”). It is distilled from grapes grown only in the regions of Lima, Ica, Arequipa, Moquegua or Tacna, and using methods that preserve the traditional principles of quality. Peru currently exports pisco to large-market countries (e.g. the European Union, the United States, Mexico, Canada, Australia, etc.); however, according to ADEX (The Peruvian Exporters Association), despite the fact that exports grow annually, losses have increased, mainly due to penalties and taxes for adulterated pisco. The adulterated pisco contains a high concentration of methanol and other types of alcohols, which are harmful to human health, and consequently it damages the economics of the beverage industry in Peru. This work addresses the design and development of a prototype tool to classify pisco and adulterated pisco, which is based on i) a gas sensor matrix that allows the identification of volatile compounds and congeners of pisco, ii) a data acquisition module based on a low-cost ARM (Advanced RISC Machine) micro-controller (Arduino), and iii) a classification model based on machine learning techniques. In the evaluation, pisco samples were used; and the results showed 97.29% of accuracy for the problem of classification and 7.43 s for the problem of training time. Therefore, it provides insights that the prototype is useful, low-cost, easy to use and fast. Thus, its development continues.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 9th Annual International Conference, SIMBig 2022, Proceedings
EditoresJuan Antonio Lossio-Ventura, Jorge Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas158-174
Número de páginas17
ISBN (versión impresa)9783031354441
DOI
EstadoPublicada - 2023
Evento9th Annual International Conference on Information Management and Big Data, SIMBig 2022 - Lima, Perú
Duración: 16 nov. 202218 nov. 2022

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1837 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia9th Annual International Conference on Information Management and Big Data, SIMBig 2022
País/TerritorioPerú
CiudadLima
Período16/11/2218/11/22

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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