Hasil untuk "Nutrition. Foods and food supply"

A. Tacon, M. Metian

K. Davis, S. Downs, Jessica A. Gephart

Akeem Olalekan Adelu, Margret Iyabode Dania

This study investigated the proximate composition, mineral content, physicochemical characteristics, bacterial viability, and sensory attributes of soybean–coconut composite milk yoghurts formulated in different ratios (100:0, 75:25, 50:50, 25:75, and 0:100) of soybean to coconut milk, codename SCMY01 to SCMY05. The moisture content of approximately 86% with no significant difference among the samples, dominates the proximate results. A gradual reduction in protein (3.79–1.21%) was observed with higher coconut substitution. Sample SCMY05 had the highest fat content (6.48%). Ash, crude fibre, and carbohydrate were highest in sample SCMY01 (0.71, 0.41, and 7.11%). Mineral analysis revealed that soybean-dominant samples were richer in calcium, sodium, magnesium, iron, and zinc, whereas potassium levels were significantly higher in coconut-rich formulations (202.25 to 220.10mg/100g). Physicochemical assessment revealed a decline in pH (4.35–3.90) and an increase in titratable acidity (0.70–1.05%) as the coconut content increased, accompanied by a substantial improvement in total solids (12.00–21.25%). No significant difference was observed in total soluble solid results. Microbial counts ranged from (7.2–8.5 log₁₀ cfu/ml), with soybean-based samples supporting relatively higher microbial growth. Sensory analysis revealed that 100% coconut milk yoghurt was most favoured in terms of colour, taste, flavour, and overall acceptability, whereas soybean-based yoghurt, despite being nutritionally superior, was less accepted due to its pronounced beany flavour. This study highlight soybean–coconut composite yoghurt as a sustainable and health-promoting alternative to dairy yoghurt, with prospects for broader consumer acceptance and commercialization.

Juan B. García Martínez, Jeffray Behr, Joshua Pearce et al.

Abstract Global catastrophic threats to the food system upon which human society depends are numerous. A nuclear war or volcanic eruption could collapse agricultural yields by inhibiting crop growth. Nuclear electromagnetic pulses or extreme pandemics could disrupt industry and mass-scale food supply by unprecedented levels. Global food storage is limited. What can be done?. This article presents the state of the field on interventions to maintain food production in these scenarios, aiming to prevent mass starvation and reduce the chance of civilizational collapse and potential existential catastrophe. The potential for rapid scaling, affordability, and large-scale deployment is reviewed for a portfolio of food production methods over land, water, and industrial systems. Special focus is given to proposing avenues for further research and technology development and to collating policy proposals. Maintaining international trade and prioritizing crops for food instead of animal feed or biofuels is paramount. Both mature, proven methods (crop relocation, plant-residue- and grass–fed ruminants, greenhouses, seaweed, fishing, etc.) and novel resilient foods are characterized. A future research agenda is outlined, including scenario characterization, policy development, production ramp-up and economic analyses, and rapid deployment trials. Governments could implement national plans and task forces to address extreme food system risks, and invest in resilient food solutions to safeguard citizens against global catastrophic food failure.

Yi Dong, Yusuke Muraoka, Scott Shi et al.

We present MM-Food-100K, a public 100,000-sample multimodal food intelligence dataset with verifiable provenance. It is a curated approximately 10% open subset of an original 1.2 million, quality-accepted corpus of food images annotated for a wide range of information (such as dish name, region of creation). The corpus was collected over six weeks from over 87,000 contributors using the Codatta contribution model, which combines community sourcing with configurable AI-assisted quality checks; each submission is linked to a wallet address in a secure off-chain ledger for traceability, with a full on-chain protocol on the roadmap. We describe the schema, pipeline, and QA, and validate utility by fine-tuning large vision-language models (ChatGPT 5, ChatGPT OSS, Qwen-Max) on image-based nutrition prediction. Fine-tuning yields consistent gains over out-of-box baselines across standard metrics; we report results primarily on the MM-Food-100K subset. We release MM-Food-100K for publicly free access and retain approximately 90% for potential commercial access with revenue sharing to contributors.

