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    Application of multivariate methods to the differentiation of Peruvian wines
    (2022-01-14)
    This work presents the results of the sensing analysis of Peruvian wines of known (Commercial wines) and handmade brands, using electronic noses (E-noses) which consist of an array of sensors based on tin oxide doped with Pd or Pt, and some with zeolite coating. The combinations of the sensors were performed seeking to obtain the best discrimination of the wines with the multivariate methods, with a high level of confidence and a good distribution of the results. The Principal Component Analysis (PCA), cluster and factorial results showed that the electronic noses allowed to efficiently identify wines of known brand from those of handmade brand, revealing the way in which the wines have been produced. On the other hand, the multivariate methods applied to the electronic noses made up of SnO2 sensors doped with palladium showed a clear differentiation of Borgoña-type wines from wines of handmade brand and evidenced the formation of agglomerations between red and Rosé wines. The application of PCA, cluster and factorial obtained in this study allowed to obtain good results in the differentiation of wines, even with electronic noses formed with a low number of sensors.
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    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.