Modern nutrition and toxicology primarily evaluate food through chemical composition, contaminant burden, and caloric density. Yet this reductionist lens may not fully capture the systemic dysregulation often observed with industrially processed diets. In this Perspective, we propose the Food Coherence System (FCS), a hypothesis-generating framework that reframes food not merely as chemical “fuel”, but as an instructional signal that can support or degrade the body’s regulatory coordination. The FCS posits that dietary exposures may modulate regulation through coupled biophysical mechanisms that influence bioelectric signaling and control, including membrane potentials, ion-channel behavior, mitochondrial membrane potential (Δψm), redox dynamics, inflammatory tone, and neuroendocrine feedback loops. Within this model, Food Coherence is defined as the degree to which a food supports stable, low-noise, functionally adaptive signaling across tissues, consistent with preserved pattern stability and regenerative capacity. We also introduce bioelectric mutagen as a provisional, testable construct describing exposure patterns predicted to increase signaling noise, destabilize setpoints, compress adaptive variability, and impair recovery dynamics, relative to matched comparators. This framework is explicitly falsifiable and is not presented as a completed causal proof. We outline measurable signatures, including variance, drift, and recovery kinetics, together with example preregistered protocols that could refine, bound, or refute their core claims. This Perspective is intended to complement existing nutrition and toxicology models by adding bioelectric coherence as a candidate dimension of health, resilience, and recovery.
Assessing the performance level of human settlement improvement in traditional villages is significant in promoting the protection of traditional villages, but there is a lack of performance research on human settlement improvement from the perspective of corporate governance in previous studies. This paper selected 16 traditional villages as case villages and obtained a total of 345 questionnaires. By reference to the Balanced Scorecard (BSC) theory, a performance evaluation index system for human settlement improvement is constructed in this paper. In addition, the level of performance exhibited by traditional villages is evaluated and analyzed via the entropy weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and the obstacle degree analysis method. This study reveals the following findings: (1) The performance level of traditional villages in Jiaozuo city ranges between 0.28 and 0.64, with an average value of 0.49, thus indicating a medium level. (2) With respect to the subdimensions of human settlement improvement performance, the policy management dimension (0.88) exhibits the highest value, followed by the villagers dimension (0.48) and the learning and growth dimension (0.27), while the financial benefits dimension (0.10) exhibits the lowest value. (3) The obstacles affecting the performance level of human settlement improvement in different types of traditional villages are characterized by both similarities and differences. This study summarized the effects of traditional village human settlement improvement, and provided more scientific and reliable governance suggestions for future traditional village human settlement improvement, so as to better promote the protection of traditional villages and the sustainable development of the human settlement environment.
Nutrition. Foods and food supply, Food processing and manufacture
Jennifer J Lee, Christine Mulligan, Hayun Jeong
et al.
Canada mandated front-of-pack labelling (FOPL) regulations, requiring pre-packaged foods meeting and/or exceeding thresholds for nutrients-of-concern (saturated fat, sugars, sodium) to display a 'High in' nutrition symbol. Although FOPL regulations align with one of the recommendations of Canada's food guide (CFG), there is limited evidence on how well the regulations could support healthy eating among Canadians. The objective of this study was to evaluate the Canadian pre-packaged food supply according to FOPL regulations and to assess the extent to which the regulations could support healthy eating among Canadians using the Canadian Food Scoring System (CFSS), a nutrient profile model based on the recommendations of CFG. Using a branded food composition database (n = 17,008), pre-packaged foods were categorized according to FOPL regulations and the CFSS. According to FOPL regulations, approximately 54% of pre-packaged foods would display a 'High in' nutrition symbol for meeting and/or exceeding thresholds for at least one nutrient-of-concern. According to the CFSS, approximately 53% of foods were a 'poor' or 'very poor' choice, while 25% were a 'good' or 'excellent' choice. Foods that would not display a 'High in' nutrition symbol showed significant variation in their healthfulness, with 45% containing low amounts of nutritious foods recommended by CFG. Our findings highlight that many pre-packaged foods in Canada do not represent healthy choices. Although many of these foods will be highlighted with a 'High in' nutrition symbol when FOPL regulations are implemented, many foods that would not display a 'High in' nutrition symbol do not align well with the recommendations of CFG, particularly those with a variety of multiple ingredients (e.g., many breads, breakfast cereals, combination dishes). Additional tools and strategies are required to support Canadians make healthy food choices.