Saransh Kumar Gupta, Rizwan Gulzar Mir, Lipika Dey et al.

The global food landscape is rife with scientific, cultural, and commercial claims about what foods are, what they do, what they should not do, or should not do. These range from rigorously studied health benefits (probiotics improve gut health) and misrepresentations (soaked almonds make one smarter) to vague promises (superfoods boost immunity) and culturally rooted beliefs (cold foods cause coughs). Despite their widespread influence, the infrastructure for tracing, verifying, and contextualizing these claims remains fragmented and underdeveloped. In this paper, we propose a Food Claim-Traceability Network (FCN) as an extension of FKG[.]in, a knowledge graph of Indian food that we have been incrementally building. We also present the ontology design and the semi-automated knowledge curation workflow that we used to develop a proof of concept of FKG[.]in-FCN using Reddit data and Large Language Models. FCN integrates curated data inputs, structured schemas, and provenance-aware pipelines for food-related claim extraction and validation. While directly linked to the Indian food knowledge graph as an application, our methodology remains application-agnostic and adaptable to other geographic, culinary, or regulatory settings. By modeling food claims and their traceability in a structured, verifiable, and explainable way, we aim to contribute to more transparent and accountable food knowledge ecosystems, supporting researchers, policymakers, and most importantly, everyday consumers in navigating a world saturated with dietary assertions.

Ayobami Paul Abolade, Ibrahim Olanrewaju Lawal, Kamoru Lanre Akanbi et al.

Access to finance is vital for improving food security, particularly in developing nations where agricultural production is crucial. Despite several financial interventions targeted at increasing agricultural production, smallholder farmers continue to lack access to reasonable, timely, and sufficient financing, limiting their ability to invest in improved technology and inputs, lowering productivity and food supply. This study examines the relationship between access to finance and food security among smallholder farmers in Ogun State, employing institutional theory as a theoretical framework. The study takes a quantitative method, with a survey for the research design and a population of 37,200 agricultural smallholder farmers. A sample size of 380 was chosen using probability sampling and simple random techniques. The data were analysed via Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings demonstrate a favourable relationship between access to finance and food security, with an R2-value of 0.615 indicating a robust link. These findings underline the need of improving financial institutions and implementing enabling policies to enable farmers have access to the financial resources they need to achieve food security outcomes.

Darko Sasanski, Riste Stojanov

This comprehensive review explores food data in the Semantic Web, highlighting key nutritional resources, knowledge graphs, and emerging applications in the food domain. It examines prominent food data resources such as USDA, FoodOn, FooDB, and Recipe1M+, emphasizing their contributions to nutritional data representation. Special focus is given to food entity linking and recognition techniques, which enable integration of heterogeneous food data sources into cohesive semantic resources. The review further discusses food knowledge graphs, their role in semantic interoperability, data enrichment, and knowledge extraction, and their applications in personalized nutrition, ingredient substitution, food-drug and food-disease interactions, and interdisciplinary research. By synthesizing current advancements and identifying challenges, this work provides insights to guide future developments in leveraging semantic technologies for the food domain.

Belinda Lopéz-Galán, Tiziana de-Magistris

Abstract This study analyses the influence of geographical origin and taste on honey consumer behavior. First, we explore the influence of geographical origin on consumers’ hedonic evaluation of honey. We then assess the influence of geographical origin and taste on their willingness to pay (WTP) for honey. We conducted a field experiment at a real supermarket. The participants were exposed to two treatments (blind and informed treatment). The findings showed that knowledge about the geographical origin of honey influences consumers’ hedonic evaluations and that the WTP for honey is more strongly influenced by geographical origin than by taste.

Tanvi Kale, Vijay Hegde, Nikhil V Dhurandhar

EFSA Panel on Food Enzymes (FEZ), Holger Zorn, José Manuel Barat Baviera et al.

