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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]

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]

Graphical abstract Highlights • Bacterial cellulose as organic substrate for a new generation of MOX sensors. • ZnO integration on bacterial cellulose as semiconductor sensing material. • MOS semiconductor organic sensor prototype assembly. • Room temperature sensor activation through the UV light and measurement. • Room temperature measurements. • Good response to gas test as NO 2 , Acetone, Ethanol and H 2. Abstract Recently a high interest has grown around the use of organic materials, as cellulose, in engineering and electronic fields due to the possibility to create hybrid devices. The objective of this work was to create a new generation of biocompatible bacterial cellulose Metal Oxides (MOX) gas sensors. ZnO semiconductor sensing material was used due to its well-known functional performances and the UV light activation possibility at room temperature. Bacterial cellulose membranes have been produced, active ZnO layer was deposited and the final sensing device was assembled and tested in presence of different chemical compounds. The prototypes showed good response to nitrogen dioxide, acetone and ethanol at room temperature, demonstrating the possibility to use bacterial cellulose as eco and environmental friendly composite transducers material for, but not limited to, chemical sensors. [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 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]