Bioactive compounds (BCs) are key components in the development of functional foods (FFs) that offer health benefits beyond basic nutrition. Compounds such as polyphenols, carotenoids, flavonoids, fatty acids, fiber, and probiotics contribute to metabolic regulation, immune support, and a reduced risk of chronic diseases, including cardiovascular conditions, diabetes, and certain types of cancer. This review examines the classification, mechanisms of action, and safety considerations of BCs, drawing on recent research and the framework of the Functional Food Center (FFC). It also highlights the impact of environmental factors on BC composition and explores technologies, such as encapsulation, to improve bioavailability.        Functional food science (FFS) emerges as an interdisciplinary field linking food technology, biomedical research, and nutrition policy. The review highlights the importance of standardized terminology, regulatory clarity, and scientifically validated interventions in advancing FF development and combating non-communicable diseases globally. Novelty: This review offers a structured and integrative framework on FF that unites scientific, technological, and regulatory perspectives on BC. It highlights the value of FFS as a discipline that proposes updated, evidence-based definitions for FF and BC. The focus on dosage, delivery mechanisms, and long-term efficacy provides a framework for future FF evaluations designed for targeted health outcomes. Keywords: bioactive compounds, pharmaceutical bioactive compounds, functional food science, functional foods, Functional Food Center

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Food formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and iterative refinement. Now, artificial intelligence offers the opportunity to accelerate this process. Yet despite rapid advances across food science, most AI applications remain isolated prediction and optimization tasks rather than parts of a broader scientific approach. Here we integrate these emerging technologies into a unified framework--the generative science of food formulation--in which digital food representations enable artificial intelligence to predict, discover, generate, organize, simulate, and optimize. We illustrate this approach through sustainability and nutrition, where generative artificial intelligence transforms environmental and nutritional metrics from post hoc evaluation criteria into explicit design objectives. Finally, we identify the data, models, benchmarks, and automation that will establish computational food design as a rigorous scientific discipline. Together, these advances have the potential to transform food formulation from an empirical discipline into a generative science.

Food comprises proteins, lipids, sugars and various other molecules that constitute a multicomponent biological system. It is challenging to investigate microscopic changes in food systems solely by performing conventional experiments. Molecular dynamics (MD) simulation serves as a crucial bridge in addressing this research gap. The Groningen Machine for Chemical Simulations (GROMACS) is an open-source, high-performing molecular dynamics simulation software that plays a significant role in food science research owing to its high flexibility and powerful functionality; it has been used to explore the molecular conformations and the mechanisms of interaction between food molecules at the microcosmic level and to analyze their properties and functions. This review presents the workflow of the GROMACS software and emphasizes the recent developments and achievements in its applications in food science research, thus providing important theoretical guidance and technical support for obtaining an in-depth understanding of the properties and functions of food.

The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health.

© 2020 Elsevier Ltd With current global challenges such as population growth, climate change and water scarcity, it is critical to develop sustainable strategies to achieve food security. One way to tackle this is by developing new products that use alternative and more sustainable ingredients. Bambara groundnut is a low-impact African legume as it can be grown on marginal soils and is resistant to high temperatures. The aim of this study was to investigate UK consumer acceptability and emotional response to snack products containing Bambara groundnut flour as an alternative sustainable ingredient. A key objective was to understand the contribution that measuring emotional response would reveal. Additionally the impact of extrinsic information on consumer acceptability and emotional response to snack products was investigated by sharing information concerning Bambara groundnut's sustainability and nutritional credentials. 100 UK participants were recruited to evaluate two biscotti and two cracker products. For each category a standard product made from standard ingredients sourced commercially, and one made replacing some of standard flour with Bambara flour were obtained. For each sample, participants were asked to rate their overall liking and emotional response based on sensory properties of the product (the blind condition). Participants were invited back for a second session, where they were informed about global resource challenges, and the sustainable features and nutritional value of Bambara, and which products contained this as an ingredient (informed condition). Under the blind condition, no significant differences in overall liking were observed between standard and Bambara products, indicating UK consumers accept the sensory properties of products that contain Bambara flour. Interestingly, the extrinsic information shifted consumer emotional response towards more positive emotions and less negative emotions when consuming products containing Bambara flour. It also made them felt less guilty when consuming the Bambara products, suggesting consumers engage with the idea of sustainable ingredients, and that this sustainable ingredient has potential for future new product development. It also highlighted the value of measuring emotional response for novel products to understand what may drive purchase behaviour when products are matched for liking. Food neophobia status did not impact product acceptability and emotional response between Bambara and standard products, however overall a lower emotional response was found for medium neophobic consumers in general who are more likely to evade novel products

