Journal articles: 'Personalized food'

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Park, Seon Kyeong, and Jae-Ho Park. "Personalized food based on genomics." Food Science and Industry 56, no. 4 (2023): 290–300. http://dx.doi.org/10.23093/fsi.2023.56.4.290.

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Zhao, Haiming, Jufeng Wang, Xiaoyu Ren, Jingyuan Li, Yong-Liang Yang, and Xiaogang Jin. "Personalized food printing for portrait images." Computers & Graphics 70 (February 2018): 188–97. http://dx.doi.org/10.1016/j.cag.2017.07.012.

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Horiguchi, Shota, Sosuke Amano, Makoto Ogawa, and Kiyoharu Aizawa. "Personalized Classifier for Food Image Recognition." IEEE Transactions on Multimedia 20, no. 10 (2018): 2836–48. http://dx.doi.org/10.1109/tmm.2018.2814339.

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Thongsri, Nattaporn, Pattaraporn Warintarawej, Santi Chotkaew, and Wanida Saetang. "Implementation of a personalized food recommendation system based on collaborative filtering and knapsack method." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 630. http://dx.doi.org/10.11591/ijece.v12i1.pp630-638.

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Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction.

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BETHKE, Malte, Lavinia MURESAN, and Monica TRIF. "OFTIFEL PERSONALIZED NUTRITIONAL CALCULATOR." Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Food Science and Technology 73, no. 2 (2016): 151. http://dx.doi.org/10.15835/buasvmcn-fst:12270.

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A food calculator for elderly people was elaborated by Centiv GmbH, an active partner in the European FP7 OPTIFEL Project, based on the functional requirement specifications and the existing recommendations for daily allowances across Europe, data which were synthetized and used to give aims in amounts per portion. The OPTIFEL Personalised Nutritional Calculator is the only available online tool which allows to determine on a personalised level the required nutrients for elderly people (65+). It has been developed mainly to support nursing homes providing best possible (personalised) nutrient enriched food to their patients. The European FP7 OPTIFEL project “Optimised Food Products for Elderly Populations” aims to develop innovative products based on vegetables and fruits for elderly populations to increase length of independence. The OPTIFEL Personalised Nutritional Calculator is recommended to be used by nursing homes.

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Zeisel, Steven H. "Precision (Personalized) Nutrition: Understanding Metabolic Heterogeneity." Annual Review of Food Science and Technology 11, no. 1 (2020): 71–92. http://dx.doi.org/10.1146/annurev-food-032519-051736.

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People differ in their requirements for and responses to nutrients and bioactive molecules in the diet. Many inputs contribute to metabolic heterogeneity (including variations in genetics, epigenetics, microbiome, lifestyle, diet intake, and environmental exposure). Precision nutrition is not about developing unique prescriptions for individual people but rather about stratifying people into different subgroups of the population on the basis of biomarkers of the above-listed sources of metabolic variation and then using this stratification to better estimate the different subgroups’ dietary requirements, thereby enabling better dietary recommendations and interventions. The hope is that we will be able to subcategorize people into ever-smaller groups that can be targeted in terms of recommendations, but we will never achieve this at the individual level, thus, the choice of precision nutrition rather than personalized nutrition to designate this new field. This review focuses mainly on genetically related sources of metabolic heterogeneity and identifies challenges that need to be overcome to achieve a full understanding of the complex interactions between the many sources of metabolic heterogeneity that make people differ from one another in their requirements for and responses to foods. It also discusses the commercial applications of precision nutrition.

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Karabay, Aknur, Arman Bolatov, Huseyin Atakan Varol, and Mei-Yen Chan. "A Central Asian Food Dataset for Personalized Dietary Interventions." Nutrients 15, no. 7 (2023): 1728. http://dx.doi.org/10.3390/nu15071728.

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Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.

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Nikitina, Marina, and Irina Chernukha. "Personalized nutrition and “digital twins” of food." Potravinarstvo Slovak Journal of Food Sciences 14 (May 28, 2020): 264–70. http://dx.doi.org/10.5219/1312.

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Mathematization of research is one of the most effective methods of virtual substantiation of foodstuff recipe and technology. This approach allows creating a product that meets consumer's individual needs, i.e. personalized foodstuff (ethnicity, cultural preferences, regional and environmental characteristics, lifestyle), and at the same time reducing the time and cost of decision-making. The article discusses the hypothesis that the “digital twin” of a food product is a virtual model of the product, namely its mathematical model (simulation model). A simulation model is a logical and mathematical description of a food product that is used to conduct a computerized experiment in order to design desired characteristics and properties. The “digital twin” combines all variety of factors from chemical composition, functional and technological properties to organoleptic indicators. The application of the “digital twin” model of the foodstuff will allow: (1) reacting quickly to changes in the composition, properties and types of raw ingredients, (2) adjusting the product recipe in response to changes in consumer preferences, (3) designing products with a given chemical composition, nutritional value and functional orientation, (4) creating functional, specialized products taking into account the metabolism of nutrients (ethnicity, cultural preferences, health status and clinical factors). Products adapted to the needs of small categories of people will help reducing the risks for those who already have diseases, and will meet the needs of those who would like to make their diet more appropriate to individual needs. The proposed approach to creating a model of the “digital twin” of the foodstuff includes several stages. The first stage involves optimization of the nutritional and biological value of the designed product. The second stage is related to designing the food product’s structural forms. But even if the recipe of a food product is optimally selected in the first stage, it does not guarantee its transformation during processing into a stable system with the required structural, mechanical, functional and technological parameters. Evaluation of the developed food product’s efficiency is possible only by analysing numerous and various parameters and indicators. It is convenient to generalize (convolute) many parameters and indicators into a single quantitative dimensionless indicator. To assess the quality and adequacy of the food product, it is suggested to use an integral indicator in the form of additive convolution – the ‘functional’ of the food product quality.

