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Liu J, Bensimon J, Lu X. Frontiers of machine learning in smart food safety. ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 111:35-70. [PMID: 39103217 DOI: 10.1016/bs.afnr.2024.06.009] [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: 08/07/2024]
Abstract
Integration of machine learning (ML) technologies into the realm of smart food safety represents a rapidly evolving field with significant potential to transform the management and assurance of food quality and safety. This chapter will discuss the capabilities of ML across different segments of the food supply chain, encompassing pre-harvest agricultural activities to post-harvest processes and delivery to the consumers. Three specific examples of applying cutting-edge ML to advance food science are detailed in this chapter, including its use to improve beer flavor, using natural language processing to predict food safety incidents, and leveraging social media to detect foodborne disease outbreaks. Despite advances in both theory and practice, application of ML to smart food safety still suffers from issues such as data availability, model reliability, and transparency. Solving these problems can help realize the full potential of ML in food safety. Development of ML in smart food safety is also driven by social and industry impacts. The improvement and implementation of legal policies brings both opportunities and challenges. The future of smart food safety lies in the strategic implementation of ML technologies, navigating social and industry impacts, and adapting to regulatory changes in the AI era.
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Affiliation(s)
- Jinxin Liu
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
| | - Jessica Bensimon
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
| | - Xiaonan Lu
- Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada.
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2
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Gong L, Lin Y. Microfluidics in smart food safety. ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 111:305-354. [PMID: 39103216 DOI: 10.1016/bs.afnr.2024.06.008] [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: 08/07/2024]
Abstract
The evolution of food safety practices is crucial in addressing the challenges posed by a growing global population and increasingly complex food supply chains. Traditional methods are often labor-intensive, time-consuming, and susceptible to human error. This chapter explores the transformative potential of integrating microfluidics into smart food safety protocols. Microfluidics, involving the manipulation of small fluid volumes within microscale channels, offers a sophisticated platform for developing miniaturized devices capable of complex tasks. Combined with sensors, actuators, big data analytics, artificial intelligence, and the Internet of Things, smart microfluidic systems enable real-time data acquisition, analysis, and decision-making. These systems enhance control, automation, and adaptability, making them ideal for detecting contaminants, pathogens, and chemical residues in food products. The chapter covers the fundamentals of microfluidics, its integration with smart technologies, and its applications in food safety, addressing the challenges and future directions in this field.
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Affiliation(s)
- Liyuan Gong
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, United States
| | - Yang Lin
- Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, United States.
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3
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Zhou F, Ma Z, Rashwan AK, Khaskheli MB, Abdelrady WA, Abdelaty NS, Hassan Askri SM, Zhao P, Chen W, Shamsi IH. Exploring the Interplay of Food Security, Safety, and Psychological Wellness in the COVID-19 Era: Managing Strategies for Resilience and Adaptation. Foods 2024; 13:1610. [PMID: 38890839 PMCID: PMC11172172 DOI: 10.3390/foods13111610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/13/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024] Open
Abstract
The global population surge presents a dual challenge and opportunity in the realms of food consumption, safety, and mental well-being. This necessitates a projected 70% increase in food production to meet growing demands. Amid this backdrop, the ongoing COVID-19 pandemic exacerbates these issues, underscoring the need for a deeper understanding of the intricate interplay between food consumption patterns and mental health dynamics during this crisis. Mitigating the spread of COVID-19 hinges upon rigorous adherence to personal hygiene practices and heightened disease awareness. Furthermore, maintaining stringent food quality and safety standards across both public and private sectors is imperative for safeguarding public health and containing viral transmission. Drawing upon existing research, this study delves into the pandemic's impact on mental health, food consumption habits, and food safety protocols. Through a comprehensive analysis, it aims to elucidate the nuanced relationship among food, food safety, and mental well-being amid the COVID-19 pandemic, highlighting synergistic effects and dynamics that underpin holistic human welfare. Our study offers a novel approach by integrating psychological wellness with food security and safety. In conceiving this review, we aimed to comprehensively explore the intricate interplay among food security, safety, and psychological wellness amid the backdrop of the COVID-19 pandemic. Our review is structured to encompass a thorough examination of existing research, synthesizing insights into the multifaceted relationships among food consumption patterns, mental health dynamics, and food safety protocols during the crisis. Our findings provide valuable insights and practical recommendations for enhancing food security and psychological well-being, thus supporting both academic research and real-world applications in crisis management and policy development.
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Affiliation(s)
- Fanrui Zhou
- Department of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of State Forestry and Grassland Administration on Highly Efficient Utilization of Forestry Biomass Resources in Southwest China, College of Material and Chemical Engineering, Southwest Forestry University, Kunming 650224, China
| | - Zhengxin Ma
- Zhejiang Key Laboratory of Crop Germplasm Resource, Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Ahmed K. Rashwan
- Department of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Department of Food and Dairy Sciences, Faculty of Agriculture, South Valley University, Qena 83523, Egypt
| | | | - Wessam A. Abdelrady
- Zhejiang Key Laboratory of Crop Germplasm Resource, Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
- Department of Agronomy, Faculty of Agriculture, South Valley University, Qena 83523, Egypt
| | - Nesma S. Abdelaty
- Department of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Department of Dairy Science, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, Egypt
| | - Syed Muhammad Hassan Askri
- Zhejiang Key Laboratory of Crop Germplasm Resource, Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Ping Zhao
- Key Laboratory of State Forestry and Grassland Administration on Highly Efficient Utilization of Forestry Biomass Resources in Southwest China, College of Material and Chemical Engineering, Southwest Forestry University, Kunming 650224, China
- Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650224, China
| | - Wei Chen
- Department of Food Science and Nutrition, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
| | - Imran Haider Shamsi
- Zhejiang Key Laboratory of Crop Germplasm Resource, Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
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4
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Zhou Z, Tian D, Yang Y, Cui H, Li Y, Ren S, Han T, Gao Z. Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection. Curr Res Food Sci 2024; 8:100679. [PMID: 38304002 PMCID: PMC10831501 DOI: 10.1016/j.crfs.2024.100679] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.