Abstract The food enzyme endo‐polygalacturonase ((1‐4)‐α‐d‐galacturonan glycanohydrolase, EC 3.2.1.15) is produced with the genetically modified Trichoderma reesei strain RF6197 by AB Enzymes GmbH. A safety evaluation of this food enzyme was made previously, in which EFSA concluded that this food enzyme did not give rise to safety concerns when used in five food manufacturing processes. Subsequently, the applicant has requested to extend its use to include two additional processes. In this assessment, EFSA updated the safety evaluation of this food enzyme when used in a total of seven food manufacturing processes. As the food enzyme–total organic solids (TOS) are removed from or not carried into the final foods in three food manufacturing processes, the dietary exposure to the food enzyme–TOS was estimated only for the remaining four processes. The dietary exposure was calculated to be up to 0.137 mg TOS/kg body weight (bw) per day in European populations. When combined with the no observed adverse effect level previously reported (1000 mg TOS/kg bw per day, the highest dose tested), the Panel derived a margin of exposure of at least 7299. Based on the new data, the revised margin of exposure and the previous evaluation, the Panel concluded that this food enzyme does not give rise to safety concerns under the revised intended conditions of use.

Emerita Dyah Ayu Purwita Sari, Hendra Budiman, Anita Agustina Styawan et al.

Brown rice (Oryza nivara S.D.Sharma & Shastry) is a rice variety that belongs to the Graminae family. Brown rice contains vitamins A, B, C, Zn and B complex. Vitamin B1 is one type of vitamin that is not stable. Its stability is influenced by pH, temperature and processing. The purpose of this study was to determine the comparison of vitamin B1 levels in brown rice and cooked brown rice. The study began with a qualitative test of vitamin B1 using 10% Pb acetate and 6 N NaOH if a yellow color and brown precipitate formed after heating, the sample was positive for vitamin B1. Determination of vitamin B1 levels in brown rice and cooked brown rice by alkalimetric method using NaOH as a titer that has been standardized in advance with potassium biftalat 0.1 N. Data analysis using the Mann Whitney test is an alternative to the Independent T-test if the t-test requirements are not met. The Mann Whitney test is used to determine whether or not there is a difference between two independent samples. The results of the qualitative test of vitamin B1 in brown rice and cooked brown rice showed that the samples were positive for vitamin B1. The quantitative test results of vitamin B1 levels in brown rice and cooked brown rice obtained an average of 12.40 mg / kg and 4.96 mg / kg. Statistical test results, the significance value (p) = 0.043, where p < 0.05 means there is a significant difference in vitamin B1 levels in brown rice and cooked brown rice. The conclusion of this study is that vitamin B1 levels in brown rice are higher than vitamin B1 levels in cooked brown rice.

Mengfei Chen, Mohamed Kharbeche, Mohamed Haouari et al.

How to ensure accessibility to food and nutrition while food supply chains suffer from demand and supply uncertainties caused by disruptive forces such as the COVID-19 pandemic and natural disasters is an emerging and critical issue. Unstable access to food influences the level of nutrition that weakens the health and well-being of citizens. Therefore, a food accessibility evaluation index is proposed in this work to quantify how well nutrition needs are met. The proposed index is then embedded in a stochastic multi-objective mixed-integer optimization problem to determine the optimal supply chain design to maximize food accessibility and minimize cost. Considering uncertainty in demand and supply, the multi-objective problem is solved in a two-phase simulation-optimization framework in which Green Field Analysis is applied to determine the long-term, tactical decisions such as supply chain configuration, and then Monte Carlo simulation is performed iteratively to determine the short-term supply chain operations by solving a stochastic programming problem. A case study is conducted on the beef supply chain in Qatar. Pareto efficient solutions are validated in discrete event simulation to evaluate the performance of the designed supply chain in various realistic scenarios and provide recommendations for different decision-makers.

Yuhao Chen, Jiangpeng He, Gautham Vinod et al.

Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets across various applications, ranging from classification and instance segmentation to 3D reconstruction. The polymorphic shapes and textures of food, coupled with high variation in forms and vast multimodal information, including language descriptions and nutritional data, make food computing a complex and demanding task for modern CV algorithms. 3D food modeling is a new frontier for addressing food related problems, due to its inherent capability to deal with random camera views and its straightforward representation for calculating food portion size. However, the primary hurdle in the development of algorithms for food object analysis is the lack of nutrition values in existing 3D datasets. Moreover, in the broader field of 3D research, there is a critical need for domain-specific test datasets. To bridge the gap between general 3D vision and food computing research, we introduce MetaFood3D. This dataset consists of 743 meticulously scanned and labeled 3D food objects across 131 categories, featuring detailed nutrition information, weight, and food codes linked to a comprehensive nutrition database. Our MetaFood3D dataset emphasizes intra-class diversity and includes rich modalities such as textured mesh files, RGB-D videos, and segmentation masks. Experimental results demonstrate our dataset's strong capabilities in enhancing food portion estimation algorithms, highlight the gap between video captures and 3D scanned data, and showcase the strengths of MetaFood3D in generating synthetic eating occasion data and 3D food objects.

Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das et al.

This paper presents an ontology design along with knowledge engineering, and multilingual semantic reasoning techniques to build an automated system for assimilating culinary information for Indian food in the form of a knowledge graph. The main focus is on designing intelligent methods to derive ontology designs and capture all-encompassing knowledge about food, recipes, ingredients, cooking characteristics, and most importantly, nutrition, at scale. We present our ongoing work in this workshop paper, describe in some detail the relevant challenges in curating knowledge of Indian food, and propose our high-level ontology design. We also present a novel workflow that uses AI, LLM, and language technology to curate information from recipe blog sites in the public domain to build knowledge graphs for Indian food. The methods for knowledge curation proposed in this paper are generic and can be replicated for any domain. The design is application-agnostic and can be used for AI-driven smart analysis, building recommendation systems for Personalized Digital Health, and complementing the knowledge graph for Indian food with contextual information such as user information, food biochemistry, geographic information, agricultural information, etc.

Maelle Moranges, Marc Plantevit, Moustafa Bensafi

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.

Magdalena Sevilla-González, Maria Fernanda Garibay-Gutiérrez, Arsenio Vargas-Vázquez et al.

Background: A risk haplotype in SLC16A11 characterized by alterations in fatty acid metabolism emerged as a genetic risk factor associated with increased susceptibility to type 2 diabetes (T2D) in Mexican population. Its role on treatment responses is not well understood. Objectives: We aimed to determine the impact of the risk haplotype on the metabolomic profile during a lifestyle intervention (LSI). Methods: We recruited Mexican-mestizo individuals with ≥1 prediabetes criteria according to the American Diabetes Association with a body mass index between 25 and 45 kg/m2. We conducted a 24-wk quasiexperimental LSI study for diabetes prevention. Here, we compared longitudinal plasma liquid chromatography/mass spectrometry metabolomic changes between carriers and noncarriers. We analyzed the association of risk haplotype with metabolites leveraging repeated assessments using multivariable-adjusted linear mixed models. Results: Before the intervention, carriers (N = 21) showed higher concentrations of hippurate, C16 carnitine, glycine, and cinnamoylglycine. After 24 wk of LSI, carriers exhibited a deleterious metabolomic profile. This profile was characterized by increased concentrations of hippurate, cinnamoglycine, xanthosine, N-acetylputrescine, L-acetylcarnitine, ceramide (d18:1/24:1), and decreased concentrations of citrulline and phosphatidylethanolamine. These metabolites were associated with higher concentrations of total cholesterol, triglycerides, and low density lipoprotein cholesterol. The effect of LSI on the risk haplotype was notably more pronounced in its impact on 2 metabolites: methylmalonylcarnitine (β: −0.56; P-interaction = 0.014) and betaine (β: −0.64; P-interaction = 0.017). Interestingly, lower consumption across visits of polyunsaturated (β: −0.038; P = 0.017) fatty acids were associated with higher concentrations of methylmalonylcarnitine. Covariates for adjustment across models included age, sex, genetic ancestry principal components, and body mass index. Conclusions: Our study highlights the persistence of deleterious metabolomic patterns associated with the risk haplotype before and during a 24-wk LSI. We also emphasize the potential regulatory role of polyunsaturated fatty acids on methylmalonylcarnitine concentrations suggesting a route for improving interventions for individuals with high-genetic risk.