Innovations in fluid mechanics are leading to better food since ancient history, while creativity in cooking inspires applied and fundamental science. Here, we review how recent advances in hydrodynamics are changing food science, and we highlight how the surprising phenomena that arise in the kitchen lead to discoveries and technologies across the disciplines, including rheology, soft matter, biophysics and molecular gastronomy. This review is structured like a menu, where each course highlights different aspects of culinary fluid mechanics. Our main themes include multiphase flows, complex fluids, thermal convection, hydrodynamic instabilities, viscous flows, granular matter, porous media, percolation, chaotic advection, interfacial phenomena, and turbulence. For every topic, we first provide an introduction accessible to food professionals and scientists in neighbouring fields. We then assess the state-of-the-art knowledge, the open problems, and likely directions for future research. New gastronomic ideas grow rapidly as the scientific recipes keep improving too.

Network pharmacology is an emerging interdisciplinary research method. The application of network pharmacology to reveal the nutritional effects and mechanisms of active ingredients in food is of great significance in promoting the development of functional food, facilitating personalized nutrition, and exploring the mechanisms of food health effects. This article systematically reviews the application of network pharmacology in the field of food science using a literature review method. The application progress of network pharmacology in food science is discussed, and the mechanisms of functional factors in food on the basis of network pharmacology are explored. Additionally, the limitations and challenges of network pharmacology are discussed, and future directions and application prospects are proposed. Network pharmacology serves as an important tool to reveal the mechanisms of action and health benefits of functional factors in food. It helps to conduct in-depth research on the biological activities of individual ingredients, composite foods, and compounds in food, and assessment of the potential health effects of food components. Moreover, it can help to control and enhance their functionality through relevant information during the production and processing of samples to guarantee food safety. The application of network pharmacology in exploring the mechanisms of functional factors in food is further analyzed and summarized. Combining machine learning, artificial intelligence, clinical experiments, and in vitro validation, the achievement transformation of functional factor in food driven by network pharmacology is of great significance for the future development of network pharmacology research.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Understanding how the gut responds to food has always been limited by the available investigatory techniques. Previous methods involving intubation and aspiration are largely limited to liquid-only meals. The aim of this review is to describe how MRI has allowed analysis of the processing of complex multiphase meals. This has demonstrated the role of physical factors such as viscosity, fat and fibre content in controlling gastric secretion and motility. It has also allowed the description of changes induced in small bowel water content and the role of osmotic effects of poorly absorbed carbohydrates such as fructose, sorbitol and mannitol. Intestinal secretions can be shown to be stimulated by a range of fruit and vegetables and the effect of this on colonic water content can also be measured. This has been used to demonstrate the mode of action of commonly used laxatives including bran and psyllium. The wealth of data which can be obtained together with its non-invasive nature and safety makes the technique ideal for the serial evaluation of the impact of different nutrients and drugs in both health and disease

In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physiological description of odors, aromas, and their hedonic properties. The method rooted in subgroup discovery belongs to the field of data science and especially data mining. It is still little used in the field of food and is based on a descriptive modeling of emotions on the basis of human physiological responses.

Food science encounters increasing complexity and challenges, necessitating more efficient, accurate, and sensitive analytical techniques. Mass spectrometry imaging (MSI) emerges as a revolutionary tool, offering more molecular-level insights. This review delves into MSI's applications and challenges in food science. It introduces MSI principles and instruments such as matrix-assisted laser desorption/ionization, desorption electrospray ionization, secondary ion mass spectrometry, and laser ablation inductively coupled plasma mass spectrometry, highlighting their application in chemical composition analysis, variety identification, authenticity assessment, endogenous substance, exogenous contaminant and residue analysis, quality control, and process monitoring in food processing and food storage. Despite its potential, MSI faces hurdles such as the complexity and cost of instrumentation, complexity in sample preparation, limited analytical capabilities, and lack of standardization of MSI for food samples. While MSI has a wide range of applications in food analysis and can provide more comprehensive and accurate analytical results, challenges persist, demanding further research and solutions. The future development directions include miniaturization of imaging devices, high-resolution and high-speed MSI, multiomics and multimodal data fusion, as well as the application of data analysis and artificial intelligence. These findings and conclusions provide valuable references and insights for the field of food science and offer theoretical and methodological support for further research and practice in food science.

