Rapid safety and quality control during fish shelf-life by using a portable device.

BACKGROUND: Fish consumption is increasing nowadays both because of its positive role for health due to the abundant presence of unsaturated fatty acids and for its use in many new food preparations (e.g. raw fillet used for uncooked sushi and sashimi dishes). The growing food industry and increased demand for the long-term storage and preservation of food have created the need to develop methods that can easily track and preserve food freshness and safety throughout shelf-life (production, storage, shipment, and consumption). While E-nose technologies have already been used and tested for these purposes, scarce information is available in the literature on the feasibility of using other food devices to detect changes in perishable food like fish during shelf-life in order to predict and correctly manage all food storage phases. The aim of the present study was to investigate the potential of Food Sniffer® portable devices to define the quality and safety of salmon fillet and burger (Salmo salar) packaged in modified atmosphere at two refrigerated conditions (4 and 8 °C). RESULTS: An increase in biogenic amines and volatile compounds especially ketones and alcohols were observed, with large amounts at final storage times of 8 °C temperature. CONCLUSION: The Food Sniffer® application was able to anticipate unacceptability conditions of salmon samples also correlated with chemical and microbiological parameters. This could represent a valid support for food industry and retail to manage perishable food commodities preventing possible food risk as well. © 2020 Society of Chemical Industry.

Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach.

In this paper, we focus on a Bayesian network s approach to combine traditional survey and social network data and official statistics to evaluate well-being. Bayesian networks permit the use of data with different geographical levels (provincial and regional) and time frequencies (daily, quarterly, and annual). The aim of this study was twofold: to describe the relationship between survey and social network data and to investigate the link between social network data and official statistics. Particularly, we focused on whether the big data anticipate the information provided by the official statistics. The applications, referring to Italy from 2012 to 2017, were performed using ISTAT's survey data, some variables related to the considered time period or geographical levels, a composite index of well-being obtained by Twitter data, and official statistics that summarize the labor market.

How scary! An analysis of visual communication concerning genetically modified organisms in Italy.

Several studies provide evidence of the role of written communication in influencing public perception towards genetically modified organisms, whereas visual communication has been sparsely investigated. This article aims to evaluate the exposure of the Italian population to scary genetically modified organism-related images. A set of 517 images collected through Google are classified considering fearful attributes, and an index that accounts for the scary impact of these images is built. Then, through an ordinary least-squares regression, we estimate the relationship between the Scary Impact Index and a set of variables that describes the context in which the images appear. The results reveal that the first (and most viewed) Google result images contain the most frightful contents. In addition, the agri-food sector in Italy is strongly oriented towards offering a negative representation of genetically modified organisms. Exposure to scary images could be a factor that affects the negative perception of genetically modified organisms in Italy.