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Affiliation(s)
- Zixuan Zhou
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Daoming Tian
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
- Beidaihe Rest and Recuperation Center of PLA, Qinhuangdao, 066000, China
| | - Yingao Yang
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Han Cui
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, 300457, China
| | - Yanchun Li
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Shuyue Ren
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Tie Han
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
| | - Zhixian Gao
- Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China
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5
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Ding H, Tian J, Yu W, Wilson DI, Young BR, Cui X, Xin X, Wang Z, Li W. The Application of Artificial Intelligence and Big Data in the Food Industry. Foods 2023; 12:4511. [PMID: 38137314 PMCID: PMC10742996 DOI: 10.3390/foods12244511] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/11/2023] [Accepted: 12/16/2023] [Indexed: 12/24/2023] Open
Abstract
Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.
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Affiliation(s)
- Haohan Ding
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; (H.D.); (X.X.)
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; (J.T.); (W.L.)
| | - Jiawei Tian
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; (J.T.); (W.L.)
| | - Wei Yu
- Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand; (W.Y.); (B.R.Y.)
| | - David I. Wilson
- Electrical and Electronic Engineering Department, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Brent R. Young
- Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand; (W.Y.); (B.R.Y.)
| | - Xiaohui Cui
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; (H.D.); (X.X.)
- School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
| | - Xing Xin
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China; (H.D.); (X.X.)
| | - Zhenyu Wang
- Jiaxing Institute of Future Food, Jiaxing 314050, China;
| | - Wei Li
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China; (J.T.); (W.L.)
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6
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Chen Y, Wu C, Zhang Q, Wu D. Review of visual analytics methods for food safety risks. NPJ Sci Food 2023; 7:49. [PMID: 37699926 PMCID: PMC10497676 DOI: 10.1038/s41538-023-00226-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023] Open
Abstract
With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.
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Affiliation(s)
- Yi Chen
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China.
| | - Caixia Wu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China
| | - Qinghui Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast, Northern Ireland, UK
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7
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Chen Y, Li H, Dou H, Wen H, Dong Y. Prediction and Visual Analysis of Food Safety Risk Based on TabNet-GRA. Foods 2023; 12:3113. [PMID: 37628112 PMCID: PMC10453234 DOI: 10.3390/foods12163113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/11/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
Food safety risk prediction is crucial for timely hazard detection and effective control. This study proposes a novel risk prediction method for food safety called TabNet-GRA, which combines a specialized deep learning architecture for tabular data (TabNet) with a grey relational analysis (GRA) to predict food safety risk. Initially, this study employed a GRA to derive comprehensive risk values from fused detection data. Subsequently, a food safety risk prediction model was constructed based on TabNet, and training was performed using the detection data as inputs and the comprehensive risk values calculated via the GRA as the expected outputs. Comparative experiments with six typical models demonstrated the superior fitting ability of the TabNet-based prediction model. Moreover, a food safety risk prediction and visualization system (FSRvis system) was designed and implemented based on TabNet-GRA to facilitate risk prediction and visual analysis. A case study in which our method was applied to a dataset of cooked meat products from a Chinese province further validated the effectiveness of the TabNet-GRA method and the FSRvis system. The method can be applied to targeted risk assessment, hazard identification, and early warning systems to strengthen decision making and safeguard public health by proactively addressing food safety risks.
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Affiliation(s)
- Yi Chen
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; (H.L.); (H.D.)
| | - Hanqiang Li
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; (H.L.); (H.D.)
| | - Haifeng Dou
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China; (H.L.); (H.D.)
| | - Hong Wen
- Hubei Provincial Institute for Food Supervision and Test, Wuhan 430075, China;
| | - Yu Dong
- School of Computer Science, University of Technology Sydney, Sydney, NSW 2008, Australia;
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8
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Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0. Food Chem 2023; 409:135303. [PMID: 36586255 DOI: 10.1016/j.foodchem.2022.135303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/29/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022]
Abstract
Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.
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Wu LY, Liu FM, Weng SS, Lin WC. EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method. Foods 2023; 12:foods12112118. [PMID: 37297360 DOI: 10.3390/foods12112118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the first-generation ensemble learning prediction model (EL V.1) was introduced to Taiwan's border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model (EL V.2) was developed based on seven algorithms to enhance the "detection rate of unqualified cases" and improve the robustness of the model. In this study, Elastic Net was used to select the characteristic risk factors. Two algorithms were used to construct the new model: The Bagging-Gradient Boosting Machine and Bagging-Elastic Net. In addition, Fβ was used to flexibly control the sampling rate, improving the predictive performance and robustness of the model. The chi-square test was employed to compare the efficacy of "pre-launch (2019) random sampling inspection" and "post-launch (2020-2022) model prediction sampling inspection". For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p < 0.001) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.
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Affiliation(s)
- Li-Ya Wu
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
| | - Fang-Ming Liu
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
| | - Sung-Shun Weng
- Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Wen-Chou Lin
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
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10
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Kou X, Shi P, Gao C, Ma P, Xing H, Ke Q, Zhang D. Data-Driven Elucidation of Flavor Chemistry. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:6789-6802. [PMID: 37102791 PMCID: PMC10176570 DOI: 10.1021/acs.jafc.3c00909] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.