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Jeon, Yoomin, Jung-Hyun Won, David Seung U. Lee, et al. "Development of a Questionnaire for the Personalized Health Functional Food." Korean Journal of Health Promotion 22, no. 1 (2022): 26–39. http://dx.doi.org/10.15384/kjhp.2022.22.1.26.

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Background: This study describes the development of a multi-dimensional questionnaire for the personalized recommendation of health functional foods.Methods: The questionnaire was designed to include two parts: common and detailed. Each question was formulated based on clinical evidence and physician-administered diagnostic tools provided by reputable sources such as government agencies and medical institutions. The common questions were intended to assess overall health status. The detailed questions were organized into 28 categories based on the classification of health functional foods’ health claims by the Ministry of Food and Drug Safety. Each question’s response was evaluated on a scale from 0 to 1. The final score of each category was calculated as the sum of the scores normalized by the number of questions in each category. Two expert physicians in health functional food assessed the content validity of the questionnaire, and the questionnaire was revised accordingly.Results: A total of 31 common questions evaluated overall health conditions based on demographic information, medical history, allergies, prescription medicines, dietary habits, and lifestyle. A question that surveys interest areas was also included to let the responders choose up to three categories for the detailed question part. A total of 173 detailed questions assessed risk factors and symptoms relevant to each health claim.Conclusions: This questionnaire recommends health functional foods by measuring health risks and symptoms, following the classification of health functional foods by the Ministry of Food and Drug Safety. This questionnaire may serve as an evidence-based information collecting tool for a personalized health functional foods recommendation system.

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TRIF, Monica, Lavinia MURESAN, and Malte BETHKE. "Personalised nutritional powder for elderly developed in OPTIFEL European Project." Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Food Science and Technology 73, no. 2 (2016): 149. http://dx.doi.org/10.15835/buasvmcn-fst:12271.

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A personalized nutritional powder for elderly containing minerals and vitamins was developed by CENTIV GmbH for further applications in the FP7 European OFTIFEL project by designing and processing of novel food products such as nutrient dense smoothie products. The amount of the powder used for each person, in each product, is calculated using a food calculator, which was developed within the same FP7 European OFTIFEL project, named OPTIFEL Personalised Nutritional Calculator.The personalized nutritional powder consisting of several minerals and vitamins, has been agglomerated in a Fluid Bed Dryer using maltodextrin as carrier, in order to obtain a higher solubility, homogenization of all the nutrients, a desired supplement with a neutral taste, free of color and which can be added in any drinks or food matrix for enrichment in a certain concentration. It will not influence the final taste and has great solubility.Developing innovative food and services tailored to elderly populations is a new challenge for key players involved in food industry and catering. The population targeted by the OPTIFEL project is elderly cooking at home or making use of meal-on-wheels services for whom the project will develop food products adapted to their taste, habits, needs and constraints.

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Roosen, Jutta, Maike Bruhn, Rebecca-Ariane Mecking, and Larissa S. Drescher. "Consumer Demand for Personalized Nutrition and Functional Food." International Journal for Vitamin and Nutrition Research 78, no. 6 (2008): 269–74. http://dx.doi.org/10.1024/0300-9831.78.6.269.

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New developments in nutrigenetic research and the European regulation 1924/2006 on health claims have spurred interest in developing and marketing functional food designed for personalized nutrition. Personalized nutrition uses genetic information regarding a person’s health risk profile. Specifically adapted nutrition recommendations are claimed to help reducing disease risk. An internet survey was conducted in December 2007 using a sample of 452 randomly selected adults in Germany. The survey instrument assesses if consumers would be willing to participate in genetic risk profiling, if they are interested in personalized nutrition advice and if they desire functional food products adapted to their individual nutrigenetic profile. In addition, we estimate the acceptance of functional food products designed to reduce the risk of cardio-vascular diseases. Consumers have a positive attitude towards the testing of their genetic profile to be used in nutrient advice. About 45 % of the sample would agree to such a test and like to obtain a personalized advice on nutrition. Similarly, more than 40 % of the sample showed a positive willingness to buy the proposed functional food products. Given these results, the concept of personalized nutrition seems promising. However, several challenges remain regarding targeted nutrition advice and food marketing.

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Martirosyan, Danik, and Sarah Stratton. "Quantum and tempus theories of function food science in practice." Functional Food Science 3, no. 5 (2023): 55. http://dx.doi.org/10.31989/ffs.v3i5.1122.

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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.

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Livingstone, Katherine, Carlos Celis-Morales, Santiago Navas-Carretero, et al. "Personalized Nutrition Advice Reduces Intake of Discretionary Foods and Beverages: Findings From the Food4Me Randomized Controlled Trial." Current Developments in Nutrition 5, Supplement_2 (2021): 152. http://dx.doi.org/10.1093/cdn/nzab035_060.

