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.

Seafood is both nutritionally and economically significant, with bivalve molluscs being particularly valuable for monitoring environmental pollutants due to their filter-feeding nature and ability to bioaccumulate pollutants. While not often linked to food poisoning, these molluscs can occasionally introduce health risks, highlighting the need for vigilant monitoring. This review provides a thorough analysis of pollutants—including persistent and emerging pollutants, as well as marine toxins—found in bivalve molluscs between 2019 and 2024. Among the studied pollutants, plasticizers and alkaloids are the most frequently analyzed, with liquid and gas chromatography (GC) tandem mass spectrometry (MS) the predominant methods, although novel approaches to determine these compounds, such as sensors, have also emerged in recent years. However, many studies are focused on establishing pollutant content without addressing bioaccumulation (BA) factors, and a lack of standardization in species and sampling locations complicates comparisons between the different published works. Despite some studies linking human activity and algal blooms to BA dynamics, more comprehensive research is needed. Additionally, limited data on the depuration capacity of molluscs underscores the need for further investigation. Although pollutant levels generally remain within legal limits, many substances remain unregulated. Environmental factors also play a critical role in influencing BA, emphasizing the need for future studies to focus on BA factors to better understand these complex dynamics.

In this perspective article, several internationally recognized experts, members of the editorial team of this journal, discuss a selection of current hot topics identified in Food Science and Foodomics. The topics are comprised of the main areas of Food Science and Foodomics, namely, food safety, food authenticity, food processing, and food bioactivity. Logically, several of the discussed topics involve more than one of the mentioned main areas. Regarding food safety, the topics discussed are the use of analytical nanotechnology, nanometrology, nano-chromatography; the determination of organic contaminants based on MS and NMR; the impact of microplastics and nanoplastics on food or the contamination of foods with plant toxins. Regarding food authenticity, the paper discusses the role of MS, NMR, biosensors and the new trends in foodomics for food authentication. In terms of food processing, the work shows interesting perspectives on novel processing technologies, the effect of food processing on the gut microbiota or in the interaction among secondary metabolites and macromolecules; the development of active packaging, and the potential effects of introducing recycled plastics in food packaging; the new green extraction and encapsulation strategies of bioactive compounds from food by-products; and the anti-biofilm capacity of natural compounds/extracts/vegetal oils and essential oils. Food bioactivity and the relation between food and health includes the bioavailability and bioaccessibility of bioactive compounds; new trends and challenges in the interaction of nutraceuticals with biological systems; how food matrix impacts the bioaccessibility of nutrients and bioactive compounds; or the study of biodiversity, food and human health through one-health concept. We anticipate elaborations on these hot topics will promote further studies in Food Science and Foodomics.

Sterigmatocystin (STE) is a possible human carcinogenic compound (2B) according to the International Agency for Research on Cancer classification. Structurally, STE is a precursor to aflatoxins, sharing a similar polyketide-derived biosynthetic pathway, which underscores its toxicological relevance. It has been reported to occur in a variety of foodstuffs including cereals and cereal-based products, spices, cheese, and nuts, among others. STE poses a substantial challenge to food safety and addressing this issue requires a comprehensive strategy encompassing prevention, monitoring, and regulation to protect both human and animal health from its harmful effects. The present paper presents the analytical methodologies for the determination of STE in foodstuffs and the reported levels of STE in food, based on a review of scientific publications from 2021 to 2024. Significative progress has been made in the development of analytical methodologies for STE determination in food; however, further advancements in analytical techniques, standardized protocols, and monitoring are essential to improve risk assessment and guide effective mitigation strategies.

