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Item type:Publication, SnO2-TiO2 and SnO2-MoO3 based composite gas sensors to develop an e-nose for Peruvian Pisco varieties differentiation(IOP Publishing Ltd, 2022-01-01)There is great interest in the development of a simple system that could identify adulteration or counterfeiting of Peruvian Pisco (a grape-based alcoholic drink). In this study, sensors based on SnO2-TiO2 and SnO2-MoO3 composites with different weight composition ratios were synthesized and characterized. These sensors were tested with aqueous solutions of EtOH/MeOH and Pisco samples of Italia and Quebranta varieties in order to explore their capacity to identify variations in these beverages. The response profile of the most sensitive sensors showed an enhanced response to alcoholic samples with greater content of ethanol up to a concentration of 45%v/v, while the increased content of methanol in the range of 0.1 to 0.3% v/v diminished the intensity of the sensor response. Differences in the composition of methanol and ethanol in the Pisco varieties studied (Italia and Quebranta) were correlated to the capacity of the composite-based sensors to differentiate them with greater performance. Sensors based on SnO2-TiO2-1/2 composites showed greater reproducibility in their response profile over time in comparison to SnO2-TiO2-1/1 and SnO2-MoO3 composites. The PCA showed that composite sensors were able to differentiate Pisco samples according to the varieties, Italia and Quebranta. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Experimental determination of a mixture composed of Camisea natural gas and CO2 laminar burning velocity(Multidisciplinary Digital Publishing Institute (MDPI), 2024-11-01)The aim of this work is to provide new experimental data on laminar burning velocities for a new synthetic mixture composed of Camisea natural gas and CO2. It was found that the relevant published experimental background data are limited to mixtures composed of methane and CO2; considering the fact that Camisea natural gas is widely used in Peru, this experimental research will serve as a supportive resource for further experimental and industrial implementations in this country, such as the design and modeling of new engines or industrial burners that are designed to be fueled by this mixture. An experimental setup for analyzing three types of flame geometry, which is feasible to implement for a wide range of conditions, was built in PUCP PI0735 laboratory and all the measurements were obtained for a range of mixtures (0%, 21.2%, 28.5%, 38.9%, 50% CO2) and ratios from around 0.55 to 0.95 at atmospheric conditions. The laminar burning velocities results obtained were analyzed in groups based on %CO2. In addition, the experimental margin error was determined by considering all the sources. The following conclusions were reached: (1) The laminar burning velocity decreases with the increase in CO2 percentage in the mixture due to the CO2 decreasing the flame temperature effect. (2) The flat flame type provided the highest value of burning velocity for each group of CO2 percentage in which it appears. (3) The highest obtained laminar burning velocity value was 22.64 ± 0.15 cm/s, for a flat flame with a ratio of 0.72 and 29.98% of CO2, while the lowest obtained value was 6.78 ± 0.15 cm/s for a conical trunk flame with a ratio of 0.59 and 49.83% of CO2. (4) The highest evaluated CO2 percentage was 50.97% for a conical trunk flame with a ratio of 0.69 and a burning velocity value of 11.04. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose(Multidisciplinary Digital Publishing Institute (MDPI), 2023-07-01)Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation–extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.