Metal oxide materials have been applied in different fields due to their excellent functional properties. Metal oxides nanostructuration, preparation with the various morphologies, and their coupling with other structures enhance the unique properties of the materials and open new perspectives for their application in the food industry. Chemical gas sensors that are based on semiconducting metal oxide materials can detect the presence of toxins and volatile organic compounds that are produced in food products due to their spoilage and hazardous processes that may take place during the food aging and transportation. Metal oxide nanomaterials can be used in food processing, packaging, and the preservation industry as well. Moreover, the metal oxide-based nanocomposite structures can provide many advantageous features to the final food packaging material, such as antimicrobial activity, enzyme immobilization, oxygen scavenging, mechanical strength, increasing the stability and the shelf life of food, and securing the food against humidity, temperature, and other physiological factors. In this paper, we review the most recent achievements on the synthesis of metal oxide-based nanostructures and their applications in food quality monitoring and active and intelligent packaging.

To determine the originality of a typical Italian Parmigiano Reggiano cheese, it is crucial to define and characterize its quality, ripening period, and geographical origin. Different analytical techniques have been applied aimed at studying the organoleptic and characteristic volatile organic compounds (VOCs) profile of this cheese. However, most of the classical methods are time consuming and costly. The aim of this work was to illustrate a new simple, portable, fast, reliable, non-destructive, and economic sensor device S3 based on an array of six metal oxide semiconductor nanowire gas sensors to assess and discriminate the quality ranking of grated Parmigiano Reggiano cheese samples and to identify the VOC biomarkers using a headspace SPME-GC-MS. The device could clearly differentiate cheese samples varying in quality and ripening time when the results were analyzed by multivariate statistical analysis involving principal component analysis (PCA). Similarly, the volatile constituents of Parmigiano Reggiano identified were consistent with the compounds intimated in the literature. The obtained results show the applicability of an S3 device combined with SPME-GC-MS and sensory evaluation for a fast and high-sensitivity analysis of VOCs in Parmigiano Reggiano cheese and for the quality control of this class of cheese.

In the present work, a gas sensor device S3 based on an array of eight metal oxides semiconductor gas sensors has been demonstrated and applied to the discrimination of quality and geographical origins of the Italian extra virgin olive oils. Furthermore, the principal component analysis (PCA) and artificial neural networks (ANN) were carried out on the set of data acquired from the sensor array response to the extra virgin olive oil headspace. The preliminary results have shown a good capability of the instrument to distinguish different kind of extra virgin olive oil samples and thus evaluate their quality and origin.