Eveline Sarintohe, William J. Burk, Jacqueline M. Vink
et al.
Introduction: Little is known about how the COVID-19 situation affected weight development among Indonesian adolescents. This longitudinal study examined whether, and for whom, the COVID-19 situation affected weight outcomes over time among adolescents from private schools and higher socio-economic positions in Indonesia, where being overweight is a rather prevalent characteristic. This study specifically examined whether appetitive traits (i.e., emotional overeating, food responsiveness) as well as baseline zBMI, sex, and urban area could explain changes in zBMI. Methods: At baseline, 411 adolescents from 5 private schools in Indonesia (53.3% males, Mage = 12.02 years, SD = 0.45) filled out questionnaires on appetitive traits and background characteristics. In addition, their height and weight were measured. Of these, 336 adolescents (81.8%) also participated at follow-up. At follow-up, height and weight were measured or reported. We used linear regression to analyze the association between predictors and interactions with zBMI. Results: The results showed a significant decrease in zBMI over time, with a lower average zBMI during COVID-19 compared to before COVID-19. Female adolescents and adolescents with higher baseline zBMI values particularly tended to show this zBMI decreasing pattern. We did not find statistically significant main effects of baseline emotional overeating, food responsiveness, and urban area or any interactions. Conclusions: Indonesian adolescents appeared to decrease in terms of zBMI during COVID-19, particularly females and adolescents with higher pre-COVID-19 zBMI. Our findings suggest that (culturally-specific) contextual changes (i.e., less exposure to the Indonesian food environment at schools and more exposure to the home environment) might have a beneficial impact in terms of preventing overweight among Indonesian adolescents, particularly among those being more vulnerable (i.e., having higher baseline zBMI).
Nutrition. Foods and food supply, Nutritional diseases. Deficiency diseases
Agricultural products are often subject to seasonal fluctuations in production and demand. Predicting and managing inventory levels in response to these variations can be challenging, leading to either excess inventory or stockouts. Additionally, the coordination among stakeholders at various level of food supply chain is not considered in the existing body of literature. To bridge these research gaps, this study focuses on inventory management of agri-food products under demand and lead time uncertainties. By implementing effective inventory replenishment policy results in maximize the overall profit throughout the supply chain. However, the complexity of the problem increases due to these uncertainties and shelf-life of the product, that makes challenging to implement traditional approaches to generate optimal set of solutions. Thus, the current study propose a novel Deep Reinforcement Learning (DRL) algorithm that combines the benefits of both value- and policy-based DRL approaches for inventory optimization under uncertainties. The proposed algorithm can incentivize collaboration among stakeholders by aligning their interests and objectives through shared optimization goal of maximizing profitability along the agri-food supply chain while considering perishability, and uncertainty simultaneously. By selecting optimal order quantities with continuous action space, the proposed algorithm effectively addresses the inventory optimization challenges. To rigorously evaluate this algorithm, the empirical data from fresh agricultural products supply chain inventory is considered. Experimental results corroborate the improved performance of the proposed inventory replenishment policy under stochastic demand patterns and lead time scenarios. The research findings hold managerial implications for policymakers to manage the inventory of agricultural products more effectively under uncertainty.
Food is a key pleasure of traveling, but travelers face a trade-off between exploring curious new local food and choosing comfortable, familiar options. This creates demand for personalized recommendation systems that balance these competing factors. To the best of our knowledge, conventional recommendation methods cannot provide recommendations that offer both curiosity and comfort for food unknown to the user at a travel destination. In this study, we propose new quantitative methods for estimating comfort and curiosity: Kernel Density Scoring (KDS) and Mahalanobis Distance Scoring (MDS). KDS probabilistically estimates food history distribution using kernel density estimation, while MDS uses Mahalanobis distances between foods. These methods score food based on how their representation vectors fit the estimated distributions. We also propose a ranking method measuring the balance between comfort and curiosity based on taste and ingredients. This balance is defined as curiosity (return) gained per unit of comfort (risk) in choosing a food. For evaluation the proposed method, we newly collected a dataset containing user surveys on Japanese food and assessments of foreign food regarding comfort and curiosity. Comparing our methods against the existing method, the Wilcoxon signed-rank test showed that when estimating comfort from taste and curiosity from ingredients, the MDS-based method outperformed the Baseline, while the KDS-based method showed no significant differences. When estimating curiosity from taste and comfort from ingredients, both methods outperformed the Baseline. The MDS-based method consistently outperformed KDS in ROC-AUC values.
