This article explores the significant impact that artificial intelligence (AI) could have on food safety and nutrition, with a specific focus on the use of machine learning and neural networks for disease risk prediction, diet personalization, and food product development. Specific AI techniques and explainable AI (XAI) are highlighted for their potential in personalizing diet recommendations, predicting models for disease prevention, and enhancing data-driven approaches to food production. The article also underlines the importance of high-performance computing infrastructures and data management strategies, including data operations (DataOps) for efficient data pipelines and findable, accessible, interoperable, and reusable (FAIR) principles for open and standardized data sharing. Additionally, it explores the concept of open data sharing and the integration of machine learning algorithms in the food industry to enhance food safety and product development. It highlights the METROFOOD-IT project as a best practice example of implementing advancements in the agri-food sector, demonstrating successful interdisciplinary collaboration. The project fosters both data security and transparency within a decentralized data space model, ensuring reliable and efficient data sharing. However, challenges such as data privacy, model interoperability, and ethical considerations remain key obstacles. The article also discusses the need for ongoing interdisciplinary collaboration between data scientists, nutritionists, and food technologists to effectively address these challenges. Future research should focus on refining AI models to improve their reliability and exploring how to integrate these technologies into everyday nutritional practices for better health outcomes.

Table olives are one of the most widespread fermented foods in the Mediterranean area, presenting an exponential increase in global consumption in the latest years. As a fermented product, its microbiota consists of a complex ecosystem, the composition of which depends on a multitude of factors and affects the quality attributes of the final product. The swiftly developing and constantly evolving field of omics technologies is being applied to unravel the profile of the microbial ecosystem and enable a deeper understanding of the fermentation process. In particular, the use of amplicon metagenomics facilitates the thorough analysis of the microbiota involved as it encompasses both culturable and unculturable microorganisms. Volatilomics aims at the identification and quantification of the volatile metabolites formed during fermentation with a direct involvement in the safety and quality evaluation of the food product. The integration of metagenomic and volatilomic data, through the application of bioinformatics can enhance the understanding of the interplay between the microbial profile and volatilome, resulting in a more comprehensive view of the system. This review summarized the overall amplicon metagenomics and volatilomics analytical approaches, along with the currently available bioinformatics tools for the data analysis in the field of table olives. Emphasis is given to the integration of amplicon metagenomic and volatilomic data employed to characterize the diversity of microbial populations and reveal the relationships between them and the volatile compounds. The latter may provide an extensive view of the microbial community dynamics, which is key in table olive fermentation and the microbiota’s functional properties. The potentiality to evaluate their effect in shaping the quality and unique features of the final product is highlighted.

Food products can contain various substances, including essential nutrients, as well as non-nutritive elements and potentially toxic metals. Metal contaminants have the potential to accumulate within the food chain and, when they exceed safe thresholds, can be toxic to humans, leading to health issues. To mitigate health hazards caused by exposure to such harmful substances, accurate monitoring of metal concentrations in various food samples is crucial. Achieving this goal needs understanding the basic principles of various elemental analysis methods. Additionally, selecting the appropriate technique or combination of techniques is critical for obtaining accurate and relevant results. Various advanced analytical techniques, such as atomic absorption spectroscopy, flame emission spectroscopy, inductively coupled plasma-mass spectrometry (ICP-MS), and X-ray fluorescence (XRF) spectrometry, can be used for the quantification of heavy metals and metalloids in food. However, each method has its own limitations, and the accuracy depends on adequate sample preparation. This paper aims to provide a clear overview of commonly used methods and techniques for heavy metal detection in food products, addressing the advantages and limitations of each analytical technique. Additionally, it compares the most important performance parameters of the presented techniques, including the limit of detection (LOD), the limit of quantification (LOQ), recovery, and precision. Moreover, ensuring food safety involves conducting a thorough risk assessment analysis. By integrating risk assessment into the evaluation of heavy metals in food, it becomes possible to determine whether observed concentrations pose significant risks to human health. This step is imperative for establishing regulatory guidelines and implementing control measures to reduce or eliminate potential health risks. Incorporating risk assessment into the broader context of the review enhances its applicability in real-world scenarios, aiding policymakers, regulatory bodies, and researchers in making informed decisions regarding food safety standards and practices.

This paper reviews the nutritional quality and safety of edible farmed insects from the point of view of the Czech-Slovenian bilateral project: Quality, Safety and Authenticity of Insect Protein-based Food and Feed (INPROFF). Insects as a sustainable source of dietary protein for animal feed and even humans, when integrated into the European agrifood system, could offer a solution to Europe’s feed protein deficit and help alleviate environmental pressure from increasing protein demand, such as declining availability of land, water, marine and energy resources, the overuse of pesticides and reduced biodiversity. However, despite a growing interest in the European Union (EU) in farming edible insects, many economic, scientific, technological, and social barriers remain. In response, Slovenia, represented by the Jožef Stefan Institute, the Biotechnical Faculty of the University of Ljubljana, and Jata Emona d.o.o. (the leading supplier of feedstuffs for the Slovenian market), a country with no history of insect rearing but an interest in alternative protein, joined with the Czech University of Life Sciences Prague, representing the Czech Republic—a country with a history of insect rearing and research into edible insects—to establish INPROFF a three-year bilateral project that aims to close the knowledge gap regarding the quality and safety of insect-based products and boost the farmed insect food and feed value chain. Specifically, it comprises three thematic pillars: (P1) nutritional enhancement, (P2) safety and quality and (P3) authenticity, traceability, and consumer acceptance. The paper also discusses the gaps in the metrological challenges of analysing insects, which will be critical for ensuring safety, quality, and sustainability. The paper finds that although much work has been done, many exciting avenues remain for new research.