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Abstract Objectives This study aimed to examine changes in intake of discretionary foods and beverages following a personalized nutrition intervention using two national classifications for discretionary foods. Methods Participants were recruited into a 6-month RCT across seven European countries (Food4Me) and were randomized to receive generalized dietary advice (Control) or one of three levels of personalized nutrition advice (based on dietary, phenotypic and genotypic information). Dietary intake from a FFQ was used to determine change between baseline and month 6 in (i) % energy, % contribution to total fat, SFA, total sugars and salt and (ii) contribution (%) made by sweets and snacks to intake of total fat, SFA, sugars and salt from discretionary foods and beverages, defined by Food Standards Scotland (FSS) and the Australian Dietary Guidelines (ADG). Results A total of 1270 adults (40.9 (SD 13.0) years; 57% female) completed the intervention. At month 6, percentage sugars from FSS discretionary items was lower in personalized nutrition vs control (19.0 ± 0.37 vs 21.1 ± 0.65; P = 0.005). Percentage energy (31.2 ± 0.59 vs 32.7 ± 0.59; P = 0.031), % total fat (31.5 ± 0.37 vs 33.3 ± 0.65; P = 0.021), SFA (36.0 ± 0.43 vs 37.8 ± 0.75; P = 0.034) and sugars (31.7 ± 0.44 vs 34.7 ± 0.78; P &lt; 0.001) from ADG discretionary items were lower in personalized nutrition vs control. The % contribution of sugars from sweets and snacks was lower in personalized nutrition vs control (19.1 ± 0.36 vs 21.5 ± 0.63; P &lt; 0.001). At 3 months, effects were consistent for ADG discretionary items, while there was no significant differences in personalized nutrition vs control for FSS discretionary items. Conclusions Compared with generalized dietary advice, personalized nutrition advice achieved greater reductions in intake of discretionary foods and beverages when the classification included all foods high in fat, added sugars and salt. Future personalized nutrition strategies may be used to target intake of discretionary foods and beverages. Funding Sources European Commission Food, Agriculture, Fisheries and Biotechnology Theme of the Seventh Framework Programme for Research and Technological Development [265494]. KML is supported by a NHMRC Emerging Leadership Fellowship (APP1173803).

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Ray, Subhasree. "Personalized Modification of Breast Milk to Help Enhancing Nutrition Profile of Neonates: A short Communication." Current Research in Nutrition and Food Science Journal 2, no. 1 (2014): 47–50. http://dx.doi.org/10.12944/crnfsj.2.1.07.

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Personalized Nutrition means in practice, adapting food to individual needs, depending on the host’s genome, this calls for an emerging field of nutrigenomic approach in order to build the tools for individualized diet, health maintenance and disease prevention. Based on this principle, breast milk is now being analyzed, modified and administered in smaller infants to provide them personalized diet, ensuring the premature infants are receiving correct amounts of nutrients they need to thrive. In the past, all milk was fortified to the same and it was ‘one-size-fits-all’. Now, nutrigenomics is moving towards having the ability to personalize each mother’s milk to give her baby precise nutrition he needs by stressing upon nutrition and interaction of three health relevant genomes in perspective, namely the food, the gut microbial and the human host genome in context of individualized nutrition and optimum health.

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Yao, Qisi, Haley Parker, Maya Vadiveloo, and Anne Thorndike. "Participant Characteristics Contributed to Variable Responsiveness to Personalized Healthy Food Incentives—A Secondary Analysis of a Randomized Crossover Trial." Current Developments in Nutrition 5, Supplement_2 (2021): 997. http://dx.doi.org/10.1093/cdn/nzab051_041.

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Abstract Objectives The recently completed Smart Cart Study, designed to promote healthier food purchases, found that personalized healthy food incentives modestly improved mean grocery purchase quality, with considerable variation in participant responsiveness. The present study explored participant characteristics associated with variable responsiveness to this healthy food incentives intervention. Methods A secondary analysis examined the Smart Cart Study, where participants received personalized weekly coupons during in the intervention and occasional generic coupons during the control period (n = 209). The outcome variable was 3-month changes in Grocery Purchase Quality Index (GPQI), a validated score calculated from purchasing data that compares the % spending relative to recommended spending to reflect the dietary quality of grocery purchases, in the intervention minus control periods. Potential exposure variables were selected a priori based on a literature review included age, sex, race/ethnicity, education, income, general health status, food literacy, food neophobia, nutritional self-efficacy and eating perception. Multivariable linear regressions evaluated characteristics that predict changes in GPQI. Stepwise model selection guided variable retention with specification of pr = 0.2. Analyses were conducted in Stata 16.1. Results Participants were predominately female (89.5%), Non-Hispanic white (94.1%), higher income (50.3% ≥$100K/year), higher education (48.4% &gt;bachelor's degree), with a mean age of 55.3. In the final model, changes in % spending on couponed foods (β = 15.58, P &lt; 0.001) and food neophobia (β = −1.77, P = 0.047) predicted changes in GPQI, while other predictors were not significant. The final model explained 13% of the variation in GPQI changes. Conclusions Greater % spending on couponed foods was associated with higher GPQI while food neophobia was associated with lower GPQI. Given that food neophobia may prevent participants from increasing their spending on new couponed healthy foods, future personalized couponing interventions should explore strategies to address food neophobia, potentially through free samples of new foods or providing coupons for familiar healthy foods, especially in diverse populations. Funding Sources The Smart Cart Study was funded by the Foundation for Food and Agricultural Research.

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Deschildre, A., S. Lejeune, M. Cap, et al. "Food allergy phenotypes: The key to personalized therapy." Clinical & Experimental Allergy 47, no. 9 (2017): 1125–37. http://dx.doi.org/10.1111/cea.12984.

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Kondo, Naomi, Manami Kuwabara, Hitomi Kodama, Masumi Kumada, and Nobuhiro Hori. "Medical treatment of food allergies should be personalized." Personalized Medicine Universe 4 (July 2015): 73–75. http://dx.doi.org/10.1016/j.pmu.2015.03.005.