Leah Costlow, Rachel Gilbert, William A. Masters
et al.
New methods for modeling least-cost diets that meet nutritional requirements for health have emerged as important tools for informing nutrition policy and programming around the world. This study develops a three-step approach using cost of healthy diet to inform targeted nutrition programming in Indonesia. We combine detailed retail prices and household survey data from Indonesia to describe how reported consumption and expenditure patterns across all levels of household income diverge from least cost healthy diets using items from nearby markets. In this analysis, we examine regional price variations, identify households with insufficient income for healthy diets, and analyze the nutrient adequacy of reported consumption patterns. We find that household food spending was sufficient to meet national dietary guidelines using the least expensive locally available items for over 98% of Indonesians, but almost all households consume substantial quantities of discretionary foods and mixed dishes while consuming too little energy from fruits, vegetables, and legumes, nuts, and seeds. Households with higher incomes have higher nutrient adequacy and are closer to meeting local dietary guidelines, but still fall short of recommendations. These findings shed new light on how actual food demand differs from least-cost healthy diets, due to factors other than affordability, such as taste, convenience, and aspirations shaped by marketing and other sociocultural influences.
Accurately tracking food consumption is crucial for nutrition and health monitoring. Traditional approaches typically require specific camera angles, non-occluded images, or rely on gesture recognition to estimate intake, making assumptions about bite size rather than directly measuring food volume. We propose the FoodTrack framework for tracking and measuring the volume of hand-held food items using egocentric video which is robust to hand occlusions and flexible with varying camera and object poses. FoodTrack estimates food volume directly, without relying on intake gestures or fixed assumptions about bite size, offering a more accurate and adaptable solution for tracking food consumption. We achieve absolute percentage loss of approximately 7.01% on a handheld food object, improving upon a previous approach that achieved a 16.40% mean absolute percentage error in its best case, under less flexible conditions.
Maria Vittoria Conti, Sara Santero, Chiara Breda
et al.
ObjectiveIndividuals with Autism Spectrum Disorder (ASD) often exhibit a low dietary diversity due to food selectivity that leads them to a marked preference for high-energy-density food, exposing them to risk of malnutrition. Despite these aspects, specific recommendations and targeted menus for this population are missing. The pilot study FOOD-AUT addresses this issue by developing canteen menus meeting the nutritional and sensory needs of adults with ASD, aiming to reduce their food selectivity, and consequently improving their health.MethodsThe project, funded by Gruppo Pellegrini S.p.A, was conducted at the daycare service of Sacra Famiglia Onlus Foundation, between March-2022 to March-2023. The study was divided into two phases. Observational phase: a comparison was made between the enrolled subjects’ nutritional needs and the nutrient content of the administered menus during the daycare service. Then mealtime compliance was assessed using standardized meal evaluation forms, both quantitative and qualitative. Intervention phase: canteen menus targeted to the individuals’ nutritional and sensory needs were administered and their acceptability was evaluated.ResultsTwenty-two individuals with ASD, aged 19–48, 72.7% males, were enrolled. Overweight and obesity prevalence were 54.5 and 18.2%, respectively. The observational phase showed how the most accepted foods had specific sensorial characteristics in line with the scientific literature. Adapting the menus improved food acceptance and reduced food waste.ConclusionThe results highlighted the need for adapted menus and greater attention to the way meals are delivered and consumed to improve nutritional status and therefore health of this population at increased risk of malnutrition.Clinical trial registrationClinicalTrial.gov, unique identifier: NCT05978895.
Background: Metabolic syndrome is a significant risk factor for both type 2 diabetes mellitus and cardiovascular disease, with a high prevalence in Asia Pacific, particularly in Indonesia. To reduce its prevalence, several studies have recommended the use of tropical nuts, which can be developed as functional foods and complementary treatment. In this context, the bioactivities of tropical nuts can largely be attributed to their rich content of monounsaturated fatty acids, polyunsaturated fatty acids, fiber, minerals, vitamins, phytosterols, and polyphenols.
