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/MetOH 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 of 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 their variety.

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 semiconductor metal 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. Silver nitrate salt (J.A. Elmer, >99.8%) was treated with citric acid, Aloe vera, or Allium sativum extracts at 80 °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 piscos (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 compared to those without doping. The Figure shows that plotting the two principal components (PC1 and PC2) for the Pisco varieties analysis led to a clear differentiation of the samples. Optimized supervised methods achieved prediction rates greater than 90%. It was concluded that it is feasible to use plant extracts from Aloe vera and Allium sativum to prepare gas sensors that can be used in the beverage industry. Keywords: Electronic nose, Aloe vera, Allium sativum, PCA, KNN algorithm, SVM [1] Wang, L.; Wang, Y.; Yu, K.; Wang, S.; Zhang, Y. & Wei, C. Sensors and Actuators B: Chemical 2016 , 232, 91-101. Figure 1

This study focuses on the synthesis and characterization of metal oxides doped with transition metals with the purpose of enhancing their sensitivity to detect organophosphate insecticides, Chlorpyrifos and Malathion, on air. The moderate toxicity of these organophosphorus pesticides and their extensive use in small-scale agriculture reveals the urgency of continuous air quality monitoring in the work environments of communities with restricted access to sophisticated techniques for detecting these contaminants, such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). The selected metal oxides are Zr doped zinc oxide (ZnO) and tin oxide (SnO2) doped with both Pt and Zr. These semiconductors (ZnO and SnO2) have the advantage of showing a sensing response at low working temperatures, in the range of 200°C and 250°C. Additionally, both have great thermal and chemical stability. The synthesis of ZnO is performed through the application of a hydrothermal synthesis methodology in an autoclave; while ZnO doping is conducted by adding the dopant precursor (ZrOCl2) to the reaction mixture. In the case of SnO2, commercial tin oxide is used for doping with Zr and Pt. The doping process was carried out in two separate processes: 1) mechanical mixing and sintering of ZrO2 and SnO2, and 2) chemical reduction of H2Cl6Pt with SnSO4 on the surface of SnO2. The characterization of these materials is carried out by employing X-ray diffraction (DRX), Fourier-transformed infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-Vis), scanning electron microscopy / energy dispersive X-ray spectroscopy (SEM/EDS), transmission electron microscopy (TEM) and sorption of N2 analysis. In addition, the quantification of pesticide concentration in commercial samples, that are used in the sensing essays, is performed with HPLC. The Pt doped SnO2 is prepared with a content of Pt between 0.05 and 0.2 wt. %, while the concentration of Zr is in the range of 0.3 and 0.7 wt. %. In the case of ZnO, the concentration of Zr is between 0.9 and 2.0 wt. %. The characterization by DRX reveals a substitutional doping for both materials. Additionally, the dopants produce an increase of the lattice strain and a small contraction of the unit cell in both metal oxides. The SEM analysis of Zr doped ZnO allows to identify changes in their morphology, which confirms an increase in particle size at Zr concentration of 2.0 wt. % in comparison to 1.2 wt. %. The sensing tests of the doped metal oxides are evaluated in an electronic nose. This equipment allows to test four sensors each time and the vapors of the pesticides are produced through a bubbling process of a liquid suspension of one organophosphate insecticide in water. The prepared doped oxides present an enhanced sensibility for the detection of organophosphates on air. Response signals are obtained with greater stability for long sensing times with zinc oxide sensors doped with Zr at 2.0 wt. % in comparison to sensors of Zr doped ZnO with lower Zr content. A study of the optimum temperature conditions (between 210°C and 220°C) and concentration of the dopant in zinc oxide and tin oxide is carried out. The temperature has a positive effect on the sensitivity of all the sensors tested. At the test temperature of 220°C, the sensitivity of the best sensors (AT-Zr-2.0-ZnO and Pt-0.13-ZrO2-0.15-SnO2) for Malathion can be maximized. In the case of Chlorpyrifos, better results are obtained for the Pt-0.13-ZrO2-0.15-SnO2 sensor, whose signal is favored at 210°C. In addition, the sensitivity of Pt-0.13-ZrO2-0.15-SnO2 with respect to AT-Zr-2.0-ZnO at 220°C is higher for Chlorpyrifos, but lower for Malathion. The statistical treatment method of principal component analysis (PCA) allows the evaluation of the signals of samples with different concentrations of pesticides. The best PCA, obtained with the data of the measurements using the most sensitive sensors, show a total explained variance greater than 90% and a better differentiation between air samples contaminated with pesticides and air samples without contamination. Figure 1

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.

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. References [1] L. Wang, Y. Wang, K. Yu, S. Wang, Y. Zhang, C. Wei, Sensors and Actuators B: Chemical. 232 (2016) 91-101. doi:10.1016/j.snb.2016.02.135. [2] K. Logaranjan, A.J. Raiza, S.C.B. Gopinath, Y. Chen, K. Pandian, Shape- and Size-Controlled Synthesis of Silver Nanoparticles Using Aloe vera Plant Extract and Their Antimicrobial Activity, Nanoscale Research Letters. 11 (2016). doi:10.1186/s11671-016-1725-x. Figure 1