Synthesis and Application of Gas Sensors Based on SnO2-TiO2 and SnO2-MoO3 Composites for Differentiation of Peruvian Pisco Varieties

Abstract

Introduction The electronic nose (e-nose) is an emerging technology with the potential to provide a rapid and affordable assessment of production quality in the food industry. Its operation is based on the detection of volatile compounds that build the aroma of beverages and foods. This data provides information about the level of freshness of food and can be correlated to the quality of beverages. Indeed, the most widely used sensors are made from semiconductor metal oxides(SnO2, TiO2, MoO3, among others) because of their low cost and great sensitivity. Therefore, this variety of gas sensor allows an economically viable alternative to sophisticated techniques: HPLC and GC-MS [1].It should be noted that the low selectivity of these oxides towards some volatile organic compounds (VOCs) has limited their application. A method to solve this problem is the preparation of composites based on two different metal oxides. In fact, the enhanced performance of semiconductor metal oxides as gas sensors originates from the synergistic effects of putting in contact grain boundaries of different metal oxides. In particular, the Schottky barrier at the interface between the oxides grains can be tailored by mixing oxides with different energy gaps [2].Peruvian Pisco is an artisanal grape brandy with an alcohol content between 38% and 48%. It is considered an emblematic drink of Peru due to its unique characteristics and traditional procedures used for its preparation. Even though this beverage has a protected designation of origin, its artisanal production presented an increasing adulteration's incidence. To find a solution to this problem, this study implemented an electronic nose designed at PUCP and made up of composite oxide sensors based on SnO2-TiO2 and SnO2-MoO3. Synthesis of Metal Oxide Composites The oxide composites were synthesized by chemical precipitation of SnO2 over the grains of both metal oxides: TiO2 and MoO3. A typical synthesis procedure consisted of dispersing 0.2 g of SnCl2.2H2O in 5 mL of 0.1M HNO3 under ultrasound for 30 minutes. Subsequently, 100 mg of MoO3 or TiO2 was added to the mixture, which was left under stirring for 15 minutes. Later a few drops of concentrated ammonium hydroxide were slowly added until reaching a pH of 10. Finally, after stirring for 30 minutes, the solid was washed by centrifugation with Mill Q water and dried at 80°C. Finally, the composite was calcined at 500°C for 2 hours. This e-nose was developed to differentiate two varieties of Pisco: Quebranta and Italia. Method The crystal structures in the metal oxide composites were determined by x-ray diffraction and the crystallite size was estimated from the Sherrer’s equation. The metal oxide composites were deposited over alumina substrates using an organic binder (α-terpineol). Interdigit platinum electrodes were previously fabricated over the substrates. The prepared sensors were calcinated at 500°C and later aged at 150°C for a night.The e-nose sensing response to VOCs in the aroma of the Pisco samples was recorded at different temperatures: 200˚C, 220˚C, and 240˚C. The data obtained were processed using supervised (SVN and KNN) and unsupervised (PCA and HCA) statistical methods. The modeling parameters were optimized by cross-validation of k-folds. Results and Conclusions The two principal components (PC1 and PC2) of the data obtained using the electronic nose confirmed its capacity to differentiate the varieties studied at certain working conditions. The best results were obtained from the application of the sensor based on SnO2-TiO2 at 240°C. In the Figure, the PCA plots showed a significant improvement in this sensor's differentiation capacity of the studied Pisco varieties when the sensing temperature was increased from 200°C to 240°C. The supervised classification analyzes allowed the development of predictive models with a success rate greater than 90% using the sensors based on SnO2-TiO2. Keywords: Peruvian Pisco, electronic nose, composite oxides, PCA, KNN algorithm and SVM.[1] Quicazán, M. C., Díaz, A. C. y Zuluaga, C. M. La nariz electrónica, una novedosa herramienta para el control de procesos y calidad en la industria agroalimentaria. VITAE, Rev. la Fac. Química Farm. 209–217 (2011).[2] Xing, L. L., Yuan, S., Chen, Z. H., Chen, Y. J. y Xue, X. Y. Enhanced gas sensing performance of SnO2/α-MoO3 heterostructure nanobelts. Nanotechnology 22, (2011). Figure 1