Objectives: This literature review aims to evaluate the potential benefits and mechanism of action of tropical nuts against metabolic syndrome.
Methods: The study design was a literature review of several articles from 3 online databases, including PubMed, Google Scholar, and ScienceDirect.
Discussions: The results showed that tropical nuts (peanut, sacha inchi, cashew, tropical almond, and Brazil nut) had several biologically active components, such as arginine, fiber, fatty acid, mineral, vitamin, phenolic compounds, resveratrol, and phytosterol. The test samples were reported to have the ability to modulate Nrf2, SOD, MDA, GSH, GPx, and CAT due to their antioxidant activity. In inflammation, tropical nuts had a significant effect on NF-κB, NLRP3, TNF-ɑ, IL-8, IL-1ꞵ, IL-6, and IL-10. The results also showed their ability to enhance lipid synthesis, nitric oxide production, advanced glycation end-product, prostaglandin, SIRT3, homocysteine, protein kinase C, adhesion molecules, platelet aggregation, GLP-1, PYY, AGRP, PPARɑ/ꞵ/δ, GLUT4, and insulin receptor.
Conclusions: Tropical nuts had beneficial effects on metabolic syndrome due to their bioactivities, including antioxidants, anti-inflammatory, anti-obesity, antidiabetic, antihypertensive, anti-dyslipidemia, and cardioprotective.
Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato
et al.
The advancement of artificial intelligence (AI) in food and nutrition research is hindered by a critical bottleneck: the lack of annotated food data. Despite the rise of highly efficient AI models designed for tasks such as food segmentation and classification, their practical application might necessitate proficiency in AI and machine learning principles, which can act as a challenge for non-AI experts in the field of nutritional sciences. Alternatively, it highlights the need to translate AI models into user-friendly tools that are accessible to all. To address this, we present a demo of a semi-automatic food image annotation tool leveraging the Segment Anything Model (SAM). The tool enables prompt-based food segmentation via user interactions, promoting user engagement and allowing them to further categorise food items within meal images and specify weight/volume if necessary. Additionally, we release a fine-tuned version of SAM's mask decoder, dubbed MealSAM, with the ViT-B backbone tailored specifically for food image segmentation. Our objective is not only to contribute to the field by encouraging participation, collaboration, and the gathering of more annotated food data but also to make AI technology available for a broader audience by translating AI into practical tools.
Robotic Assisted Feeding (RAF) addresses the fundamental need for individuals with mobility impairments to regain autonomy in feeding themselves. The goal of RAF is to use a robot arm to acquire and transfer food to individuals from the table. Existing RAF methods primarily focus on solid foods, leaving a gap in manipulation strategies for semi-solid and deformable foods. This study introduces Long-horizon Visual Action (LAVA) based food acquisition of liquid, semisolid, and deformable foods. Long-horizon refers to the goal of "clearing the bowl" by sequentially acquiring the food from the bowl. LAVA employs a hierarchical policy for long-horizon food acquisition tasks. The framework uses high-level policy to determine primitives by leveraging ScoopNet. At the mid-level, LAVA finds parameters for primitives using vision. To carry out sequential plans in the real world, LAVA delegates action execution which is driven by Low-level policy that uses parameters received from mid-level policy and behavior cloning ensuring precise trajectory execution. We validate our approach on complex real-world acquisition trials involving granular, liquid, semisolid, and deformable food types along with fruit chunks and soup acquisition. Across 46 bowls, LAVA acquires much more efficiently than baselines with a success rate of 89 +/- 4% and generalizes across realistic plate variations such as different positions, varieties, and amount of food in the bowl. Code, datasets, videos, and supplementary materials can be found on our website.
Ahmad AlMughrabi, Umair Haroon, Ricardo Marques
et al.