The chief intent of this review is to explain the different extraction techniques and efficiencies for the recovery of protein from food waste (FW) sources. Although FW is not a new concept, increasing concerns about chronic hunger, nutritional deficiency, food security, and sustainability have intensified attention on alternative and sustainable sources of protein for food and feed. Initiatives to extract and utilize protein from FW on a commercial scale have been undertaken, mainly in the developed countries, but they remain largely underutilized and generally suited for low-quality products. The current analysis reveals the extraction of protein from FW is a many-sided (complex) issue, and that identifies for a stronger and extensive integration of diverse extraction perspectives, focusing on nutritional quality, yield, and functionality of the isolated protein as a valued recycled ingredient

Global food systems must deliver nutritious, sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical, and fragmented. Artificial intelligence (AI) offers a transformative path to link molecular composition to functional performance, connect chemical structure to sensory outcomes, and accelerate cross-disciplinary innovation across the production pipeline. While broadly applicable to food systems, we focus on sustainable proteins--plant-based, fermentation-derived, and cultivated--as a high-impact testbed for AI-driven closed-loop design. We review the applications, opportunities, and challenges of AI for Food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory science, manufacturing, and recipe generation. We identify four priorities: advancing scientific machine learning with embedded domain priors, treating food as a programmable biomaterial, building self-driving laboratories for automated discovery, and developing deep reasoning models that integrate nutrition and sustainability. Integrating AI responsibly into the food innovation cycle can accelerate the transition to sustainable food systems and establish a predictive, design-driven science of food for human and planetary health.

The size and morphology of particles impact the quality and functionality of food in derived particulate systems. This review presents an extensive bibliometric analysis targeting food science and technology. Through keyword compilation and bibliographic coupling analysis, the research topics identified are as follows: stability and functionality of food systems, ultrasound‐assisted processes, food oral processing and digestion, starch properties and modifications, and spray drying and microencapsulation. This comprehensive overview details the primary methods employed in particulate systems and highlights the influence of particles in the design of food systems.

© 2019 Institute of Food Technologists® Food science-based graduates are a significant proportion of the United Kingdom (UK) food and drink industry's workforce. Aside from technical standards, there is no cohesive competency framework to support key stakeholders; employers, students, and degree providers. Clarity of desirable competencies for the range of graduate opportunities available would enable students to undertake effective career planning and personal development, and educators to refine programs to satisfy market needs. Using a previously developed language tool, a Likert style, industry-wide online survey in UK and Republic of Ireland (ROI) was conducted involving 218 participants from a broad range of food science-related employment areas, evaluated by a combination of descriptive and multivariate data analysis. The survey outcomes indicate types of desirable competencies in a food science graduate. The tailored competency framework, Competencies for Food Graduate Careers (CFGC), is a set of 48 elements across eight themes, outlining which are desirable for each of 14 typical initial graduate roles. To enhance the quality of “oven-ready” graduates entering the food industry, it is recommended CFGC be used for careers education and competency development in higher education. The framework is aimed to have further applications for job specification development and also to improve awareness of careers in the food industry

Food is central to life. Food provides us with energy and foundational building blocks for our body and is also a major source of joy and new experiences. A significant part of the overall economy is related to food. Food science, distribution, processing, and consumption have been addressed by different communities using silos of computational approaches. In this paper, we adopt a person-centric multimedia and multimodal perspective on food computing and show how multimedia and food computing are synergistic and complementary. Enjoying food is a truly multimedia experience involving sight, taste, smell, and even sound, that can be captured using a multimedia food logger. The biological response to food can be captured using multimodal data streams using available wearable devices. Central to this approach is the Personal Food Model. Personal Food Model is the digitized representation of the food-related characteristics of an individual. It is designed to be used in food recommendation systems to provide eating-related recommendations that improve the user's quality of life. To model the food-related characteristics of each person, it is essential to capture their food-related enjoyment using a Preferential Personal Food Model and their biological response to food using their Biological Personal Food Model. Inspired by the power of 3-dimensional color models for visual processing, we introduce a 6-dimensional taste-space for capturing culinary characteristics as well as personal preferences. We use event mining approaches to relate food with other life and biological events to build a predictive model that could also be used effectively in emerging food recommendation systems.