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Frossard, Mikaël, Natalia Gasilova, Lionel Arlettaz, Eric Dayer, and Hubert H. Girault. "Personalized and rapid test for food-related allergy." Journal of Allergy and Clinical Immunology 141, no. 6 (2018): 2297–300. http://dx.doi.org/10.1016/j.jaci.2017.11.065.

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Mori, Francesca, Simona Barni, Giulia Liccioli, and Elio Novembre. "Oral Immunotherapy (OIT): A Personalized Medicine." Medicina 55, no. 10 (2019): 684. http://dx.doi.org/10.3390/medicina55100684.

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Oral Immunotherapy (OIT), a promising allergen-specific approach in the management of Food Allergies (FA), is based on the administration of increasing doses of the culprit food until reaching a maintenance dose. Each step should be adapted to the patient, and OIT should be considered an individualized treatment. Recent studies focused on the standardization and identification of novel biomarkers in order to correlate endotypes with phenotypes in the field of FA.

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YU, Qing, Masashi ANZAWA, Sosuke AMANO, and Kiyoharu AIZAWA. "Personalized Food Image Classifier Considering Time-Dependent and Item-Dependent Food Distribution." IEICE Transactions on Information and Systems E102.D, no. 11 (2019): 2120–26. http://dx.doi.org/10.1587/transinf.2019pcp0005.

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Yun, Hyun-Jung, Na-Ra Han, Hyun-Woo An, Won-Kyo Jung, Hyun-Woo Kim, and Sang-Gil Lee. "Development of an Abalone 3D Food Printing Ink for the Personalized Senior-Friendly Foods." Foods 11, no. 20 (2022): 3262. http://dx.doi.org/10.3390/foods11203262.

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Notably for seniors, 3D food printing is an appropriate processing method for creating customized meals that meet their unique nutritional requirements and textural preferences. This study attempted to develop an ink for food 3D printers containing abalone powder and several nutrition properties that meet the criteria for senior-friendly foods. The texture of the products was adjusted using gelatin. The ink consisted of abalone powder (10%), soybean protein (4.5%), polydextrose (2.5%), vitamin C (0.0098%), and gellan gum (1%). To examine the physicochemical properties of the ink, texture, water holding capacity, and rheological properties were measured. In addition, the suitability of the 3D printing was examined. As a result, 3% gelatin 3D food printing ink demonstrated optimal printability and could be converted into foods that could be consumed in one step (teeth intake), depending on the types of food for seniors.

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Xie, Yisha, Qingqing Liu, Wenwen Zhang, et al. "Advances in the Potential Application of 3D Food Printing to Enhance Elderly Nutritional Dietary Intake." Foods 12, no. 9 (2023): 1842. http://dx.doi.org/10.3390/foods12091842.

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The contradiction between the growing demand from consumers for “nutrition & personalized” food and traditional industrialized food production has consistently been a problem in the elderly diet that researchers face and discuss. Three-dimensional (3D) food printing could potentially offer a solution to this problem. This article reviews the recent research on 3D food printing, mainly including the use of different sources of protein to improve the performance of food ink printing, high internal phase emulsion or oleogels as a fat replacement and nutrition delivery system, and functional active ingredients and the nutrition delivery system. In our opinion, 3D food printing is crucial for improving the appetite and dietary intake of the elderly. The critical obstacles of 3D-printed food for the elderly regarding energy supplements, nutrition balance, and even the customization of the recipe in a meal are discussed in this paper. By combining big data and artificial intelligence technology with 3D food printing, comprehensive, personalized, and customized geriatric foods, according to the individual traits of each elderly consumer, will be realized via food raw materials-appearance-processing methods. This article provides a theoretical basis and development direction for future 3D food printing for the elderly.

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Héritier, Harris, Chloé Allémann, Oleksandr Balakiriev, et al. "Food & You: A digital cohort on personalized nutrition." PLOS Digital Health 2, no. 11 (2023): e0000389. http://dx.doi.org/10.1371/journal.pdig.0000389.

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Nutrition is a key contributor to health. Recently, several studies have identified associations between factors such as microbiota composition and health-related responses to dietary intake, raising the potential of personalized nutritional recommendations. To further our understanding of personalized nutrition, detailed individual data must be collected from participants in their day-to-day lives. However, this is challenging in conventional studies that require clinical measurements and site visits. So-called digital or remote cohorts allow in situ data collection on a daily basis through mobile applications, online services, and wearable sensors, but they raise questions about study retention and data quality. “Food & You” is a personalized nutrition study implemented as a digital cohort in which participants track food intake, physical activity, gut microbiota, glycemia, and other data for two to four weeks. Here, we describe the study protocol, report on study completion rates, and describe the collected data, focusing on assessing their quality and reliability. Overall, the study collected data from over 1000 participants, including high-resolution data of nutritional intake of more than 46 million kcal collected from 315,126 dishes over 23,335 participant days, 1,470,030 blood glucose measurements, 49,110 survey responses, and 1,024 stool samples for gut microbiota analysis. Retention was high, with over 60% of the enrolled participants completing the study. Various data quality assessment efforts suggest the captured high-resolution nutritional data accurately reflect individual diet patterns, paving the way for digital cohorts as a typical study design for personalized nutrition.

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Ribeiro, David, Telmo Barbosa, Jorge Ribeiro, et al. "SousChef System for Personalized Meal Recommendations: A Validation Study." Applied Sciences 12, no. 2 (2022): 702. http://dx.doi.org/10.3390/app12020702.