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Affiliation(s)
- Xingran Kou
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Peiqin Shi
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Chukun Gao
- Laboratory for Physical Chemistry, ETH Zürich, 8093 Zürich, Switzerland
| | - Peihua Ma
- Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qinfei Ke
- Collaborative Innovation Center of Fragrance Flavour and Cosmetics, School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai 201418, China
| | - Dachuan Zhang
- National Centre of Competence in Research (NCCR) Catalysis, Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
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11
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Perçin S. Identifying barriers to big data analytics adoption in circular agri-food supply chains: a case study in Turkey. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:52304-52320. [PMID: 36829092 DOI: 10.1007/s11356-023-26091-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
Big data analytics (BDA), along with the resource efficiency and sustainability perspectives of a circular economy, supports the transition to circular agri-food supply chains (AFSCs), contributing to a country's achievement of the United Nations' Sustainable Development Goals. However, there is still limited research demonstrating the importance and awareness of BDA implementation in circular AFSCs in developing countries. As a result of the barriers to BDA adoption in these regions, circular AFSCs in developing countries are still in their infancies. This study sought to identify the barriers to BDA adoption in circular AFSCs in Turkey using a Delphi-based Pythagorean fuzzy analytic hierarchy process. The proposed method removes the potential for bias and produces consensus among managers of companies in various AFSCs in Turkey. The findings of this study show that the most impactful barriers to BDA are technical, economic and social, followed by environmental and organisational. The most crucial sub-barriers to BDA adoption are "lack of trust, privacy and security", "lack of financial resources" and "lack of skilled human resources". This research can guide industry managers and policymakers in the development of strategies for overcoming barriers to BDA adoption in circular AFSCs in developing nations.
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Affiliation(s)
- Selçuk Perçin
- Department of Business Administration, Karadeniz Technical University, 61080, Trabzon, Turkey.
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12
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Ammar KA, Kheir AMS, Ali BM, Sundarakani B, Manikas I. Developing an analytical framework for estimating food security indicators in the United Arab Emirates: A review. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023; 26:1-20. [PMID: 36846351 PMCID: PMC9943759 DOI: 10.1007/s10668-023-03032-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Rapid population growth, climate change, limited natural resources, and the COVID-19 pandemic contribute to increased global hunger, necessitating intensive efforts to ensure food security and nutrition (FSN). Previous FSN approaches covered some dimensions, but not all, resulting in significant gaps in food security indicators. The Gulf Cooperation Council (GCC) and the Middle East and North Africa (MENA) regions have received less attention in food security studies, thus far necessitating considerable effort to develop an appropriate analytical framework. This study reviewed articles and international reports of FSN indicators, drivers and policies, methods, and models and extracted the challenges and gaps from the global and UAE contexts. The UAE and the world have gaps in FSN drivers, indicators, and methods, necessitating potential solutions to meet future challenges such as rapid population growth, pandemics, and limited natural resources. As a result, we created a newly developed analytical framework that addresses the shortcomings of previous approaches such as sustainable food systems developed by FAO and the Global Food Security Index (GFSI) and covers all aspects of food security. Gaps in knowledge in FSN drivers and policies, indicators, big data, methods, and models were considered in the developed framework, which has specific advantages. The novel developed framework addresses all food security dimensions (access, availability, stability, and utilization), ensuring poverty reduction, food security, and nutrition security while outperforming previous approaches (i.e., FAO and GFSI). The developed framework could be used successfully not only in the UAE and MENA, but also, globally, helping to solve food insecurity and malnutrition for future generations. The scientific community and policymakers should disseminate such solutions to address global food insecurity and ensure nutrition for future generations in the face of rapid population growth, limited natural resources, climate change, and spreading pandemics. Supplementary Information The online version contains supplementary material available at 10.1007/s10668-023-03032-3.
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Affiliation(s)
- Khalil A. Ammar
- International Center for Biosaline Agriculture, ICBA, Dubai, UAE
| | | | - Beshir M. Ali
- Faculty of Business, University of Wollongong in Dubai, Knowledge Park, Dubai, 20183 UAE
| | - Balan Sundarakani
- Faculty of Business, University of Wollongong in Dubai, Knowledge Park, Dubai, 20183 UAE
| | - Ioannis Manikas
- Faculty of Business, University of Wollongong in Dubai, Knowledge Park, Dubai, 20183 UAE
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13
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HAFSAN H, HUY DTN, VAN TUAN P, MAHMUDIONO T, DINKU T, NASIRIN C, SUTARTO S, KADHIM MM, SINGH K, AL-MAWLAWI ZS. Modelling of inactivation of microorganisms in the process of sterilization using high pressure supercritical fluids. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.111621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Affiliation(s)
- Hafsan HAFSAN
- Universitas Islam Negeri Alauddin Makassar, Indonesia
| | - Dinh Tran Ngoc HUY
- Banking University HCMC, Vietnam; International University of Japan, Japan
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14
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Zhang H, Zhang D, Wei Z, Li Y, Wu S, Mao Z, He C, Ma H, Zeng X, Xie X, Kou X, Zhang B. Analysis of public opinion on food safety in Greater China with big data and machine learning. Curr Res Food Sci 2023; 6:100468. [PMID: 36891545 PMCID: PMC9988419 DOI: 10.1016/j.crfs.2023.100468] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 02/10/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. To systematically collect and analyze public opinion on food safety in Greater China, we developed IFoodCloud, which automatically collects data from more than 3,100 public sources. Meanwhile, we constructed sentiment classification models using multiple lexicon-based and machine learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best model's F1 score achieved 0.9737, demonstrating its great predictive ability and robustness. Using IFoodCloud, we analyzed public sentiment on food safety in Greater China and the changing trend of public opinion at the early stage of the 2019 Coronavirus Disease pandemic, demonstrating the potential of big data and machine learning for promoting risk communication and decision-making.
