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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]

Tea is the second most consumed beverage, and its aroma, determined by volatile compounds (VOCs) present in leaves or developed during the processing stages, has a great influence on the final quality. The goal of this study is to determine the volatilome of different types of tea to provide a competitive tool in terms of time and costs to recognize and enhance the quality of the product in the food chain. Analyzed samples are representative of the three major types of tea: black, green, and white. VOCs were studied in parallel with different technologies and methods: gas chromatography coupled with mass spectrometer and solid phase microextraction (SPME-GC-MS) and a device called small sensor system, (S3). S3 is made up of tailor-made metal oxide gas sensors, whose operating principle is based on the variation of sensor resistance based on volatiloma exposure. The data obtained were processed through multivariate statistics, showing the full file of the pre-established aim. From the results obtained, it is understood how supportive an innovative technology can be, remotely controllable supported by machine learning (IoF), aimed in the future at increasing food safety along the entire production chain, as an early warning system for possible microbiological or chemical contamination. [ABSTRACT FROM AUTHOR]