Edible oils are essential in daily diet due to their high contribution of fatty acids, fat-soluble vitamins, and triglycerides. During the processes of growing, processing, storage, transport, or packaging, they can be contaminated by ubiquitous phthalate esters, considered endocrine disruptors. Thus, analytical methodologies to allow tight control of their presence are mandatory. Several sample treatments have been considered in this study: microwave-assisted extraction (MAE), liquid-liquid extraction (LLE), dispersive solid phase extraction (DSPE), magnetic SPE (MSPE), solid phase microextraction (SPME), Surface-enhanced Raman spectroscopy (SERS) or its combinations. However, the selection of an analytical procedure is usually performed from an Analytical Chemistry perspective, without considering factors such as the sustainability of the selected methodology and/or its impact on the environment. In this sense, the Green Analytical Chemistry strategy can play an important role. To demonstrate this fact, six different analytical procedures have been evaluated in terms of sustainability by using several greenness evaluation tools. Procedures from MAE-gel permeation chromatography (GPC)-SPE till SERS, showing adequate analytical characteristics, have been selected. The application of Analytical GREEnness calculator (AGREE), AGREE preparation (AGREE prep), and blue applicability grade index (BAGI) tools showed that MAE-GPC-SPE was the less green analytical procedure while SERS was the greener one. The BAGI evaluation showed that headspace (HS) and SERS were the most applicable procedures.

This review examined the potential of hemp components as functional feed and food ingredients, focusing on their impact on the quality and nutritional value of animal products. Following hemp legalization, there was growing interest in its potential to enhance animal diets and processed animal products due to its rich nutritional profile, including high levels of polyunsaturated fatty acids (PUFA), essential amino acids, and fibre. Incorporating hemp components into feed for monogastric animals, particularly poultry, improved lipid stability, sensory attributes, and the fatty acid composition of meat and eggs. Hemp supplementation for ruminants, especially in goats, increased PUFA and conjugated linoleic acid (CLA) content in milk, improved meat tenderness, and enhanced oxidative stability. However, research on hemp supplementation for pigs and beef remained limited, indicating the need for further exploration of these species. Hemp cake, rich in protein, fibre, and essential fatty acids, was the most widely used hemp component due to its economic viability, nutritional benefits, and sustainability, contributing to improved meat and milk quality. Regulatory concerns about the transfer of tetrahydrocannabinol (THC) residues in the produced animal products restricted the use of hemp biomass. In processed animal products, hemp components were studied for their potential to enhance nutritional value, replace animal fats, and serve as natural preservatives. Although they improved the fatty acid profile and antioxidant properties of meat products, challenges such as textural changes and increased lipid oxidation needed to be addressed for optimal use. Limited studies on dairy products indicated promising nutritional enhancements, but textural issues could impact consumer acceptance. In conclusion, hemp components show significant potential for improving the quality and nutritional value of animal products. Further research is necessary to address regulatory, sensory, and formulation challenges and to expand their application across different animal species and processed animal products.

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.

Artificial intelligence (AI) is revolutionizing plant sciences by enabling precise plant species identification, early disease diagnosis, crop yield prediction, and precision agriculture optimization. AI uses machine learning and image recognition to aid ecological research and biodiversity conservation. It plays a crucial role in plant breeding by accelerating the development of resilient, high-yielding crops with desirable traits. AI models using climate and soil data contribute to sustainable agriculture and food security. In plant phenotyping, AI automates the measurement and analysis of plant characteristics, enhancing our understanding of plant growth. Ongoing research aims to improve AI models’ robustness and interpretability while addressing data privacy and algorithmic biases. Interdisciplinary collaboration is essential to fully harness AI’s potential in plant sciences for a sustainable, food-secure future.

Pickering emulsions have emerged as suitable alternatives to healthily and sustainably deliver unstable compounds, addressing the demands of consumers, increasingly concerned about the nutritional value and environmental impact of the products they consume. They are stabilized by insoluble solid particles that partially hydrate both the oil (O) and aqueous (W) phases through a combination of steric and electrostatic repulsions determined by their surface properties. Since the desorption energy of the particles is very high, their adsorption is considered irreversible, which accounts for their greater stability compared to conventional emulsions. Proteins and polysaccharides, used either individually or in combination, can stabilize Pickering emulsions, and recent studies have revealed that microorganisms are also suitable stabilizing particles. This review provides an overview of recent research on Pickering emulsions, highlighting the properties of the stabilizing particles, and their ability to deliver hydrophobic and/or unstable compounds. The use of Pickering emulsions as fat-replacers, edible inks for 3D-printing or their incorporation into packaging material are also presented and discussed, pointing out their great potential for further innovation.