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Affiliation(s)
- Haoyang Zhang
- Department of Agrotechnology & Food Sciences, Wageningen University and Research, 6708 PB, Wageningen, the Netherlands
| | - Dachuan Zhang
- Institute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland
| | - Zhisheng Wei
- State Key Laboratory of Food Science and Technology, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Yan Li
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Shaji Wu
- School of Perfume and Aroma, Shanghai Institute of Technology, Shanghai, 200333, China
| | - Zhiheng Mao
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Chunmeng He
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Haorui Ma
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Xin Zeng
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Xiaoling Xie
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, 710119, China
| | - Xingran Kou
- School of Perfume and Aroma, Shanghai Institute of Technology, Shanghai, 200333, China
| | - Bingwen Zhang
- Department of Food Science and Nutrition, University of Jinan, Jinan, 250002, China
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15
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Chakraborty D, Rana NP, Khorana S, Singu HB, Luthra S. Big Data in Food: Systematic Literature Review and Future Directions. JOURNAL OF COMPUTER INFORMATION SYSTEMS 2022. [DOI: 10.1080/08874417.2022.2132428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Debarun Chakraborty
- Symbiosis Institute of Business Management, Constituent of Symbiosis International (Deemed University), Nagpur, Pune, India
| | | | - Sangeeta Khorana
- Department of Economics, Finance and Entrepreneurship, Aston Business School, Birmingham, United Kingdom
| | - Hari Babu Singu
- Symbiosis Institute of Business Management, Constituent of Symbiosis International (Deemed University), Nagpur, Pune, India
| | - Sunil Luthra
- AICTE Training and Learning (ATAL) Cell, All India Council of Technical Education (AICTE), New Delhi, India
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16
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IoT-based food traceability system: Architecture, technologies, applications, and future trends. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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17
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Hassoun A, Alhaj Abdullah N, Aït-Kaddour A, Ghellam M, Beşir A, Zannou O, Önal B, Aadil RM, Lorenzo JM, Mousavi Khaneghah A, Regenstein JM. Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. Crit Rev Food Sci Nutr 2022; 64:873-889. [PMID: 35950635 DOI: 10.1080/10408398.2022.2110033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | | | - Mohamed Ghellam
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Ayşegül Beşir
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Oscar Zannou
- Faculty of Engineering, Food Engineering Department, Ondokuz Mayis University, Samsun, Turkey
| | - Begüm Önal
- Gourmet International Ltd, Izmir, Turkey
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad, Pakistan
| | - Jose M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
| | - Amin Mousavi Khaneghah
- Department of Fruit and Vegetable Product Technology, Prof. Wacław Dąbrowski Institute of Agricultural and Food Biotechnology - State Research Institute, Warsaw, Poland
| | - Joe M Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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18
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Rowan NJ. The role of digital technologies in supporting and improving fishery and aquaculture across the supply chain – Quo Vadis? AQUACULTURE AND FISHERIES 2022. [DOI: 10.1016/j.aaf.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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19
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Ammar KA, Kheir AM, Manikas I. Agricultural big data and methods and models for food security analysis-a mini-review. PeerJ 2022; 10:e13674. [PMID: 35789661 PMCID: PMC9250308 DOI: 10.7717/peerj.13674] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 06/13/2022] [Indexed: 01/17/2023] Open
Abstract
Background Big data and data analysis methods and models are important tools in food security (FS) studies for gap analysis and preparation of appropriate analytical frameworks. These innovations necessitate the development of novel methods for collecting, storing, processing, and extracting data. Methodology The primary goal of this study was to conduct a critical review of agricultural big data and methods and models used for FS studies published in peer-reviewed journals since 2010. Approximately 130 articles were selected for full content review after the pre-screening process. Results There are different sources of data collection, including but not limited to online databases, the internet, omics, Internet of Things, social media, survey rounds, remote sensing, and the Food and Agriculture Organization Corporate Statistical Database. The collected data require analysis (i.e., mining, neural networks, Bayesian networks, and other ML algorithms) before data visualization using Python, R, Circos, Gephi, Tableau, or Cytoscape. Approximately 122 models, all of which were used in FS studies worldwide, were selected from 130 articles. However, most of these models addressed only one or two dimensions of FS (i.e., availability and access) and ignored the other dimensions (i.e., stability and utilization), creating a gap in the global context. Conclusions There are certain FS gaps both worldwide and in the United Arab Emirates that need to be addressed by scientists and policymakers. Following the identification of the drivers, policies, and indicators, the findings of this review could be used to develop an appropriate analytical framework for FS and nutrition.