Aylin Bayındır-Gümüş, Ebru Öztürk, Mihály Soós

Background. People live in a technological world, where social media is used very commonly. Social media has effects on eating behaviors, as in other aspects. For this reason, it is important to measure social media effect. Objective. This study aimed to adapt the Scale of Effects of Social Media on Eating Behaviour (SESMEB) that examines the effect of social media on eating behavior in Hungarian university students. Material and methods. The SESMEB was translated into the target language by taking various stages. The online questionnaire including general information, social media use, and the eighteen-item SESMEB was used to collect data. The scale was administered to the study group consisting of 213 Hungarian university students, and data from 203 of them were analyzed. Confirmatory factor analyses were performed to test construct validity, and the Cronbach alpha coefficient was calculated for the reliability of the scale in Hungarian. Results. Total correlation value was higher than 0.50 for all items of the scale. The fit indices were at an acceptable level or had a perfect fit. The t-values were significant at the level of 0.1 and ranged between 2.927 and 5.706. The Spearman–Brown coefficient was calculated at 0.894. The reliability coefficient of the scale was calculated to be 0.866. SESMEB scores were different according to spending time daily, sharing content, and using filters or Photoshop on social media (p < 0.05). Conclusions. Higher than 0.80 Cronbach’s alpha coefficient and other results show that Hungarian SESMEB is a valid and reliable tool. Therefore, Hungarian SESMEB will be useful for further studies to determine the impact of social media on eating behaviors.

Tiantian Zhao, Guowan Su, Lijun Zhang et al.

Abstract Food‐derived peptides have garnered significant attention in research due to their multifaceted functionalities, abundant availability, efficient utilization of agricultural by‐products, and environmentally sustainable preparation methods. These peptides play a crucial role in human health, yet their precise mechanisms of action remain largely unexplored, posing challenges in their screening, preparation, and effective application utilizing protein‐based raw materials. This review offers an extensive examination of 19 types of bioactive peptides derived from food. The sources of food‐derived bioactive peptides are well concluded and the classifications are made according to their potential health benefit based on five primary systems: general bodily functions, the nervous system, the cardiovascular system, the metabolic system, and the immune system. This review specifically highlights the multifaceted impacts of tasty peptides on human health, extending beyond their gustatory effects. Furthermore, it explores the interplay between various functions of bioactive peptides, noting a progression from basic to advanced functionalities. Antioxidant activity and the modulation of key enzymes are identified as fundamental actions that are interconnected with other functional properties. This implies that a single bioactive peptide could exhibit multiple beneficial effects. The key role of antioxidant capabilities is underscored based on their broad influence and straightforward assessment. This comprehensive analysis aims to deepen the systematic understanding of the diverse benefits offered by various food‐derived peptides.

Jinjin Huang, M. Zhang, A. Mujumdar et al.

Abstract Fresh food is rich in nutrients but is usually seasonal, perishable, and challenging to store without degradation of quality. The inherent limitations of various preservation technologies can result in losses in all stages of the supply chain. As consumers of fresh foods have become more health-conscious, new technologies for intelligent, energy-efficient, and nondestructive preservation and processing have emerged as a research priority in recent years. This review aims to summarize the quality change characteristics of postharvest fruits, vegetables, meats, and aquatic products. It critically analyzes research progress and applications of various emerging technologies, which include: the application of high-voltage electric field, magnetic field, electromagnetic field, plasma, electrolytic water, nanotechnology, modified atmosphere packaging, and composite bio-coated film preservation technologies. An evaluation is presented of the benefits and drawbacks of these technologies, as well as future development trends. Moreover, this review provides guidance for design of the food supply chain to take advantage of various technologies used to process food, reduce losses and waste of fresh food, and this improve the overall resilience of the supply chain.