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Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several scientific techniques were applied in order to guarantee the authenticity and quality of this EVOO. In the present study, the volatile compounds (VOCs) by gas chromatography–mass spectrometry with solid-phase micro-extraction detection (GC–MS SPME), organoleptic analysis by the official Slow Food panel and the detection by a Small Sensor System (S3) were applied. Ten EVOOs from Umbria, a central Italian region, were selected from the 2021 Slow Food Italian extra virgin olive oil official guide, which includes hundreds of high-quality olive oils. The results demonstrated the possibility to discriminate the ten EVOOs, even if they belong to the same Italian region, by all three techniques. The result of GC–MS SPME detection was comparable at the discrimination level to the organoleptic test with few exceptions, while the S3 was able to better separate some EVOOs, which were not discriminated perfectly by the other two methods. The correlation analysis performed among and between the three methodologies allowed us to identify 388 strong associations with a p value less than 0.05. This study has highlighted how much the mix of VOCs was different even among few and localized EVOOs. The correlation with the sensor detection, which is faster and chipper compared to the other two techniques, elucidated the similarities and discrepancies between the applied methods. [ABSTRACT FROM AUTHOR]

Chemical sensors are tools able to detect organic and inorganic molecules in the three states of matter with different mechanisms of interaction. Each molecule interacts in a different way with sensing materials, producing dissimilar changes in its intrinsic properties. In recent years, chemical sensors have been widely used in arrays for devices known as electronic noses (ENs). They showed great potentiality to be applied in various fields, like health, environmental monitoring and food quality and identity. One of these devices, called Small Sensors System (S3) produced by NASYS, a spin-off of University of Brescia, is equipped with innovative chemical sensors that analyse volatile compounds. They are designed and constructed at SENSOR Laboratory (University of Brescia) and are different from the other metal oxide semiconductor (MOX) sensors available on the market because of their nanometric dimensions. This brings numerous advantages since nanomaterials have an extraordinary length-to-width ratio, that enhances sensing capability and longterm stability for prolonged operation. In addition, the three-dimensional network formed by the nanowires increases the adsorption surface and the catalytic activity, improving the response and decreasing the threshold up to few ppm concentrations. The ability to connect to Internet makes S3 an Internet of Thing (IoT) device. This allows to send and storage data in the cloud and to use S3 remotely. Indeed, S3 has a dedicated user-friendly Web App whereby users can analyse data on-line using different techniques, from univariate and multivariate statistical analysis to Artificial Neural Networks. S3 system has shown the capability to be a useful tool in agri-food field. Among the whole applications, it has been successfully used to recognize the authenticity of food and to individuate food pathogens and microbiological contamination as described in the following sections. [ABSTRACT FROM AUTHOR]

The drift of metal oxide semiconductor (MOX) chemical sensors is one of the most important topics in this field. The work aims to test the performance of MOX gas sensors over the aging process. Firstly, sensors were tested with ethanol to understand their behavior and response changes. In parallel, beers with different alcoholic content were analyzed to assess what happened in a real application scenario. With ethanol analysis, it was possible to quantify drift of the baseline of the sensors and changes that could affect their responses over time (from day 1 to day 51). Conversely, the beer dataset has been exploited to evaluate how two different classifiers perform the classification task based on the alcohol content of the samples. A hybrid k-nearest neighbors artificial neural network (k-NN-ANN) approach and "standard" k-NN were used to evaluate to distinguish among the samples when the measures were affected by drift. To achieve this goal, data acquired from day one to day six were used as training to predict data collected up to day 51. Overall, performances of the two methods were similar, even if the best result in terms of accuracy is reached by k-NN-ANN (96.51%). [ABSTRACT FROM AUTHOR]

In this paper, we present the investigations on metal oxide-based gas sensors considering the works performed at SENSOR lab, University of Brescia (Italy). We reported the developments in synthesis techniques for the preparation of doped and functionalized low-dimensional metal oxide materials. Furthermore, we discussed our achievements in the fabrication of heterostructures with unique functional features. In particular, we focused on the strategies to improve the sensing performance of metal oxides at relatively low operating temperatures. We presented our studies on surface photoactivation of sensing structures considering the application of biocompatible materials in the architecture of the functional devices as well. [ABSTRACT FROM AUTHOR]