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Affiliation(s)
- Khalil A. Ammar
- International Center for Biosaline Agriculture, ICBA, Dubai, United Arab Emirates
| | - Ahmed M.S. Kheir
- International Center for Biosaline Agriculture, ICBA, Dubai, United Arab Emirates,Soils, Water and Environment Research Institute, Agricultural Research Center, Giza, Egypt
| | - Ioannis Manikas
- Faculty of Business, University of Wollongong in Dubai, Dubai, UAE, United Arab Emirates
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20
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Min W, Liu C, Xu L, Jiang S. Applications of knowledge graphs for food science and industry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100484. [PMID: 35607620 PMCID: PMC9122965 DOI: 10.1016/j.patter.2022.100484] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The deployment of various networks (e.g., Internet of Things [IoT] and mobile networks), databases (e.g., nutrition tables and food compositional databases), and social media (e.g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods. However, these multi-source heterogeneous food data appear as information silos, leading to difficulty in fully exploiting these food data. The knowledge graph provides a unified and standardized conceptual terminology in a structured form, and thus can effectively organize these food data to benefit various applications. In this review, we provide a brief introduction to knowledge graphs and the evolution of food knowledge organization mainly from food ontology to food knowledge graphs. We then summarize seven representative applications of food knowledge graphs, such as new recipe development, diet-disease correlation discovery, and personalized dietary recommendation. We also discuss future directions in this field, such as multimodal food knowledge graph construction and food knowledge graphs for human health.
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Affiliation(s)
- Weiqing Min
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chunlin Liu
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leyi Xu
- Soochow University, Suzhou, Jiangsu 215006, China
| | - Shuqiang Jiang
- Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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21
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Kim SS, Kim S. Impact and prospect of the fourth industrial revolution in food safety: Mini-review. Food Sci Biotechnol 2022; 31:399-406. [PMID: 35464250 PMCID: PMC8994800 DOI: 10.1007/s10068-022-01047-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/17/2022] [Accepted: 02/08/2022] [Indexed: 12/27/2022] Open
Abstract
The fourth industrial revolution represented by big data and artificial intelligence (AI), already had a significant impact on the food industry. In this review, the impacts and prospects of the 4th industrial revolution in food safety were discussed. First, the general process and characteristics of AI application from data collection to visualization are covered. Additionally, various data collection and analysis methods are discussed, with emphasis on the collection of high variety, volume, and velocity data and visualization. Available literature presents examples of machine learning applications in food samples that are mostly associated with the classification of agricultural food items through convolutional neural networks. Based on these examples, the prospects of the 4th industrial revolution in food safety are categorized as follows: prediction of food safety risk, detection of foodborne pathogens, and food safety management. This mini-review will help understand the relationship between the 4th industrial revolution and food safety.
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Affiliation(s)
- Sang-Soon Kim
- Department of Food Engineering, Dankook University, 31116 Cheonan, Republic of Korea
| | - Sangoh Kim
- Department of Plant and Food Sciences, Sangmyung University, 31066 Cheonan, Republic of Korea
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22
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Allende A, Bover-Cid S, Fernández PS. Challenges and opportunities related to the use of innovative modelling approaches and tools for microbiological food safety management. Curr Opin Food Sci 2022. [DOI: 10.1016/j.cofs.2022.100839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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23
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Makridis G, Mavrepis P, Kyriazis D. A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety. Mach Learn 2022. [DOI: 10.1007/s10994-022-06151-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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24
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Manikas I, Sundarakani B, Shehabeldin M. Big data utilisation and its effect on supply chain resilience in Emirati companies. INTERNATIONAL JOURNAL OF LOGISTICS-RESEARCH AND APPLICATIONS 2022. [DOI: 10.1080/13675567.2022.2052825] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Ioannis Manikas
- Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates
| | - Balan Sundarakani
- Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates
| | - Mohamed Shehabeldin
- Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates
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25
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Chen Y, Dou H, Chang Q, Fan C. PRIAS: An Intelligent Analysis System for Pesticide Residue Detection Data and Its Application in Food Safety Supervision. Foods 2022; 11:780. [PMID: 35327203 PMCID: PMC8947552 DOI: 10.3390/foods11060780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 12/04/2022] Open
Abstract
Pesticide residue is a prominent factor that leads to food safety problems. For this reason, many countries sample and detect pesticide residues in food every year, which generates a large amount of pesticide residue data. However, the way to deeply analyze and mine these data to quickly identify food safety risks is still an unresolved issue. In this study, we present an intelligent analysis system that supports the collection, processing, and analysis of detection data of pesticide residues. The system is first based on a number of databases such as maximum residue limit standards for the fusion of pesticide residue detection results; then, it applies a series of statistical methods to analyze pesticide residue data from multiple dimensions for quickly identifying potential risks; it uses the Apriori algorithm to mine the implicit association in the data to form pre-warning rules; finally, it applies Word document automatic generation technology to automatically generate pesticide residue analysis and pre-warning reports. The system was applied to analyze the pesticide residue detection results of 42 cities in mainland China from 2012 to 2015. Application results show that the system proposed in this study can greatly improve the depth, accuracy and efficiency of pesticide residue detection data analysis, and it can provide better decision support for food safety supervision.
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Affiliation(s)
- Yi Chen
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China;
| | - Haifeng Dou
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China;
| | - Qiaoying Chang
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China; (Q.C.); (C.F.)
| | - Chunlin Fan
- Chinese Academy of Inspection and Quarantine, Beijing 100176, China; (Q.C.); (C.F.)
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26
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Hassoun A, Aït-Kaddour A, Abu-Mahfouz AM, Rathod NB, Bader F, Barba FJ, Biancolillo A, Cropotova J, Galanakis CM, Jambrak AR, Lorenzo JM, Måge I, Ozogul F, Regenstein J. The fourth industrial revolution in the food industry-Part I: Industry 4.0 technologies. Crit Rev Food Sci Nutr 2022; 63:6547-6563. [PMID: 35114860 DOI: 10.1080/10408398.2022.2034735] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0.
