Bibliographic citations
Trevejo, J., (2019). Preparación, caracterización de sensores a base de zeolita, óxidos de estaño y de zinc conformando una nariz electrónica y su aplicación para la diferenciación de piscos peruanos [Tesis, Pontificia Universidad Católica del Perú]. http://hdl.handle.net/20.500.12404/15017
Trevejo, J., Preparación, caracterización de sensores a base de zeolita, óxidos de estaño y de zinc conformando una nariz electrónica y su aplicación para la diferenciación de piscos peruanos [Tesis]. PE: Pontificia Universidad Católica del Perú; 2019. http://hdl.handle.net/20.500.12404/15017
@mastersthesis{renati/532557,
title = "Preparación, caracterización de sensores a base de zeolita, óxidos de estaño y de zinc conformando una nariz electrónica y su aplicación para la diferenciación de piscos peruanos",
author = "Trevejo Pinedo, Jorge Nelson",
publisher = "Pontificia Universidad Católica del Perú",
year = "2019"
}
Pisco is one of the most consumed beverages in Peru. It has historical and commercial importance and is protected by a Denomination of Origin that regulates its production. However, adulteration and falsification of Pisco are common practices that undermine its reputation both in the domestic and international markets. Therefore, this work aims to differentiate Acholado, Italia and Quebranta Peruvian Pisco varieties, as well as differentiate Pisco Quebranta mixtures with cane liquor in several proportions, using an electronic nose equipment constituted by a sensors array based on metal oxides (platinum-doped tin oxide doped and silver-doped zinc oxide), both types of sensors with and without a zeolite-Y coating. It is postulated that the sensors readings for different Pisco types are representative for each variety and allows their identification. The characterization of the prepared materials is carried out by DRX and FTIR. The configuration of the electronic nose and the data collection is made through LabView2018 software and the optimal conditions of temperature, sensing time, metallic doping of the sensor and zeolite-Y coating are determined. The best responses are obtained by sensors 0,10%Pt/SnO2; 0,05%Pt/SnO2//ZY and 1,5% Ag/ZnO. The sensor readings are subjected to classification analysis both unsupervised (PCA, HCA) and supervised (SVM, RF, KNN) with the objective of determining the discrimination capacity of Pisco samples by the sensors present in the electronic nose.
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