Synthesis and Application of Gas Sensors Based on Tin Oxide Doped with Green Synthesized Silver Nanoparticles for Differentiation of Peruvian Pisco Varieties

Abstract

Introduction Electronic noses allow a rapid and economical evaluation of freshness, maturity or decomposition of food and beverages for classification according to their based on their volatile components. The most widespread gas sensors are based on metal semiconductor oxides (such as SnO2, TiO2 or ZnO) due to their low cost and high sensibility. However, their main disadvantage is its low selectivity. A doping treatment with noble metals is required to generate an improvement in the response to specific gases and decrease the working temperature [1].Pisco is a grape-based alcoholic beverage of historical and commercial importance in Peru, with a growing presence in the national and international market. Although its production and commercialization are protected by a designation of origin, the industry faces a severe adulteration issue. This study evaluates the application of SnO2 gas sensors doped with silver nanoparticles obtained by green synthesis on an electronic nose as a fast and low-cost method to differentiate Pisco varieties. Green Synthesized Silver Nanoparticles Silver nanoparticles were prepared via a conventional synthesis and two green synthesis routes [2]. Silver nitrate (J.A. Elmer, >99.8%) was treated with citric acid, Aloe vera or Allium sativum extracts at 70 °C and pH 8. The nanoparticles obtained were characterized by UV-Visible and ATR spectroscopy. Tin oxide (Merck, >99.9%) was mixed with the nanoparticle suspension and calcinated at 450 °C for 2 h. Method The sensing of Peruvian Pisco (Quebranta and Italia varieties) was performed using an electronic nose made at our university with SnO2-based sensors doped with silver nanoparticles. The working temperature was 240 °C and the sample and purge times were 80 and 220 s. The sensing results obtained were analyzed using multivariate statistical methods that were unsupervised (PCA and HCA) and supervised (SVM and KNN). The parameters of the models were optimized using k-folding cross-validation based on their accuracy, and subsequently used to predict the Pisco variety of another set of measurements. Results and Conclusions Tin oxides doped with silver nanoparticles allowed a greater differentiation of Pisco varieties (Figure 1) compared to those without doping. Optimized supervised methods achieved prediction rates greater than 90%. It was concluded that it is feasible to use plant species as an alternative to reduce the negative effects, such as water and soil contamination, caused by chemicals.