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Affiliation(s)
- Abdo Hassoun
- Sustainable AgriFoodtech Innovation & Research (SAFIR), Arras, France
- Syrian Academic Expertise (SAE), Gaziantep, Turkey
| | | | - Adnan M Abu-Mahfouz
- Council for Scientific and Industrial Research, Pretoria, South Africa
- Department of Electrical & Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
| | - Nikheel Bhojraj Rathod
- Department of Post-Harvest Management of Meat, Poultry and Fish, Post-Graduate Institute of Post-Harvest Management, Raigad, Maharashtra, India
| | - Farah Bader
- Saudi Goody Products Marketing Company Ltd, Jeddah, Saudi Arabia
| | - Francisco J Barba
- Nutrition and Bromatology Area, Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Faculty of Pharmacy, University of Valencia, València, Spain
| | - Alessandra Biancolillo
- Department of Physical and Chemical Sciences, University of L'Aquila, Coppito, L'Aquila, Italy
| | - Janna Cropotova
- Department of Biological Sciences in Ålesund, Norwegian University of Science and Technology, Ålesund, Norway
| | - Charis M Galanakis
- Research & Innovation Department, Galanakis Laboratories, Chania, Greece
- Food Waste Recovery Group, ISEKI Food Association, Vienna, Austria
| | - Anet Režek Jambrak
- Faculty of Food Technology and Biotechnology, University of Zagreb, Zagreb, Croatia
| | - José M Lorenzo
- Centro Tecnológico de la Carne de Galicia, Ourense, Spain
- Área de Tecnología de los Alimentos, Facultad de Ciencias de Ourense, Universidad de Vigo, Ourense, Spain
| | - Ingrid Måge
- Fisheries and Aquaculture Research, Nofima - Norwegian Institute of Food, Ås, Norway
| | - Fatih Ozogul
- Department of Seafood Processing Technology, Faculty of Fisheries, Cukurova University, Adana, Turkey
| | - Joe Regenstein
- Department of Food Science, Cornell University, Ithaca, New York, USA
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27
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CHEN TC, YU SY. Research on food safety sampling inspection system based on deep learning. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.29121] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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28
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Raja V, Krishnamoorthy S, Moses J, Anandharamakrishnan C. ICT applications for the food industry. FUTURE FOODS 2022. [DOI: 10.1016/b978-0-323-91001-9.00001-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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29
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Wang X, Bouzembrak Y, Lansink AO, van der Fels-Klerx HJ. Application of machine learning to the monitoring and prediction of food safety: A review. Compr Rev Food Sci Food Saf 2021; 21:416-434. [PMID: 34907645 DOI: 10.1111/1541-4337.12868] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/15/2021] [Accepted: 10/21/2021] [Indexed: 12/13/2022]
Abstract
Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.
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Affiliation(s)
- Xinxin Wang
- Business Economics, Wageningen University & Research, Wageningen, The Netherlands
| | - Yamine Bouzembrak
- Wageningen Food Safety Research, Wageningen University & Research, Wageningen, The Netherlands
| | - Agjm Oude Lansink
- Business Economics, Wageningen University & Research, Wageningen, The Netherlands
| | - H J van der Fels-Klerx
- Business Economics, Wageningen University & Research, Wageningen, The Netherlands.,Wageningen Food Safety Research, Wageningen University & Research, Wageningen, The Netherlands
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30
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31
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Zhou Q, Zhang H, Wang S. Artificial intelligence, big data, and blockchain in food safety. INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2021. [DOI: 10.1515/ijfe-2021-0299] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Food safety plays an essential role in our daily lives, and it becomes serious with the development of worldwide trade. To tackle the food safety issues, many advanced technologies have been developed to monitor the process of the food industry (FI) to ensure food safety, including the process of food production, processing, transporting, storage, and retailing. These technologies are often referred to as artificial intelligence (AI), big data, and blockchain, which have been widely applied in many research areas. In this review, we introduce these technologies and their applications in the food safety domain. Firstly, basic concepts of these technologies are presented. Then, applications for food safety from a data perspective based on these technologies are analyzed. Finally, future challenges of the applications of AI, big data, and blockchain are discussed.
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Affiliation(s)
- Qinqin Zhou
- College of Food Science and Engineering, Nanjing University of Finance and Economics , Nanjing 210023 , China
| | - Hao Zhang
- College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics , Nanjing 210016 , China
| | - Suya Wang
- College of Food Science and Engineering, Nanjing University of Finance and Economics , Nanjing 210023 , China
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32
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Jacobs N, Brewer S, Craigon PJ, Frey J, Gutierrez A, Kanza S, Manning L, Munday S, Pearson S, Sacks J. Considering the ethical implications of digital collaboration in the Food Sector. PATTERNS (NEW YORK, N.Y.) 2021; 2:100335. [PMID: 34820642 PMCID: PMC8600150 DOI: 10.1016/j.patter.2021.100335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The Internet of Food Things Network+ (IoFT) and the Artificial Intelligence and Augmented Intelligence for Automated Investigation for Scientific Discovery Network+ (AI3SD) brought together an interdisciplinary multi-institution working group to create an ethical framework for digital collaboration in the food industry. This will enable the exploration of implications and consequences (both intentional and unintentional) of using cutting-edge technologies to support the implementation of data trusts and other forms of digital collaboration in the food sector. This article describes how we identified areas for ethical consideration with respect to digital collaboration and the use of Industry 4.0 technologies in the food sector and describes the different interdisciplinary methodologies being used to produce this framework. The research questions and objectives that are being addressed by the working group are laid out, with a report on our ongoing work. The article concludes with recommendations about working on projects in this area. This working group is aiming to create an ethical framework to elicit questions, facilitate discussions, and enable the exploration of the implications and consequences of digital collaboration in the food supply chain in line with the approach of responsible innovation. Ethics is a complex, diverse, and interdisciplinary area and cannot be formalized to provide a singular “right answer”. Because technology has significant ethical implications, we must empower developers, companies, and other stakeholders to engage with this complexity. To do this, individuals and companies alike need to be provided with methods of understanding the issues and trade-offs that could arise from their technology and processes. This endeavor is not one that can be worked on alone; it requires an interdisciplinary team and the use of a range of methodologies to understand and frame the issues at stake. Furthermore, running this initiative as part of two networks has provided access to a wealth of further expertise to aid with evaluation and feedback on our research.