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Nutrition is an essential part of our life. A healthy diet can help to prevent several chronic diseases like diabetes, obesity, cancer, and cardiovascular diseases, being influenced by social, cultural, and economic factors. Meal recommender systems are a trend to assist people in finding new recipes to cook and adopt healthier eating habits. However, food choice is complex and driven by multiple factors which need to be reflected in the personalization process of these systems to ensure their adoption. We present SousChef, a meal recommender system that can help to plan multiple meals considering an individual’s food preferences, restrictions, and nutritional needs. Our approach uses recipes rather than individual food items, limiting recommendations to tasteful and culturally acceptable food combinations. Several experiments were performed to evaluate the system from different perspectives: nutritional, food preferences, and restrictions, and the recommendations’ variability. Our results highlight the importance of using extensive and diverse content in recommendations to meet food preferences, restrictions, and nutritional needs of people with different characteristics.

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Ali, Adli, Nur Hana Hamzaid, and Noor Akmal Shareela Ismail. "The Interplay of Nutriepigenomics, Personalized Nutrition and Clinical Practice in Managing Food Allergy." Life 11, no. 11 (2021): 1275. http://dx.doi.org/10.3390/life11111275.

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Food allergy in children has been a common issue due to the challenges of prescribing personalized nutrition with a lack of nutriepigenomics data. This has indeed further influenced clinical practice for appropriate management. While allergen avoidance is still the main principle in food allergy management, we require more information to advance the science behind nutrition, genes, and the immune system. Many researchers have highlighted the importance of personalized nutrition but there is a lack of data on how the decision is made. Thus, this review highlights the relationship among these key players in identifying the solution to the clinical management of food allergy with current nutriepigenomics data. The discussion integrates various inputs, including clinical assessments, biomarkers, and epigenetic information pertaining to food allergy, to curate a holistic and personalized approach to food allergy management in particular.

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Barrow, Michelle, Linda Bell, and Celia Bell. "Transforming personalized nutrition practice." Nutrition Reviews 78, no. 12 (2020): 1046–51. http://dx.doi.org/10.1093/nutrit/nuaa012.

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Abstract The strengths and limitations of current approaches to clinical nutrition practice and their underpinning research are explored in this article. It describes how a personalized nutrition practice approach supported by evidence-based pathophysiological reasoning could direct additional research, which could then transform practice and support food industry developments. Current use of the term “personalized nutrition” is reviewed and a definition is provided. Also explored are current approaches to personalized nutrition practice and evidence-based practice in clinical nutrition. Patient-centered practice, which involves individuals in their healthcare decisions, is currently being provided under the name “personalized.” An evidence-based personalized practice approach should include the use of robust, standardized, and validated tools that gather a patient’s signs and symptoms, health history, family history, genetics, environment, lifestyle, social life, diet, behavior and other factors that have an impact on physiological processes. It should also gather anthropometric measures as well as functional, diagnostic, and prognostic biomarkers for pathophysiological mechanisms. Such tools would pool n = 1 data into a case-by-case evidence base that uses computational network modelling to predict the efficacy of personalized nutrition interventions. Prediction of the efficacy of interventions should also be validated using, when possible, blinded, randomized, controlled, stratified intervention studies. This model would provide practitioners with data that support evidence-based pathophysiological reasoning. It would enable clinicians to prioritize interventions on the basis of the mechanisms of action of interventions and to ameliorate the mechanisms of pathophysiology, which are a priority for the individual. Interventions then may be applied using a patient-centered practice approach. This would transform evidence-based nutrition practice into a P4 medicine approach that is personalized, preventive, predictive, and participatory. Developing pathophysiological mechanistic understanding also provides new opportunities for stakeholders, including the food industry, researchers, healthcare practitioners, and consumers.

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Toribio-Mateas, Miguel A., and Tim D. Spector. "Could food act as personalized medicine for chronic disease?" Personalized Medicine 14, no. 3 (2017): 193–96. http://dx.doi.org/10.2217/pme-2016-0017.

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Oh, Yoori, Jieun Choi, and Yoonhee Kim. "A Personalized Dietary Coaching Method Using Food Clustering Analysis." KIPS Transactions on Software and Data Engineering 5, no. 6 (2016): 289–94. http://dx.doi.org/10.3745/ktsde.2016.5.6.289.

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Jeon, So-Hye, and Nam-Hyun Kim. "Diabetes Risk Analysis Model with Personalized Food Intake Preference." Journal of the Korea Academia-Industrial cooperation Society 14, no. 11 (2013): 5771–77. http://dx.doi.org/10.5762/kais.2013.14.11.5771.

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Coskun, Ahmet F., Justin Wong, Delaram Khodadadi, Richie Nagi, Andrew Tey, and Aydogan Ozcan. "A personalized food allergen testing platform on a cellphone." Lab Chip 13, no. 4 (2013): 636–40. http://dx.doi.org/10.1039/c2lc41152k.

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Jeong, Yong-Yun, Min-Gyu Park, Gun-Woo Kim, Seok-hwan Kim, and Kyoung-Shin Park. "A Comparative Evaluation Study on Personalized Food Recommendation Algorithms." Journal of the Korea Institute of Information and Communication Engineering 27, no. 3 (2023): 318–27. http://dx.doi.org/10.6109/jkiice.2023.27.3.318.

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Kim, Youjin, Seunghee Kang, Soyoung Maeng, Eunbi Huh, and Oran Kwon. "Industry trends in Personalized food based on nutritional stuatus." Food Science and Industry 56, no. 4 (2023): 301–20. http://dx.doi.org/10.23093/fsi.2023.56.4.301.

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Parate, Asst Prof Roshani. "Cross Platform Application for Food Delivery Services." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28259.

