Veronica Sberveglieri

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.