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Affiliation(s)
- Naomi Jacobs
- Imagination Lancaster, LICA, Lancaster University, Lancaster, Lancashire LA1 4YW, UK
| | - Steve Brewer
- The Lincoln Institute of Agri-Food Technology, University of Lincoln, Lincoln LN1 2LG, UK
| | - Peter J Craigon
- Future Food Beacon of Excellence and School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nottingham LE12 5RD, UK
| | - Jeremy Frey
- School of Chemistry, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Anabel Gutierrez
- School of Business and Management, Royal Holloway University of London, Egham TW20 0EX, UK
| | - Samantha Kanza
- School of Chemistry, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | | | - Samuel Munday
- School of Chemistry, Faculty of Engineering & Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Simon Pearson
- The Lincoln Institute of Agri-Food Technology, University of Lincoln, Lincoln LN1 2LG, UK
| | - Justin Sacks
- Imagination Lancaster, LICA, Lancaster University, Lancaster, Lancashire LA1 4YW, UK
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33
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Abstract
Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.
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Katikou P. Digital Technologies and Open Data Sources in Marine Biotoxins' Risk Analysis: The Case of Ciguatera Fish Poisoning. Toxins (Basel) 2021; 13:692. [PMID: 34678985 PMCID: PMC8539326 DOI: 10.3390/toxins13100692] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/25/2021] [Accepted: 09/28/2021] [Indexed: 12/25/2022] Open
Abstract
Currently, digital technologies influence information dissemination in all business sectors, with great emphasis put on exploitation strategies. Public administrations often use information systems and establish open data repositories, primarily supporting their operation but also serving as data providers, facilitating decision-making. As such, risk analysis in the public health sector, including food safety authorities, often relies on digital technologies and open data sources. Global food safety challenges include marine biotoxins (MBs), being contaminants whose mitigation largely depends on risk analysis. Ciguatera Fish Poisoning (CFP), in particular, is a MB-related seafood intoxication attributed to the consumption of fish species that are prone to accumulate ciguatoxins. Historically, CFP occurred endemically in tropical/subtropical areas, but has gradually emerged in temperate regions, including European waters, necessitating official policy adoption to manage the potential risks. Researchers and policy-makers highlight scientific data inadequacy, under-reporting of outbreaks and information source fragmentation as major obstacles in developing CFP mitigation strategies. Although digital technologies and open data sources provide exploitable scientific information for MB risk analysis, their utilization in counteracting CFP-related hazards has not been addressed to date. This work thus attempts to answer the question, "What is the current extent of digital technologies' and open data sources' utilization within risk analysis tasks in the MBs field, particularly on CFP?", by conducting a systematic literature review of the available scientific and grey literature. Results indicate that the use of digital technologies and open data sources in CFP is not negligible. However, certain gaps are identified regarding discrepancies in terminology, source fragmentation and a redundancy and downplay of social media utilization, in turn constituting a future research agenda for this under-researched topic.
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Affiliation(s)
- Panagiota Katikou
- Ministry of Rural Development and Food, Directorate General of Rural Development, Directorate of Research, Innovation and Education, Hapsa & Karatasou 1, 54626 Thessaloniki, Greece
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Application Research: Big Data in Food Industry. Foods 2021; 10:foods10092203. [PMID: 34574314 PMCID: PMC8467977 DOI: 10.3390/foods10092203] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 12/04/2022] Open
Abstract
A huge amount of data is being produced in the food industry, but the application of big data—regulatory, food enterprise, and food-related media data—is still in its infancy. Each data source has the potential to develop the food industry, and big data has broad application prospects in areas like social co-governance, exploit of consumption markets, quantitative production, new dishes, take-out services, precise nutrition and health management. However, there are urgent problems in technology, health and sustainable development that need to be solved to enable the application of big data to the food industry.
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Talari G, Cummins E, McNamara C, O'Brien J. State of the art review of Big Data and web-based Decision Support Systems (DSS) for food safety risk assessment with respect to climate change. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.08.032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Biermann O, Koya SF, Corkish C, Abdalla SM, Galea S. Food, Big Data, and Decision-making: a Scoping Review-the 3-D Commission. J Urban Health 2021; 98:69-78. [PMID: 34414511 PMCID: PMC8440752 DOI: 10.1007/s11524-021-00562-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/07/2021] [Indexed: 12/16/2022]
Abstract
Food is an important determinant of health, featuring prominently in the Sustainable Development Goals. The term "big data" is seldom used in relation to food, partly because food data are scattered across different sectors. The increasing availability of food-related data presents an opportunity to glean new insights on food and food systems. These insights may enhance the quality of products and services and improve decision-making on optimizing food availability, all to the end of producing better health. Yet, knowledge gaps remain about the unique opportunities and challenges linked to big data on food and their use in decision-making. This scoping review explored the available literature linking food with big data and decision-making, using the following research question: What is the current literature on data about food, and how are these data used in decision-making? We searched PubMed until 29 February 2020 and Embase, Web of Sciences, and the Cochrane Database of Systematic Reviews until 8 March 2020. We included studies written in English and conducted narrative analyses to identify relevant themes from included studies. Sixteen studies fulfilled our eligibility criteria, including big data analyses, modelling studies, and reviews. These studies described the added value of using big data and how evidence from big data had or can be used for decision-making, as well as challenges and opportunities for such use. The majority of the included studies examined the link between food and big data, while hypothesizing of how these insights could inform decision-making, including policies, interventions, programs, and financing. There were only two examples wherein big data on food informed decision-making directly. The review highlights several false dichotomies in how the subject is approached in the literature and the importance of context, both between and within countries, in shaping the availability and types of data that can be used as meaningful evidence to inform decision-making. This review shows the paucity of research around the intersection of food, big data, and decision-making, as well as the potential in using big data on food systems to the end of informing decisions to improve the health of populations. Future research and decision-making around health systems can benefit from examining the full spectrum of perspectives on the subject. Future research and decision-making around health systems can also employ the steadfast embrace of technology, which will potentially reduce disparities in big data availability, to the end of improving the health of populations.