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In the ever-developing field of food delivery services, the " Cross-Platform Application for Food Delivery Services " prototype project brings great solutions to close this gap. The project aims to create a comprehensive and user-friendly platform that perfectly meets the needs and demands of today's customers. With the proliferation of smartphones and the transition to online services and applications, food delivery has become an important part of almost everyone's life, that is, their daily life. However, existing applications often fall short of providing a real and personalized experience, leaving users with varying needs and priorities. The current problem is that existing food delivery applications have limitations or some shortcomings; This is because these apps lack important features like loyalty services, diet filtering for special meals, personalized recommendations and pre-orders or bulk orders. Users are faced with a fragmented experience that does not meet their personal preferences, nutritional needs and other needs. Our prototype aims to define the food app service concept by offering a cross-platform application with missing features. Loyalty services encourage user retention, purchases and reward user loyalty, custom filters help with dietary restrictions, personalized recommendations enhance user experience knowledge, and pre-ordering or multi-ordering streamlines or clarifies restaurant operations and services. The solution is designed to create a comprehensive, user-oriented and technological platform that defines the new online food business model for convenience and people's satisfaction of use. The " Cross-Platform Application for Food Delivery Services " model not only solves the current problems of food delivery services, but also leads to forward-looking innovation in this field. It promises seamless, personalized and visual delivery of perfect food. This project represents a significant step towards improving the user experience and opens the door to further growth in food service. Keywords— cross platform, prototype, food delivery, services.

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Rudkowska, Iwona. "Genomics and Personalized Nutrition." Nutrients 13, no. 4 (2021): 1128. http://dx.doi.org/10.3390/nu13041128.

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Lisitsyn, A. B., I. M. Chernukha, and M. A. Nikitina. "A system approach to simulation of individual food products." Theory and practice of meat processing 5, no. 3 (2020): 12–17. http://dx.doi.org/10.21323/2414-438x-2020-5-3-12-17.

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There is no doubt that the further development in the field of nutrition is linked with personalization. Nutrition management with account for metabolism plays a key role in health strengthening and prevention of human diseases. The paper gives a review of studies associated with personalized nutrition. Personalized nutrition is inextricably linked with personalized food products. At present, however, mass production of personalized food products for individuals or small groups of people is unfeasible. The devel‑ opment of personalized food products requires both time and labor input, as well as multidisciplinary and profound knowledge in a wide spectrum of areas associated with biology, medicine, nutrition and food systems. Among the most important characteristics of modern science is the study of complex and super-complex organized objects such as the food system. These objects were studied previously but by the way of significant simplification of their structure. Investigation of objects with all variety and complexity of their organization requires not only new scientific ideas but also a new conceptual framework, new research methodology, new approaches to simulation of both products and physiological processes. In this study, the authors made an attempt to bring the theoretical view on an individual product closer to the complex task solution using the method of mathematical physiology. The intuitive conceptual model for a process of food design is shown with regard to the “health passport” of an individual, disease risk and gastrointestinal (GI) tract status. The differential equations of the concentration dynamics of protein, denatured protein and peptides in the human stomach are presented. The differential equations that describe the process of protein assimilation in the human stomach were solved in the simulation environment Simplex 3. The presented fragments of model realization show the pos‑ sibility of virtual study on an effect of different indicators of the food nutritional value on the rate of digestion and the process of cleavage of complex components (proteins, fats and carbohydrates) to mono-structural elements depending on different state and influence factors.

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Tejada-Ortigoza, Viridiana, and Enrique Cuan-Urquizo. "Towards the Development of 3D-Printed Food: A Rheological and Mechanical Approach." Foods 11, no. 9 (2022): 1191. http://dx.doi.org/10.3390/foods11091191.

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Additive manufacturing, or 3D printing, has raised interest in many areas, such as the food industry. In food, 3D printing can be used to personalize nutrition and customize the sensorial characteristics of the final product. The rheological properties of the material are the main parameters that impact the 3D-printing process and are crucial to assuring the printability of formulations, although a clear relationship between these properties and printability has not been studied in depth. In addition, an understanding of the mechanical properties of 3D-printed food is crucial for consumer satisfaction, as they are related to the texture of food products. In 3D-printing technologies, each manufacturing parameter has an impact on the resulting mechanical properties; therefore, a thorough characterization of these parameters is necessary prior to the consumption of any 3D-printed food. This review focuses on the rheological and mechanical properties of printed food materials by exploring cutting-edge research working towards developing printed food for personalized nutrition.

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González-Peña, Diana, and Lorraine Brennan. "Recent Advances in the Application of Metabolomics for Nutrition and Health." Annual Review of Food Science and Technology 10, no. 1 (2019): 479–519. http://dx.doi.org/10.1146/annurev-food-032818-121715.

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Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

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Gallo, Ignazio, Nicola Landro, Riccardo La Grassa, and Andrea Turconi. "Food Recommendations for Reducing Water Footprint." Sustainability 14, no. 7 (2022): 3833. http://dx.doi.org/10.3390/su14073833.

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Most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, kitchen assistance, or nutritional and caloric estimation of dishes, ignoring personalized and conscious food recommendations resources of the planet. Therefore, in this work, we present a personalized food recommendation scheme, mapping the ingredients to the most resource-friendly dishes on the planet and in particular, selecting recipes that contain ingredients that consume as little water as possible for their production. The system proposed here is able to understand the user’s behavior and to suggest tailor-made recipes with lower water quantity used in production. By continuously using the system, the user can gradually reduce their water footprint and benefit from a healthier diet. The proposed recommendation system was compared with the results of two papers available in the literature that represent the state of the art, obtaining similar results. Therefore, the results of the presented recommendation system can be considered reliable.

