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.