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Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented. [ABSTRACT FROM AUTHOR]

Nowadays, innovation in food technologies is fundamental and consumers are increasingly aware and demanding. To create a final product that is more and more appealing, health and safety guidelines are pushing towards new challenges. It is precisely due to the high quality required by the producers that the aim discussed in this project has been conceived. Until today, the controls on the entire production line have been slowed down by the limitations of the technologies involved, including the high cost of instrumentation for microbiological analysis, the need for qualified personnel to carry them out, the long execution times and the invasiveness of the techniques themselves. This project has, therefore, proposed a user-friendly solution that is minimally invasive, fast and at a lower cost. This system makes use of classical microbiological analysis and, in parallel, use of an innovative electronic-nose small sensor system (S3+), which can be trained to recognize the volatile fingerprint of a specific product and customized for a specific use. The aim of this project was to develop a system that is able to detect the mold contamination on fruit and vegetable jams and marmalades, using a new kind of innovative metal semiconductor gas sensor (MOS) device. The application of this technology has, therefore, made it possible to classify various samples of uncontaminated and contaminated fruit and vegetable preparations. Thanks to the classification implemented by a data-driven algorithm, it has been possible to build an anomaly detector that is able to recognize the occurrence of possible contamination, thus acting as an early alert system in the food chain. All this will occur in less than 1 min once the system is trained, in contrast with classical microbiological or chemical techniques that normally require longer timeframes to obtain a result and involve the use of reagents, increasing the costs. [ABSTRACT FROM AUTHOR]

Beer is the most consumed alcoholic beverage; with 177.5 million kiloliters produced every year, it is one of the most relevant food products. Diacetyl is a typical byproduct of yeast metabolism that is formed during the fermentation inside breweries. The perception of this high volatile and butter-like flavor molecule varies according to the kind of beer, from a positive and highly sought characteristic to a characteristic that is avoided. Furthermore, its toxicity when inhaled has been proven. Typical diacetyl analysis includes voltametric detection and chromatographic analysis techniques. Using metal oxide sensors (MOS), this analysis can become fast and cost-effective, evaluating the differences in diacetyl concentrations through resistance variation. The S3+ (Nano Sensor Systems s.r.l.; Reggio Emilia, Italy; device can recognize volatile compounds through a tailormade array of different materials. The results can be shown on a PCA that is directly generated by the instruments and can be used to manage the productive process through an IoT integrated system. Testing different beer typology through electrochemical sensors allows for the validation of this new approach for diacetyl evaluation. The results have shown an excellent ability to detect diacetyl in different beer samples, perfectly discriminating among different concentrations. [ABSTRACT FROM AUTHOR]

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]

The marmalade and jam market is growing worldwide, with the European countries being the main producers in this sector. The market has ancient origins and the production is aimed at conserving the surplus fruits during some period of the year. Nowadays, the automatic production processes are wide-ranging but start with high-quality raw materials and follow an appropriate cooking process to conserve the main features of the final product. On the other hand, cases of overcooking may occur which lead to the production of hydroxy-methyl-furfural and derivatives with consequent browning and poor organoleptic characteristics of the final product. This study aimed to use chemical oxide nanowire gas sensors device S3 coupled with optical techniques and recognizing algorithms to create a multi-actor platform able to control the production process of jams and marmalades with a fast response time, to assist the production process and avoid economical losses in the sector. PCA shows that this innovative technology can recognize changes in the volatile fingerprint, distinguishing when the positive and more natural organoleptic characteristics of the fruit are still present from the appearance of the organoleptic defects due to a faulty production process. [ABSTRACT FROM AUTHOR]

Vinegar is a fermented product that is appreciated world-wide. It can be obtained from different kinds of matrices. Specifically, it is a solution of acetic acid produced by a two stage fermentation process. The first is an alcoholic fermentation, where the sugars are converted in ethanol and lower metabolites by the yeast action, generally Saccharomyces cerevisiae. This was performed through a technique that is expanding more and more, the so-called "pied de cuve". The second step is an acetic fermentation where acetic acid bacteria (AAB) action causes the conversion of ethanol into acetic acid. Overall, the aim of this research is to follow wine vinegar production step by step through the volatiloma analysis by metal oxide semiconductor MOX sensors developed by Nano Sensor Systems S.r.l. This work is based on wine vinegar monitored from the grape must to the formed vinegar. The monitoring lasted 4 months and the analyses were carried out with a new generation of Electronic Nose (EN) engineered by Nano Sensor Systems S.r.l., called Small Sensor Systems Plus (S3+), equipped with an array of six gas MOX sensors with different sensing layers each. In particular, real-time monitoring made it possible to follow and to differentiate each step of the vinegar production. The principal component analysis (PCA) method was the statistical multivariate analysis utilized to process the dataset obtained from the sensors. A closer look to PCA graphs affirms how the sensors were able to cluster the production steps in a chronologically correct manner. [ABSTRACT FROM AUTHOR]