Accurate food volume estimation is essential for dietary assessment, nutritional tracking, and portion control applications. We present VolETA, a sophisticated methodology for estimating food volume using 3D generative techniques. Our approach creates a scaled 3D mesh of food objects using one- or few-RGBD images. We start by selecting keyframes based on the RGB images and then segmenting the reference object in the RGB images using XMem++. Simultaneously, camera positions are estimated and refined using the PixSfM technique. The segmented food images, reference objects, and camera poses are combined to form a data model suitable for NeuS2. Independent mesh reconstructions for reference and food objects are carried out, with scaling factors determined using MeshLab based on the reference object. Moreover, depth information is used to fine-tune the scaling factors by estimating the potential volume range. The fine-tuned scaling factors are then applied to the cleaned food meshes for accurate volume measurements. Similarly, we enter a segmented RGB image to the One-2-3-45 model for one-shot food volume estimation, resulting in a mesh. We then leverage the obtained scaling factors to the cleaned food mesh for accurate volume measurements. Our experiments show that our method effectively addresses occlusions, varying lighting conditions, and complex food geometries, achieving robust and accurate volume estimations with 10.97% MAPE using the MTF dataset. This innovative approach enhances the precision of volume assessments and significantly contributes to computational nutrition and dietary monitoring advancements.
Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das
et al.
This paper presents a novel approach to compute food composition data for Indian recipes using a knowledge graph for Indian food (FKG[.]in) and LLMs. The primary focus is to provide a broad overview of an automated food composition analysis workflow and describe its core functionalities: nutrition data aggregation, food composition analysis, and LLM-augmented information resolution. This workflow aims to complement FKG[.]in and iteratively supplement food composition data from verified knowledge bases. Additionally, this paper highlights the challenges of representing Indian food and accessing food composition data digitally. It also reviews three key sources of food composition data: the Indian Food Composition Tables, the Indian Nutrient Databank, and the Nutritionix API. Furthermore, it briefly outlines how users can interact with the workflow to obtain diet-based health recommendations and detailed food composition information for numerous recipes. We then explore the complex challenges of analyzing Indian recipe information across dimensions such as structure, multilingualism, and uncertainty as well as present our ongoing work on LLM-based solutions to address these issues. The methods proposed in this workshop paper for AI-driven knowledge curation and information resolution are application-agnostic, generalizable, and replicable for any domain.
Abubakar Mohammed, Vidyasagar Potdar, Mohammed Quaddus
Blockchain technology (BCT) has been proven to have the potential to transform food supply chains (FSCs) based on its potential benefits. BCT promises to improve food supply chain processes. Despite its several benefits, little is known about the factors that drive blockchain adoption within the food supply chain and the impact of blockchain technology on the food supply chain, as empirical evidence is scarce. This study, therefore, explores factors, impacts and challenges of blockchain adoption in the FSC. The study adopts an exploratory qualitative interview approach. The data consist of Twenty-one interviews which were analyzed using thematic analysis techniques in NVivo (v12), resulting in identifying nine factors classified under three broad categories (Technology—complexity, compatibility, cost; Organization—organization size, knowledge; Environment—government support, competitive pressure, standardization, and compliance) as the most significant factors driving blockchain adoption in the FSC. In addition, five impacts were identified (visibility, performance, efficiency, trust, and value creation) to blockchain technology adoption. This study also identifies significant challenges of blockchain technology (interoperability, privacy, infrastructure conditions, and lack of knowledge). Based on the findings, the study developed a conceptual framework for blockchain adoption in food supply chains. The study adds to the corpus of knowledge by illuminating the adoption of blockchain technology and its effects on food supply chains and by giving the industry evidence-based guidance for developing its blockchain plans. The study provides full insights and awareness of blockchain adoption challenges among executives, supply chain organizations, and governmental agencies.
Cátia V. Almeida Santos, Catarina Pereira, Nuno Martins
et al.
SO<sub>2</sub> is a preservative often used in the food industry, particularly in the wine industry. However, regulatory authorities and consumers have been strongly suggesting its reduction or even its replacement. In order to understand the impact of SO<sub>2</sub> on the profiles of volatile organic compounds (VOCs) as well as amino acids (AAs), the aging of two white wines (one being a varietal and another being a blend) under identical conditions and in the presence of different doses of total SO<sub>2</sub> was studied. After alcoholic fermentation (t = 0), either no SO<sub>2</sub> was added (0 mg/L), or 30, 60, 90, or 120 mg/L of SO<sub>2</sub> was applied. The samples under study were kept on fine lees for 3 months (t = 3). After 3 months (t = 6) and 9 months (t = 12), the wines were bottled and analyzed. For t = 0 and t = 3, the samples were submitted to HS-SPME-GC/MS for VOC analysis and LC-DAD for AA analysis. From the principal component analysis of the detected VOCs, it was observed that the blended wine in comparison with the varietal wine, was less impacted by the applied SO<sub>2</sub> doses and aging time. From the AA profile, it was also observed in this study that maturation on fine lees resulted in an increase in the total concentration of AAs as would be expected.