Furan is generally produced during thermal processing of various foods including baked, fried, and roasted food items such as cereal products, coffee, canned, and jarred prepared foods as well as in baby foods. Furan is a toxic and carcinogenic compound to humans and may be a vital hazard to infants and babies. Furan could be formed in foods through thermal degradation of carbohydrates, dissociation of amino acids, and oxidation of polyunsaturated fatty acids. The detection of furan in food products is difficult due to its high volatility and low molecular weight. Headspace solid-phase microextraction coupled with gas chromatography/mass spectrometer (GC/MS) is generally used for analysis of furan in food samples. The risk assessment of furan can be characterized using margin of exposure approach (MOE). Conventional strategies including cooking in open vessels, reheating of commercially processed foods with stirring, and physical removal using vacuum treatment have remained unsuccessful for the removal of furan due to the complex production mechanisms and possible precursors of furan. The innovative food-processing technologies such as high-pressure processing (HPP), high-pressure thermal sterilization (HPTS), and Ohmic heating have been adapted for the reduction of furan levels in baby foods. But in recent years, only HPP has gained interest due to successful reduction of furan because of its nonthermal mechanism. HPP-treated baby food products are commercially available from different food companies. This review summarizes the mechanism involved in the formation of furan in foods, its toxicity, and identification in infant foods and presents a solution for limiting its formation, occurrence, and retention using novel strategies

Sustainable Recipes is a tool that (1) connects food recipes ingredient lists with the closest organic providers to minimize the distance that food travels from farm to food preparation site and (2) recommends recipes given a GPS coordinate to minimize food miles. Sustainable Recipes provides consumers, entrepreneurs, cooking enthusiasts, and restauranteurs in the United States and elsewhere with an easy to use interface to help them (1) connect with organic ingredient producers to source ingredients to produce food recipes minimizing food miles and (2) recommend recipes using locally grown food. The main academic contribution of Sustainable Recipes is to bridge the gap between two streams of literature in data science of food recipes: studies of food recipes and studies of food supply chains. The outcomes of the interphase are (1) a map visualization that highlights the location of the producers that can supply the ingredients for a food recipe along with a ticket consisting of their contact addresses and the food miles used to produce a recipe and (2) a list of recipes that minimize food miles for a given GPS coordinate in which the recipe is going to be produced.

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

© 2020 Institute of Food Technologists® It is fundamental that students are able to identity where they have developed specific professional competencies during their study. This ensures students can not only articulate their competencies well in job applications and assessments but also draw on their experiences for use in the workplace. The aim of study was to ascertain if desirability of an element or competency as indicated by employers, was reflected in an equivalent level of program content, appropriately perceived by the student. A case study approach mapped elements of the previously developed Competencies for Food Graduate Careers framework against food sciences curricula at University of Nottingham. The mapping process facilitated evaluation of appropriate levels of inclusion of each element in degree programs, by recording types of content and experiences provided, in collaboration with teaching staff. Perspectives of the student experience were captured using an online survey. In addition, guidance from a prior industry survey provided context of the level of desirability for each element across the range of graduate roles in the United Kingdom. The results showed some areas of mismatch, where curricular content did not align with employer expectations or student perceptions. This has informed review of this curriculum, to best reflect “competency development” to meet the needs of the food industry. Recommendations were made to address gaps through enhancement of content, delivery, communication, or assessment. Additionally, the exercise has suggested a more informed development of curricula categorization and coding for future similar mapping activities

Food is very essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding the human behavior, improving the human health and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from disparate sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food related issues in medicine, biology, gastronomy and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, vast amounts of work has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews, which shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this paper, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.

Functional food science has witnessed remarkable advancements in recent years, driven by our evolving understanding of quantum mechanics and the concept of time. The interplay between these two theories, namely the Quantum theory and the Tempus theory, has opened new avenues for research and innovation in functional food. Functional food science integrates these theories to better understand the complex relationship between food, chronic disease, and health. Quantum theory explores the molecular-level interplay between energy, matter, and bioactive compounds in functional foods, optimizing their development and utilization for chronic disease and health. The Tempus theory emphasizes timed consumption, aligning functional foods with circadian rhythms and metabolic processes for enhanced nutrient absorption, utilization, and metabolic responses. By synchronizing food intake with the body's natural rhythms, the Tempus theory enhances the efficacy of functional foods in promoting health and preventing disease. Integrating the quantum and tempus theories in functional food science provides a comprehensive approach to understanding and utilizing the potential of functional foods for personalized nutritional interventions and improving overall well-being.Keywords: functional food science, Quantum theory, Tempus theory, bioactive compounds, cellular interactions, timing, health promotion, personalized nutrition.

