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Luo S, Zhang J, Sun J, Zhao T, Deng J, Yang H. Future development trend of food-borne delivery systems of functional substances for precision nutrition. ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 112:385-433. [PMID: 39218507 DOI: 10.1016/bs.afnr.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
BACKGROUND Precision nutrition, a personalized nutritional supplementation model, is widely acknowledged for its significant impact on human health. Nevertheless, challenges persist in the advancement of precision nutrition, including consumer dietary behaviors, nutrient absorption, and utilization. Thus, the exploration of effective strategies to enhance the efficacy of precision nutrition and maximize its potential benefits in dietary interventions and disease management is imperative. SCOPE AND APPROACH The primary objective of this comprehensive review is to synthesize and assess the latest technical approaches and future prospects for achieving precision nutrition, while also addressing the existing constraints in this field. The role of delivery systems is pivotal in the realization of precision nutrition goals. This paper outlines the potential applications of delivery systems in precision nutrition and highlights key considerations for their design and implementation. Additionally, the review offers insights into the evolving trends in delivery systems for precision nutrition, particularly in the realms of nutritional fortification, specialized diets, and disease prevention. KEY FINDINGS AND CONCLUSIONS By leveraging computer data collection, omics, and metabolomics analyses, this review scrutinizes the lifestyles, dietary patterns, and health statuses of diverse organisms. Subsequently, tailored nutrient supplementation programs are devised based on individual organism profiles. The utilization of delivery systems enhances the bioavailability of functional compounds and enables targeted delivery to specific body regions, thereby catering to the distinct nutritional requirements and disease prevention needs of consumers, with a particular emphasis on special populations and dietary preferences.
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Affiliation(s)
- Shuwei Luo
- College of Food Science and Nutritional and Engineering, China Agricultural University, Beijing, P.R. China
| | - Juntao Zhang
- College of Food Science and Nutritional and Engineering, China Agricultural University, Beijing, P.R. China
| | - Jing Sun
- College of Food Science and Nutritional and Engineering, China Agricultural University, Beijing, P.R. China
| | - Tong Zhao
- College of Food Science and Nutritional and Engineering, China Agricultural University, Beijing, P.R. China
| | - Jianjun Deng
- State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, P.R. China
| | - Haixia Yang
- College of Food Science and Nutritional and Engineering, China Agricultural University, Beijing, P.R. China.
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Theodore Armand TP, Nfor KA, Kim JI, Kim HC. Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients 2024; 16:1073. [PMID: 38613106 PMCID: PMC11013624 DOI: 10.3390/nu16071073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.
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Affiliation(s)
- Tagne Poupi Theodore Armand
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea; (T.P.T.A.); (J.-I.K.)
| | - Kintoh Allen Nfor
- Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea;
| | - Jung-In Kim
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea; (T.P.T.A.); (J.-I.K.)
| | - Hee-Cheol Kim
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea; (T.P.T.A.); (J.-I.K.)
- Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea;
- College of AI Convergence, u-AHRC, Inje University, Gimhae 50834, Republic of Korea
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MacMillan Uribe AL, Patterson J. Are Nutrition Professionals Ready for Artificial Intelligence? JOURNAL OF NUTRITION EDUCATION AND BEHAVIOR 2023; 55:623. [PMID: 37684082 DOI: 10.1016/j.jneb.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Affiliation(s)
| | - Julie Patterson
- College of Health and Human Sciences, Northern Illinois University, DeKalb, IL
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Angelsen A, Starke AD, Trattner C. Healthiness and environmental impact of dinner recipes vary widely across developed countries. NATURE FOOD 2023; 4:407-415. [PMID: 37156979 DOI: 10.1038/s43016-023-00746-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 04/06/2023] [Indexed: 05/10/2023]
Abstract
Contrary to food ingredients, little is known about recipes' healthiness or environmental impact. Here we examine 600 dinner recipes from Norway, the UK and the USA retrieved from cookbooks and the Internet. Recipe healthiness was assessed by adherence to dietary guidelines and aggregate health indicators based on front-of-pack nutrient labels, while environmental impact was assessed through greenhouse gas emissions and land use. Our results reveal that recipe healthiness strongly depends on the healthiness indicator used, with more than 70% of the recipes being classified as healthy for at least one front-of-pack label, but less than 1% comply with all dietary guidelines. All healthiness indicators correlated positively with each other and negatively with environmental impact. Recipes from the USA, found to use more red meat, have a higher environmental impact than those from Norway and the UK.