Nutrition. Foods and food supply, Nutritional diseases. Deficiency diseases
IntroductionAs low FODMAP (Fermentable oligosaccharides, disaccharides, monosaccharides and polyols) diet therapy is recommended for most of Irritable Bowel Syndrome (IBS) patients, the consequent insufficient of dietary fibers (DFs) intake exert an adverse impact on intestinal health. It is necessary to find suitable DFs for IBS patients.MethodsThis study extracted a water-insoluble polysaccharide from Wolfiporia cocos (WIP) by alkali-extraction and acid-precipitation method. Its molecular weight was detected by high performance gel permeation chromatography (HPGPC) analysis. The structure of WIP was analyzed by Fourier transform infrared (FT-IR) spectrum, Nuclear Magnetic Resonance (NMR) spectra and X-ray diffraction (XRD). The properties related to stability, digestion, viscosity, osmotic activity, adsorption and fermentation were investigated, aimed to explore the feasibility of WIP as a new DF supplement for patients with IBS. In addition, 16S rRNA sequencing analysis was conducted to explore its effects on IBS-related gut microbiota.Results and DiscussionThe results showed that WIP had a single homogeneous composition and the molecular weight was 8.1 × 103 Da. WIP was indicated as a kind of pyranose form with β anomeric configuration and the main chain of WIP was 1,3-β-glucan with amorphous structure. In addition to good thermal stability, WIP also has low bioavailability and can reach the colon mostly without being digested. Moreover, the low viscosity and osmotic activity, the high water- swelling and water/oil-holding capacity, fructose adsorption capacity and poor fermentation performance of WIP demonstrated that it is suitable for IBS patients. It is worth noting that WIP regulates IBS associated gut microbiota effectively, such as the abundance of Lachnospiraceae and Prevotella. These findings provide a theoretical basis for the development of WIP as a dietary supplement for IBS patients with low FODMAP diet therapy.GRAPHICAL ABSTRACT
Paola Caceres-Rodríguez, Christopher Chavarria-Tapia, Karen Basfi-fer-Obregón
et al.
Introducción: En Chile, la situación laboral del nutricionista se ha visto afectada por la numerosa competencia y limitados cupos en las áreas laborales tradicionales. En consecuencia, la carrera de Nutrición y Dietética de la Universidad de Chile decide abrir una nueva práctica profesional en áreas de desempeño emergentes a partir del año 2021. El objetivo de este estudio fue evaluar su implementación y valoración, considerando la opinión de todos los involucrados.
Metodología: Estudio descriptivo transversal mixto con muestreo no probabilístico. Para recoger la opinión de los/as participantes se diseñaron y realizaron encuestas online a alumnos/as del último nivel y a sus supervisores, entrevistas semiestructuradas a tutores de centros de prácticas y grupos de discusión a profesores de grado. Se utilizó estadística descriptiva para los resultados cuantitativos y análisis temático para las cuestiones cualitativas.
Resultados: Se obtuvo la opinión de 27 estudiantes, 8 supervisores, 10 tutores y 13 profesores de pregrado. En cuanto a la implementación, tanto la estructura como los centros y su modalidad de práctica fueron adecuados. La valoración fue alta por parte de los implicados, destacando el desarrollo y gestión de competencias genéricas entre los/as alumnos/as, y su contribución a ampliar el campo laboral, así como el papel del nutricionista en áreas emergentes. Como aspecto a mejorar, es necesario detallar más las actividades a realizar en cada centro.
Conclusiones: La implementación de esta práctica fue exitosa, con un alto nivel de valoración. Su inclusión en el plan de estudios responde adecuadamente al perfil del egresado y a los objetivos de desarrollo de la unidad, respondiendo a necesidades previamente detectadas en el plan de estudios y en el entorno, contribuyendo así a la inserción laboral de los/as egresados/as y a la diversificación del campo actual de la disciplina.
Nutrition. Foods and food supply, Nutritional diseases. Deficiency diseases