This study has for the first time shown that complex food emulsifiers such as starch and protein can be applied to produce stable w/o/w emulsions with the membrane emulsification technology. Using a microporous metal membrane with a 20 μm pore size, 2% of polyoxyethylene (20) sorbitan monolaurate (Tween 20), 4% of octenyl succinic anhydride (OSA) starch or 1.5% of pea protein isolate (PPI) in the external water phase respectively was the minimum concentration necessary to stabilise the w/o/w droplets. Uniform with a span as low as 0.45 and for at least 13-day stable w/o/w emulsions of droplets between 35 and 320  μm were obtained. The release of a magnesium tracer from the internal water phase of xanthan gum-thickened w/o/w emulsions, when OSA starch and PPI were used, was found to be limited to around 3% after 13-day storage. However, w/o/w emulsions stabilised with Tween 20 were less stable with magnesium showing a release of 27% on day 13

This chapter explores the evolution, classification, and health implications of food processing, while emphasizing the transformative role of machine learning, artificial intelligence (AI), and data science in advancing food informatics. It begins with a historical overview and a critical review of traditional classification frameworks such as NOVA, Nutri-Score, and SIGA, highlighting their strengths and limitations, particularly the subjectivity and reproducibility challenges that hinder epidemiological research and public policy. To address these issues, the chapter presents novel computational approaches, including FoodProX, a random forest model trained on nutrient composition data to infer processing levels and generate a continuous FPro score. It also explores how large language models like BERT and BioBERT can semantically embed food descriptions and ingredient lists for predictive tasks, even in the presence of missing data. A key contribution of the chapter is a novel case study using the Open Food Facts database, showcasing how multimodal AI models can integrate structured and unstructured data to classify foods at scale, offering a new paradigm for food processing assessment in public health and research.

The Food and Hospitality Systems Graduate Program offers M.S. and Ph.D. graduate degrees under three track options: Food Science, Hospitality Management and Agricultural Systems Management. We also offer an online graduate certificate program in Food Safety and Defense. The catalog provides a complete list of these degree options (http:// catalog.missouri.edu/degreesanddegreeprograms/). F_S 1010: Introduction to Viticulture and Enology This course will give a general overview of growing grapes (viticulture) and winemaking (enology) with an emphasis on Missouri wines and wineries. This course is the first course in a sequence of courses in the viticulture and enology track of the food science degree program.

Diabetes mellitus is a chronic condition characterized by increased blood glucose levels from dysfunctional carbohydrate metabolism. Dietary intervention can help to prevent and manage the disease. Food hydrocolloids have been shown to have favorable properties in relation to glycaemic regulation. However, the use of food hydrocolloids of bacterial origin to modulate glucose responses is much less explored than other types of hydrocolloids. We, therefore, carried out the first review examining the impact of intake of food hydrocolloids of bacterial origin (as a direct supplement or incorporated into foods) on glycemic response in humans. Fourteen studies met the inclusion criteria. They used either xanthan gum, pullulan, or dextran as interventions. There was a wide variation in the amount of hydrocolloid supplementation provided and methods of preparation. Postprandial blood glucose responses were reduced in half of the studies, particularly at higher intake levels and longer chain hydrocolloids. When xanthan gum was added to the cooking process of muffins and rice, a significant reduction in postprandial blood glucose was observed. The use of these hydrocolloids is potentially effective though more research is needed in this area

A food composition knowledge base, which stores the essential phyto-, micro-, and macro-nutrients of foods is useful for both research and industrial applications. Although many existing knowledge bases attempt to curate such information, they are often limited by time-consuming manual curation processes. Outside of the food science domain, natural language processing methods that utilize pre-trained language models have recently shown promising results for extracting knowledge from unstructured text. In this work, we propose a semi-automated framework for constructing a knowledge base of food composition from the scientific literature available online. To this end, we utilize a pre-trained BioBERT language model in an active learning setup that allows the optimal use of limited training data. Our work demonstrates how human-in-the-loop models are a step toward AI-assisted food systems that scale well to the ever-increasing big data.