Coffee capsules have become one of the most used methods to have a coffee in the last few years. In this work, coffee was prepared using a professional espresso coffee machine. We investigated the volatilome of four different polypropylene coffee capsule typologies (Biologico, Dolce, Deciso, Guatemala) with and without capsules in order to reveal the possible differences in the VOCs spectra. The volatilome of each one was singularly studied through an analysis by gas chromatography and mass spectrometry (GC–MS), checking the abundance of different VOCs in coffee extracted with and without a capsule protection and compared to its related sample. Furthermore, ANOVA and Tukey tests were applied to statistically identify and individuate the possible differences. As a result, it was found that coffee capsules, offer advantages of protecting coffee from oxidation or rancidity and, consequently extended shelf life as well as did not cause a reduction of volatile compounds intensity. Therefore, it is possible to conclude that the aroma of polypropylene coffee capsule extraction is not damaged compared to a traditional espresso. [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]

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors' array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%. [ABSTRACT FROM AUTHOR]

Food poisoning is still the first cause of hospitalization worldwide and the most common microbial agent, Campylobacter jejuni, is the most commonly reported gastrointestinal disease in humans in the EU (European Union) as is reported by the European Union One Health 2018 Zoonoses Report styled by the EFSA (European Food Safety Authority) and ECDC (European Center for Disease Prevention and Control). One of the vehicles of transmission of this disease is milk. Nanostructured MOS (Metal Oxide Semiconductor) sensors have extensively demonstrated their ability to reveal the presence and follow the development of microbial species. The main objective of this work was to find a set up for the detection and development follow up of C. jejuni in milk samples. The work was structured in two different studies, the first one was a feasibility survey and the second one was to follow up the development of the bacteria inside milk samples. The obtained results of the first study demonstrate the ability of the sensor array to differentiate the contaminated samples from the control ones. Thanks to the second study, it has been possible to find the limit of microbial safety of the contaminated milk samples. [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]

Jams are appreciated worldwide and have become a growing market, due to the greater attention paid by consumers for healthy food. The selected products for this study represent a segment of the European market that addresses natural products without added sucrose or with a low content of natural sugars. This study aims to identify volatile organic compounds (VOCs) that characterize three flavors of fruit and five recipes using gas chromatography–mass spectrometry (GC–MS) and solid-phase micro-extraction (SPME) analysis. Furthermore, an innovative device, a small sensor system (S3), based on gas sensors with nanomaterials has been used; it may be particularly advantageous in the production line. Results obtained with linear discriminant analysis (LDA) show that S3 can distinguish among the different recipes thanks to the differences in the VOCs that are present in the specimens, as evidenced by the GC–MS analysis. Finally, this study highlights how the thermal processes for obtaining the jam do not alter the natural properties of the fruit. [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]

Campylobacter spp infection affects more than 200,000 people every year in Europe and in the last four years a trend shows an increase in campylobacteriosis. The main vehicle for transmission of the bacterium is contaminated food like meat, milk, fruit and vegetables. In this study, the aim was to find characteristic volatile organic compounds (VOCs) of C. jejuni in order to detect its presence with an array of metal oxide (MOX) gas sensors. Using a starting concentration of 103 CFU/mL, VOCs were analyzed using Gas-Chromatography Mass-Spectrometry (GC-MS) with a Solid-Phase Micro Extraction (SPME) technique at the initial time (T0) and after 20 h (T20). It has been found that a Campylobacter sample at T20 is characterized by a higher number of alcohol compounds that the one at T0 and this is due to sugar fermentation. Sensor results showed the ability of the system to follow bacteria curve growth from T0 to T20 using Principal Component Analysis (PCA). In particular, this results in a decrease of ΔR/R0 value over time. For this reason, MOX sensors are a promising technology for the development of a rapid and sensitive system for C. jejuni. [ABSTRACT FROM AUTHOR]

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent. [ABSTRACT FROM AUTHOR]