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Affiliation(s)
| | - Alain D Starke
- Amsterdam School of Communication Research, University of Amsterdam, Amsterdam, Netherlands
- MediaFutures: Research Centre for Responsible Media Technology & Innovation, University of Bergen, Bergen, Norway
| | - Christoph Trattner
- MediaFutures: Research Centre for Responsible Media Technology & Innovation, University of Bergen, Bergen, Norway.
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Clifford Astbury C. Health and sustainability of everyday food. NATURE FOOD 2023; 4:357. [PMID: 37156978 DOI: 10.1038/s43016-023-00761-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
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Karabay A, Bolatov A, Varol HA, Chan MY. A Central Asian Food Dataset for Personalized Dietary Interventions. Nutrients 2023; 15:nu15071728. [PMID: 37049566 PMCID: PMC10096622 DOI: 10.3390/nu15071728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 04/03/2023] Open
Abstract
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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Affiliation(s)
- Aknur Karabay
- Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan
| | - Arman Bolatov
- Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan
| | - Huseyin Atakan Varol
- Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan
| | - Mei-Yen Chan
- School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan
- Correspondence:
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Prediction, Discovery, and Characterization of Plant- and Food-Derived Health-Beneficial Bioactive Peptides. Nutrients 2022; 14:nu14224810. [PMID: 36432497 PMCID: PMC9697201 DOI: 10.3390/nu14224810] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/31/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Nature may have the answer to many of our questions about human, animal, and environmental health. Natural bioactives, especially when harvested from sustainable plant and food sources, provide a plethora of molecular solutions to nutritionally actionable, chronic conditions. The spectrum of these conditions, such as metabolic, immune, and gastrointestinal disorders, has changed with prolonged human life span, which should be matched with an appropriately extended health span, which would in turn favour more sustainable health care: "adding years to life and adding life to years". To date, bioactive peptides have been undervalued and underexploited as food ingredients and drugs. The future of translational science on bioactive peptides-and natural bioactives in general-is being built on (a) systems-level rather than reductionist strategies for understanding their interdependent, and at times synergistic, functions; and (b) the leverage of artificial intelligence for prediction and discovery, thereby significantly reducing the time from idea and concept to finished solutions for consumers and patients. This new strategy follows the path from benefit definition via design to prediction and, eventually, validation and production.
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Deep learning accurately predicts food categories and nutrients based on ingredient statements. Food Chem 2022; 391:133243. [PMID: 35623276 DOI: 10.1016/j.foodchem.2022.133243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/06/2022] [Accepted: 05/16/2022] [Indexed: 11/20/2022]
Abstract
Determining attributes such as classification, creating taxonomies and nutrients for foods can be a challenging and resource-intensive task, albeit important for a better understanding of foods. In this study, a novel dataset, 134 k BFPD, was collected from USDA Branded Food Products Database with modification and labeled with three food taxonomy and nutrient values and became an artificial intelligence (AI) dataset that covered the largest food types to date. Overall, the Multi-Layer Perceptron (MLP)-TF-SE method obtained the highest learning efficiency for food natural language processing tasks using AI, which achieved up to 99% accuracy for food classification and 0.98 R2 for calcium estimation (0.93 ∼ 0.97 for calories, protein, sodium, total carbohydrate, total lipids, etc.). The deep learning approach has great potential to be embedded in other food classification and regression tasks and as an extension to other applications in the food and nutrient scope.
