Critical Reviews In Food Science and Nutrition

Cannabidiol (CBD) in industrial hemp is a promising functional food ingredient with multifarious health benefits, including anticancer activity, antioxidant activity, anti-inflammatory properties, and anxiolytic effects. In recent years, the application of CBD in the food industry has been emerging and several CBD fortified products are available across the globe. Currently, the scientific information associated with CBD are segregated, and there is a lack of connectivity between their recent explorations. Therefore, in this review, the findings associated with CBD that are crucial for extending its food applications are comprehensively discussed. It begins by exploring the global regulatory landscape of CBD. Followingly, the factors that affect CBD production in the field, CBD isolation techniques from industrial hemp flowers, and their functional properties are comprehensively detailed. Importantly, this review examines reported delivery systems for enhancing the physicochemical properties and bioavailability of CBD, thus broadening its potential applications in the food industry. Overall, this review would connect the patches of CBD information available from the field to food and would be resourceful for food scientists, regulatory agencies, and hemp farmers.

In order to provide a further overview of the bioactivities of food-derived umami peptides, the study reviewed the sources, preparation and screening, flavor presentation properties, functional properties and their recent progress in the food industry. Food-derived umami peptides could be extracted and prepared by enzymatic and fermentation techniques. Machine learning, molecular docking technology and peptidomics are efficient methods for screening and identifying umami peptides and exploring their flavor mechanisms. In addition to the flavor properties, some of the umami peptides have a variety of functional properties, including antioxidant, hypolipidemic and hypoglycemic properties. Therefore, these umami peptides could not only be used as raw materials for traditional seasonings, but also be developed for the field of functional foods. This review aims to comprehensively summarize the flavor properties and functional properties of umami peptides, providing new directions for the exploitation and utilization of umami peptides in the food industry.

Intelligent packaging (also called smart packaging) has garnered significant attention for its ability to monitor food quality and spoilage during storage, without compromising packaging integrity. Natural pigments are being explored as sensors of food properties in these applications, but they are often chemically unstable, which limits their widespread utilization for this purpose. Therefore, enhancing pigment stability is imperative. This review article discusses the different types of natural pigments used in intelligent packaging applications. It then highlights the origin of their chemical instability and the factors that impact it, before outlining potential strategies to improve their stability. Natural pigments used in intelligent packaging include phenolic-, quinone-, pyrrole-, polyene- and betalain-based molecules, which are characterized by their unique chemical structures, especially the presence of conjugated structures that selectively absorb visible light. The environmental conditions that trigger a color-change in each pigment are highlighted. Pigment stability can be enhanced through a variety of approaches, including molecular modifications, pigment encapsulation, and label matrix design. These approaches modify the microenvironment of the pigments, thereby increasing their stability. These pigment enhancement methods hold promise for the development of intelligent packaging materials that can be used in the food and other industries.

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens. Simultaneously, the ambition toward establishing a sustainable food system has motivated the adoption of cutting-edge technologies such as Artificial Intelligence to strengthen food security. Given the myriad challenges confronting stakeholders in their endeavors to safeguard food security, Artificial Intelligence emerges as a promising tool capable of crafting holistic management strategies for food security. This entails maximizing crop yields, mitigating losses, and trimming operational expenses. AI models present notable benefits in efficiency, precision, uniformity, automation, pattern identification, accessibility, and scalability for food security endeavors. The escalation in the global trend for adopting alternative protein sources such as edible insects and microalgae as a sustainable food source reflects a growing recognition of the need for sustainable and resilient food systems to address the challenges of population growth, environmental degradation, and food insecurity. Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.