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Ma, Yizhou, and Lu Zhang. "Formulated food inks for extrusion-based 3D printing of personalized foods: a mini review." Current Opinion in Food Science 44 (April 2022): 100803. http://dx.doi.org/10.1016/j.cofs.2021.12.012.

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Spector, Tim, Francesco Asnicar, Sarah Berry, et al. "Microbiome Signatures of Nutrients, Foods and Dietary Patterns: Potential for Personalized Nutrition from The PREDICT 1 Study." Current Developments in Nutrition 4, Supplement_2 (2020): 1587. http://dx.doi.org/10.1093/cdn/nzaa062_044.

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Abstract Objectives The human gut microbiome has been linked to risk and severity of a multitude of chronic diseases, but large-scale, high-resolution studies linking it to host diet are lacking. The PREDICT 1 study (NCT03479866) enrolled 1,102 healthy US and UK adults to examine the genetic, metabolic, microbial, and meal composition/context contributions to metabolic responses to food. Here, we identify microbial features (species, genes, pathways) linked with diet and assess their potential to predict personalized food responses. Methods Dietary intake was assessed using validated EPIC (UK) and Harvard (US) semi-quantitative food frequency questionnaires (FFQs) to capture habitual intake (1 yr). Nutrient and food consumption (adjusted for energy intake) were calculated, and dietary patterns and diversity of intake were estimated. Shotgun metagenomic profiling was performed on fecal samples collected at baseline from 1,001 UK and 97 US individuals. Results We observed strong associations between overall microbial structure, as well as feature-level associations with nutrients, foods, food groups, and established dietary indices. Strongest associations were with daily intake of coffee, meat, and dairy foods, and saturated fatty acids; Spearman's r = 0.45, 0.29, 0.27 and 0.46, respectively. The Healthy Food Diversity Index and the Plant Dietary Index (PDI; comprised of healthy vs. unhealthy PDI, h-PDI, u-PDI) were strongly associated with community structure (r = 0.36, 0.34, and 0.34), highlighting the synergistic impact of dietary diversity, food quality, and microbial outcomes. We identified two clusters of microbial species with consistent, opposed correlations with ‘healthy’ and ‘unhealthy’ nutrients, food groups, and dietary indices with clear segregation between the h-PDI vs. u-PDI. These clusters were also coupled to cardiometabolic biomarkers. The associations observed in the UK cohort were reproducible in the independent US cohort. Conclusions The relationship between a healthy diet, resultant microbial signatures, and cardiometabolic outcomes strongly support the interactions between the foods we eat, the bacteria they enrich, and chronic disease outcomes, highlighting the importance of diet quality and diversity in personalized precision nutrition. Funding Sources NIHR, Wellcome Trust, Zoe Global Ltd.

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Bean, Melanie, Danyel Smith, Sarah Farthing, Elizabeth Adams, Thomas Naumann, and Morgan Meyer. "511 Automation of Home Food Inventory Scoring to Standardize Reporting, Enhance Clinical Utility, and Operationalize Delivery of Personalized Behavioral Targets." Journal of Clinical and Translational Science 6, s1 (2022): 104. http://dx.doi.org/10.1017/cts.2022.305.

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OBJECTIVES/GOALS: The Fulkerson Home Food Inventory (HFI) is widely used to assess the home food environment, a key target of behavioral weight loss trials. However, no standardized report is available. We created publicly available procedures to automate and standardize HFI reporting, yielding a personalized report to enhance this measures clinical utility. METHODS/STUDY POPULATION: Parents in the TEENS adolescent behavioral weight loss trial complete the HFI at 0-, 2-, 4-, 8-, and 12m and receive personalized reports at each timepoint. In REDCap, participants identify foods available in their home. HFI syntax is applied to calculate the obesogenic home food availability score. Categories of foods found are identified, with specific guidance provided to enhance their home food environment. Prior to automation, procedures were time intensive and error prone. To address this, HFI data are exported into Excel by a PowerShell (v7.2) command-line script using Python (v3.10) with the REDCap API. Results are calculated with F# (v6.0) using Microsoft Excel Interop API and inserted into a report template with F# using the Microsoft Publisher Interop API. This process is repeated at each timepoint. RESULTS/ANTICIPATED RESULTS: The new automated procedures significantly reduce time to generate reports and enhance accuracy. Procedures yield a 2-page individualized report that includes the obesogenic home food environment score and identifies categories of healthy items found (e.g., fruits, vegetables, whole grains) as well as areas of improvement (e.g., high-fat dairy products, processed meats). Specific items found in each category are identified. The report identifies food found in the home (e.g., chicken nuggets) with suggested healthier substitutions (e.g., lean chicken breast). This syntax and commands will be made publicly available for use in the scientific and clinical community. DISCUSSION/SIGNIFICANCE: These publicly available procedures optimize, automate, and standardize reporting for the HFI. Procedures improve efficiency within large-scale clinical trials and yield a personalized report to enhance the clinical utility of this measure and empower participants to make informed decisions about their health behaviors.

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Chen, Yi, Yandi Guo, Qiuxu Fan, Qinghui Zhang, and Yu Dong. "Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning." Foods 12, no. 10 (2023): 2079. http://dx.doi.org/10.3390/foods12102079.

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Current food recommender systems tend to prioritize either the user’s dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user’s personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph (CRKG) with millions of triplets, containing user–recipe interactions, recipe–ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model (FKGM) using knowledge graph embedding and multi-task learning. FKGM employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user’s requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that FKGM outperformed four competing baseline models in integrating users’ dietary preferences and personalized health requirements in food recommendations and performed best on the health task.

