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Over the last 40 years, the term "electronic nose" (EN) has defined a device equipped with an array of not selective gas sensors capable of providing a response as a function of a stimulus provided by volatile chemical compounds (VOCs). Numerous studies have started from this idea, which have led to significant improvements and advantages, especially useful for providing a device capable of monitoring situations and applications in real-time. Applications that have strongly pushed the evolution of the "electronic nose" technology away from the laboratories and closer to more complex and stimulating real situations (Comini and Sberveglieri, 2010). One of the very initial goals of the EN was to simulate the mammalian nose to obtain a fast response regarding the characteristics of the analyte, high sensitivity for odours and high discrimination between them. In the last few years, a lot of upgrades have been made to the EN technology, thanks to artificial intelligence, machine learning evolutions, stability of the sensing elements, cloud processing, predictive algorithms, etc. Thanks to this strong commitment of all, this technology is reopening great interest in the industrial and consumers application field, managing to arrive directly in the transformation chains. The types of gas sensors used are various and are based on the modification of a physical or chemical parameter caused by the gases themselves. Conductometer sensors are the most common, being able to transduce a chemical signal in an electrical resistance signal. Other types of sensors have been developed and can be part of a functional array: Optical sensor, polymer sensor, electrochemical gas sensor, Quartz microbalances or SAW (Paolesse et al., 2017). In this presentation we will review the different sensor arrays most commonly used and a brief history of their evolution. From the point of view of sensor preparation technology, the one based on MEMS is becoming more and more widespread. A brief mention will also be made of the sensors used in the EN standard (called S3+) made by Nano Sensor Systems S.r.l. spin-off of the University of Brescia. We will conclude by presenting the evolution of sensors in recent years to better understand how the multisensory, multidisciplinary and cloud computing approach has positively influenced the real potential of Electronic Noses. [ABSTRACT FROM AUTHOR]

Biogas is becoming one of the most used and profitable renewable sources. It is obtained through many different processes that frequently involve different green renewable sources. During the different steps of the process, it generates several by-products that could be reused as organic fertilizers. The aim of this work was to study the volatile fraction and determine the volatile fingerprint of 6 different organic samples involved in biogas production by the innovative Small Sensor System (S3) based on Semiconductor Metal Oxide (MOX) gas sensors. Obtained results show the volatile profile of each sample that at the same time support the sensor's device results. S3 result shows a perfect discrimination of the volatile fraction of the different studied matrices based on the different composition of their volatile set. matrix and shows how the sensors are able, in real time, to cluster and discriminate the fingerprint of these renewable sources. In the end, S3 results are very promising to enhance the traceability and the origin of the sources in the biogas industry at each specific stage of production, focusing on the possible release of off-flavors in the environment from different types of organic biomass and the reuse of their by-products supporting circular economy. The aim of this work was to find and identify the VOCs set that characterizes different types of organic renewable sources through the use of the tailor made Small Sensor Systems S3 to distinguish between different odor sources based on their qualitative and quantitative differences in VOCs profile, in order to be able in the future to produce a user friendly fast, economic and with auto learning capabilities to support the aforementioned green industrial processes. [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]

Extra virgin olive oil (EVOO) is characterized by its aroma and other sensory attributes. These are determined by the geographical origin of the oil, extraction process, place of cultivation, soil, tree varieties, and storage conditions. In the present work, an array of metal oxide gas sensors (called S3), in combination with the SPME-GC-MS technique, was applied to the discrimination of different types of olive oil (phase 1) and to the identification of four varieties of Garda PDO extra virgin olive oils coming from west and east shores of Lake Garda (phase 2). The chemical analysis method involving SPME-GC-MS provided a complete volatile component of the extra virgin olive oils that was used to relate to the S3 multisensory responses. Furthermore, principal component analysis (PCA) and k-Nearest Neighbors (k-NN) analysis were carried out on the set of data acquired from the sensor array to determine the best sensors for these tasks and to assess the capability of the system to identify various olive oil samples. k-NN classification rates were found to be 94.3% and 94.7% in the two phases, respectively. These first results are encouraging and show a good capability of the S3 instrument to distinguish different oil samples. [ABSTRACT FROM AUTHOR]