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Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting. ELECTRONICS 2022. [DOI: 10.3390/electronics11101626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Artificial intelligence (AI) is among the major emerging research areas and industrial application fields. An important area of its application is in the preventive healthcare domain, in which appropriate dietary intake reporting is critical in assessing nutrient content. The traditional dietary assessment is cumbersome in terms of dish accuracy and time-consuming. The recent technology in computer vision with automatic recognition of dishes has the potential to support better dietary assessment. However, due to the wide variety of available foods, especially local dishes, improvements in food recognition are needed. In this research, we proposed an AI-based multiple-dish food recognition model using the EfficientDet deep learning (DL) model. The designed model was developed taking into consideration three types of meals, namely single-dish, mixed-dish, and multiple-dish, from local Taiwanese cuisine. The results demonstrate high mean average precision (mAP) = 0.92 considering 87 types of dishes. With high recognition performance, the proposed model has the potential for a promising solution to enhancing dish reporting. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud-computing-based system to enhance the accuracy of current dietary intake reporting tasks.
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Timotijevic L, Carr I, De La Cueva J, Eftimov T, Hodgkins CE, Koroušić Seljak B, Mikkelsen BE, Selnes T, Van't Veer P, Zimmermann K. Responsible Governance for a Food and Nutrition E-Infrastructure: Case Study of the Determinants and Intake Data Platform. Front Nutr 2022; 8:795802. [PMID: 35402471 PMCID: PMC8984108 DOI: 10.3389/fnut.2021.795802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/23/2021] [Indexed: 11/13/2022] Open
Abstract
The focus of the current paper is on a design of responsible governance of food consumer science e-infrastructure using the case study Determinants and Intake Data Platform (DI Data Platform). One of the key challenges for implementation of the DI Data Platform is how to develop responsible governance that observes the ethical and legal frameworks of big data research and innovation, whilst simultaneously capitalizing on huge opportunities offered by open science and the use of big data in food consumer science research. We address this challenge with a specific focus on four key governance considerations: data type and technology; data ownership and intellectual property; data privacy and security; and institutional arrangements for ethical governance. The paper concludes with a set of responsible research governance principles that can inform the implementation of DI Data Platform, and in particular: consider both individual and group privacy; monitor the power and control (e.g., between the scientist and the research participant) in the process of research; question the veracity of new knowledge based on big data analytics; understand the diverse interpretations of scientists' responsibility across different jurisdictions.
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Affiliation(s)
- Lada Timotijevic
- School of Psychology, University of Surrey, Guildford, United Kingdom
- *Correspondence: Lada Timotijevic
| | - Indira Carr
- School of Law, University of Surrey, Guildford, United Kingdom
| | | | | | - Charo E. Hodgkins
- School of Psychology, University of Surrey, Guildford, United Kingdom
| | | | - Bent E. Mikkelsen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Trond Selnes
- Wageningen Economic Research, Wageningen University and Research Centre, Wageningen, Netherlands
| | - Pieter Van't Veer
- Wageningen Economic Research, Wageningen University and Research Centre, Wageningen, Netherlands
| | - Karin Zimmermann
- Wageningen Economic Research, Wageningen University and Research Centre, Wageningen, Netherlands
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Computational gastronomy: A data science approach to food. J Biosci 2022. [DOI: 10.1007/s12038-021-00248-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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12
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Starke AD, Willemsen MC, Trattner C. Nudging Healthy Choices in Food Search Through Visual Attractiveness. Front Artif Intell 2021; 4:621743. [PMID: 33969288 PMCID: PMC8102049 DOI: 10.3389/frai.2021.621743] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 02/12/2021] [Indexed: 11/24/2022] Open
Abstract
Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction.
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Affiliation(s)
- Alain D Starke
- Marketing and Consumer Behaviour Group, Wageningen University and Research, Wageningen, Netherlands.,Department of Information Science and Media Studies, University of Bergen, Bergen, Norway
| | - Martijn C Willemsen
- Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands.,Recommender Lab, Jheronimus Academy of Data Science, 's-Hertogenbosch, Netherlands
| | - Christoph Trattner
- Department of Information Science and Media Studies, University of Bergen, Bergen, Norway
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