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Boddupally, Sadwika Sri, Vangaveeti Kavya Sree, Valugula Sathwik, and Neha Thakur. "A Novel Time-Aware Food Recommender System based on Deep Learning and Graph Clustering." International Journal of Scientific Methods in Intelligence Engineering Networks 02, no. 03 (2024): 26–33. http://dx.doi.org/10.58599/ijsmien.2024.2304.

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In the era of personalized recommendation systems, there exists a burgeoning need for algorithms capable of adapting to users’ preferences dynamically. Food recommendation systems face unique challenges due to the temporal nature of mealtime preferences and seasonal variations. This paper introduces a novel TimeAware Food Recommender System that integrates deep learning techniques (to capture individual preferences) with graph clustering methodologies to provide personalized and temporally relevant food suggestions. By amalgamating these approaches, the Time-Aware Food Recommender System offers recommendations that align with users’ tastes, mealtime preferences, and seasonal variations. Evaluation using real-world datasets demonstrates the superiority of the Time-Aware Food Recommender System over traditional recommendation methods, showcasing its potential for enhancing user satisfaction in food recommendation platforms.

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Rao, Kodepogu Koteswara, Chavala Shanti, Annam Jagadeeswara Rao, Songa Bosu Babu, Gaddala Lalitha Kumari, and Yalamanchili Surekha. "Personalized Smart Diet Assistance System in Health Care Prosperity with AI and AR." Ingénierie des systèmes d information 27, no. 2 (2022): 267–74. http://dx.doi.org/10.18280/isi.270210.

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Health care prosperity is the most challenging task for human being in the present dangerous COVID scenario and the discovery proposes an augmented reality based personalized smart diet assistance system which provides diet recommendations, appropriate time, type, quantity and method of consumption of a food item diet based on user health parameters based on location and event activities. The augmented reality based system comprises a user data input, an image processing, food consumption assistance, transmissible disease information retrieval and diet planning modules. The system incorporates an AI based camera to scan a food item before or after cooking and utilizes augmented reality to indicate the nutritional information. The proposed system provides personalized diet recommendations to the user based on personal data such as height, weight, existing medical conditions and thereof of a user. The system retrieves existing transmissible diseases data from world health organizations and data from news articles about any viral infections or diseases to suggest immunity boosting foods to the user to thereby safeguard the user against such diseases or infections.

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Rewane, Rutuja, and P. M. Chouragade. "Personalized Android Application for Food Identification and Calorie Count Visualization." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 1142–47. http://dx.doi.org/10.26438/ijcse/v7i4.11421147.

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Abreu, Bruno, Mariana Conceição, Mariana Garcia, Pedro Martins, and João Lima. "Personalized Portion: a New Approach in the Future Food Service." DARNIOS APLINKOS VYSTYMAS 19, no. 1 (2022): 119–26. http://dx.doi.org/10.52320/dav.v19i1.211.

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The concept of the personalized portion is associated with the amount of food adapted to each person and their needs, the proposal is based on the integration of work environment of companies that collaborate with the contract catering sector. The key points to act and implement in the companies are: the implementation of the nutrition appointments; the development of the database with the information of each employee; the translation of this information for the distribution in macronutrients and grams that, finally, are converted from the image of the employee’s "dish" of simple and fast interpretation.

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Helmy, Tarek, Ahmed Al-Nazer, Saeed Al-Bukhitan, and Ali Iqbal. "Health, Food and User's Profile Ontologies for Personalized Information Retrieval." Procedia Computer Science 52 (2015): 1071–76. http://dx.doi.org/10.1016/j.procs.2015.05.114.

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German, J. Bruce, Angela M. Zivkovic, David C. Dallas, and Jennifer T. Smilowitz. "Nutrigenomics and Personalized Diets: What Will They Mean for Food?" Annual Review of Food Science and Technology 2, no. 1 (2011): 97–123. http://dx.doi.org/10.1146/annurev.food.102308.124147.

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Burlachko, Ya O., O. E. Ryabysh, N. A. Khibuchenko, A. P. Kolbina, I. G. Dyadikova, and M. L. Maksimov. "Personalized use of minor food components in the menopause period." Glavvrač (Chief Medical Officer), no. 6 (June 14, 2022): 96–97. http://dx.doi.org/10.33920/med-03-2206-21.

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In the Russian Federation, the number of women in peri- and postmenopause is more than 21 million [1]. In addition to changes in sex hormone levels associated with menopause, its timing is an important predictor of future health outcomes, such as the duration of vasomotor symptoms and the risk of developing hormone-dependent tumors. The influence of reproductive factors (e.g., age at menarche, pregnancy) and sociodemographic factors on intermediate and long-term health outcomes of menopause has been extensively studied in the past. However, the influence of a woman’s diet on the course of menopause has not been studied sufficiently. There is also little evidence in the literature on the impact of diet at a young age, the so-called established eating patterns, on the menopause course. The authors aim to study the actual nutrition of menopausal women with manifestations of climacteric syndrome and make an attempt to influence the quality of life and the manifestation of menopausal syndrome with the help of lifestyle correction, diet modification and the use of minor biologically active substances. The aim was also to determine the features of the diet of women of reproductive age (to determine the excess and lack of nutrients) in order to create an algorithm for correcting the diet and preventing delayed risk factors.

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Gan, Junai, Justin B. Siegel, and J. Bruce German. "Molecular annotation of food – Towards personalized diet and precision health." Trends in Food Science & Technology 91 (September 2019): 675–80. http://dx.doi.org/10.1016/j.tifs.2019.07.016.

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Journal articles on the topic 'Personalized food'

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