This review systematically explores the emerging perspectives on analytical techniques and machine learning applications in food metabolomics, with a focus on their roles in the era of Industry 4.0. The study emphasizes the utilization of chromatography-mass spectrometry and proton nuclear magnetic resonance spectroscopy as primary tools for metabolic profiling, highlighting their respective strengths and limitations. LC-MS, known for its high sensitivity and specificity, faces challenges such as complex data interpretation and the need for advanced computational tools.1H NMR offers reproducibility and quantitative accuracy but struggles with lower sensitivity compared to mass spectrometry. The review also delves into the integration of multivariate data analysis techniques like principal component analysis and partial least squares-discriminant analysis, which enhance data dimensionality reduction and pattern recognition, yet require careful validation to avoid overfitting. Furthermore, the application of machine learning algorithms, including random forests and support vector machines, is discussed in the context of improving classification and predictive tasks in food metabolomics. Practical applications of these technologies are demonstrated in areas such as quality control, nutritional studies, and food adulteration detection. The review emphasizes the need for standardization in methodologies and the development of more accessible and cost-effective analytical workflows. Future research directions include enhancing the sensitivity of 1H NMR, integrating metabolomics with other omics technologies, and fostering data sharing to build comprehensive reference libraries. This review aims to provide a comprehensive and critical overview of the current advancements and future potentials of analytical techniques and machine learning in food metabolomics, aligning with the goals of Industry 4.0.

Fermentation is widely known for its ability to enhance the food nutritional ingredients, and its products are with improved texture, flavor, and nutritional. Fermented blacken foods (FBF) are a unique group of foods characterized by their darker hue while retaining nutritional benefits. Despite their popularity, research on melanoidins, the compounds responsible for the characteristic color and health-promoting properties of FBF, remains limited. This review summarizes the formation, extraction, purification, structural features, and health benefits of melanoidins in FBF. The relationship between preparation methods, and physicochemical properties and bioactivities was elucidated. The formation of melanoidins in FBF, influenced by the Maillard reaction (MR) and microbial metabolic activity is analyzed, highlighting the dynamic nature of melanoidin synthesis in fermentation systems. Furthermore, the review addresses the characteristics of FBF production processes and the role of microorganisms and enzymes in melanoidin formation. Melanoidins in FBF exhibit diverse chemical compositions and molecular structures, influenced by precursor molecules, reaction pathways, and environmental factors. These compounds contribute to the sensory attributes, stability, and bioactivity of FBF including antioxidant, antimicrobial, and prebiotic properties. This review underscores the importance of melanoidins in FBF and their influences on food quality, nutrition, and health.

Promoting sustainable diets requires identifying those foods that balance nutritional quality, environmental impact, and cost, and taking cultural preferences into account. However, research on the sustainability of dietary habits in non-adult populations are limited. This cross-sectional study aims to investigate the relationship between different sustainability indicators and identify sustainable foods among those culturally acceptable by Portuguese children and adolescents. Dietary intake of 521 children and 633 adolescents was determined using food-diaries and 24-h recalls, respectively. Nutritional, environmental, and economic indicators were assessed for each food item identified as culturally acceptable among Portuguese children and adolescents. Spearman correlations were computed to assess the relationship between sustainability indicators. A sustainability score (0-3) was calculated to identify the most sustainable foods. Nutritional quality was positively correlated with greenhouse gas emissions and cost and inversely correlated with food industrial processing. Only around 10% of foods received a maximum sustainability score, namely fresh and processed vegetables, fresh fruit and fruit jars, legumes, pasta, rice and other grains, potatoes and other starchy tubers, natural and 100% fruit juices, and nectars. Overall, the most nutritious foods tend to have a higher environmental impact and cost, and few food options are simultaneously nutrient-rich, environmentally friendly and affordable.

The gut microbiome plays a crucial role in human health, affecting metabolic, immune, and cognitive functions. While the impact of various dietary components on the microbiome is well-studied, the effect of legumes remains less explored. This review examines the influence of legume consumption on gut microbiome composition, diversity, and metabolite production, based on 10 human and 21 animal studies. Human studies showed mixed results, with some showing increased microbial diversity and others finding no significant changes. However, legume consumption was linked to increases in beneficial bacteria like Bifidobacterium and Faecalibacterium. Animal studies generally indicated enhanced microbial diversity and composition changes, though these varied by legume type and the host’s health. Some studies highlighted legume-induced shifts in bacteria associated with better metabolic health. Overall, the review emphasizes the complexity of legume-microbiome interactions and the need for standardized methodologies and longitudinal studies. While legumes have the potential to positively affect the gut microbiome, the effects are nuanced and depend on context. Future research should investigate the long-term impacts of legume consumption on microbiome stability and its broader health implications, particularly for disease prevention and dietary strategies.