Cost Actions are competitive projects funded by the European Cooperation in Science and Technology (COST) organisation with the main objective of promoting the creation of research networks in innovative areas, facilitating the collaboration between the academia and the industry in Europe and beyond. In this European framework, in March 2020, it has been approved the Cost Action entitled “European Network for assuring food integrity using nondestructive spectral sensors” (SensorFINT). The main general aim of the Action is to create within the EU a vibrant and multidisciplinary network, combining experience in research, manufacture, training and technology transfer in relation to non-destructive spectral sensors, which can accelerate its implementation within the food industry. Furthermore, it will generate and disseminate knowledge about these emerging and innovative technologies and their application for the real-time in situ control of critical quality, safety, authenticity, and performance attributes for raw and in-process materials, i.e. in the entire food chain, allowing to increase the transfer of knowledge from academia to the industry and, therefore, to improve European food industry competitiveness. Currently, the increasing complexity of food supply chains has provided more opportunities for food fraud, resulting in many food crises over the years (BSE, melamine, horse meat, fipronil in eggs, etc.), which reduces the confidence of the consumers in the industry, inspectors, and policy makers. These scandals have placed increased focus on developing measures to ensure the integrity of the food in the whole chain, and thereby reduce the incidences of food fraud. The analytical needs for the agri-food industry are linked not only to compliance with regulations but also to the need to control their processes through an “intelligent quality control,” along with knowing the variability of raw materials and the final product for increasing its competitiveness. Inaccurate or uninformative quality and safety assessment methodologies are detrimental to producers, processors and ultimately to consumers of food products. In addition, new strategies related to the adoption of “non-targeted” methods—able to analyse the product and produce a food fingerprint that can provide information on quality and authenticity— are demanded. Therefore, to verify integrity in marketed products, it is necessary to update the current analytical and sampling control systems, through the development of modern and cost-effective analytical methods. This situation has forced the food businesses to rethink their risk mitigation processes, especially as food fraud is opportunistic and can be difficult to detect through classical analytical methods that look for specific components in the food. Traditional methods of analysis are too slow and expensive to facilitate adequate production, but the nature of non-destructive spectral sensors, combined with specific data processing techniques, fits perfectly with these needs. Spectral sensors enable rapid, non-destructive, accurate, and cost-effective analysis of large numbers of samples and the measurement of multiple parameters in a variety of products and processes. One of its main advantages is related to the large amount of product that can be analysed when it works in continuous mode. SensorFINT is focused in answering this problematic through the use of non-destructive spectral sensors. Among the available spectral sensors, near infrared spectroscopy (NIRS) is currently one of the most suitable for implementation within the food industry. But also the Action will consider other spectral technologies—as fluorescence, Raman, thermal or time-resolved spectroscopy, and their fusion or combination with multi-spectral imaging—to provide solutions for critical issues that cannot be managed just with a sensor alone. Most applications of these technologies in the food industry are made at-line. Industry requires them to be deployed in situ and preferably online for full process control over the entire food chain. These requirements introduce constraints on sensor design and calibration
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