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Affiliation(s)
- Olivia Biermann
- Department of Global Public Health, Karolinska Institutet, Solna, Sweden
- Rockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA
| | - Shaffi Fazaludeen Koya
- Rockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, USA
| | - Claire Corkish
- Rockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, USA
| | - Salma M Abdalla
- Rockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, USA
| | - Sandro Galea
- Rockefeller Foundation-Boston University 3-D Commission on Determinants, Data, and Decision-making, Boston, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, USA
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Abstract
Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.
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Affiliation(s)
- George-John Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece;
| | - Emma Sims
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, United Kingdom
| | - Panagiotis Tsakanikas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, 11855 Athens, Greece;
| | - Fady Mohareb
- Bioinformatics Group, Department of Agrifood, School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, United Kingdom
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39
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Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance. BIG DATA AND COGNITIVE COMPUTING 2021. [DOI: 10.3390/bdcc5030028] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.
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40
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Donaghy JA, Danyluk MD, Ross T, Krishna B, Farber J. Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain. Front Microbiol 2021; 12:668196. [PMID: 34093486 PMCID: PMC8177817 DOI: 10.3389/fmicb.2021.668196] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/01/2021] [Indexed: 01/11/2023] Open
Abstract
Foodborne pathogens are a major contributor to foodborne illness worldwide. The adaptation of a more quantitative risk-based approach, with metrics such as Food safety Objectives (FSO) and Performance Objectives (PO) necessitates quantitative inputs from all stages of the food value chain. The potential exists for utilization of big data, generated through digital transformational technologies, as inputs to a dynamic risk management concept for food safety microbiology. The industrial revolution in Internet of Things (IoT) will leverage data inputs from precision agriculture, connected factories/logistics, precision healthcare, and precision food safety, to improve the dynamism of microbial risk management. Furthermore, interconnectivity of public health databases, social media, and e-commerce tools as well as technologies such as blockchain will enhance traceability for retrospective and real-time management of foodborne cases. Despite the enormous potential of data volume and velocity, some challenges remain, including data ownership, interoperability, and accessibility. This paper gives insight to the prospective use of big data for dynamic risk management from a microbiological safety perspective in the context of the International Commission on Microbiological Specifications for Foods (ICMSF) conceptual equation, and describes examples of how a dynamic risk management system (DRMS) could be used in real-time to identify hazards and control Shiga toxin-producing Escherichia coli risks related to leafy greens.
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Affiliation(s)
- John A Donaghy
- Corporate Operations - Quality Management (Food Safety) Société des Produits Nestlé S.A., Vevey, Switzerland
| | - Michelle D Danyluk
- IFAS Food Science and Human Nutrition, University of Florida, Gainesville, FL, United States
| | - Tom Ross
- Centre for Food Safety and Innovation, University of Tasmania, Hobart, TSA, Australia
| | - Bobby Krishna
- Department of Food Safety, Dubai Municipality, Dubai, United Arab Emirates
| | - Jeff Farber
- Department of Food Science, University of Guelph, Guelph, ON, Canada
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41
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Kumari L, Jaiswal P, Tripathy SS. Various techniques useful for determination of adulterants in valuable saffron: A review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.061] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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42
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Ventura V, Cavaliere A, Iannò B. #Socialfood: Virtuous or vicious? A systematic review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.02.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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43
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Dórea FC, Revie CW. Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making. Front Vet Sci 2021; 8:633977. [PMID: 33778039 PMCID: PMC7994248 DOI: 10.3389/fvets.2021.633977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/18/2021] [Indexed: 11/20/2022] Open
Abstract
The biggest change brought about by the “era of big data” to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex “variety” dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.
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Affiliation(s)
- Fernanda C Dórea
- Department of Disease Control and Epidemiology, National Veterinary Institute, Uppsala, Sweden
| | - Crawford W Revie
- Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom
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44
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Jia XX, Li S, Han DP, Chen RP, Yao ZY, Ning BA, Gao ZX, Fan ZC. Development and perspectives of rapid detection technology in food and environment. Crit Rev Food Sci Nutr 2021; 62:4706-4725. [PMID: 33523717 DOI: 10.1080/10408398.2021.1878101] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Food safety become a hot issue currently with globalization of food trade and food supply chains. Chemical pollution, microbial contamination and adulteration in food have attracted more attention worldwide. Contamination with antibiotics, estrogens and heavy metals in water environment and soil environment have also turn into an enormous threat to food safety. Traditional small-scale, long-term detection technologies have been unable to meet the current needs. In the monitoring process, rapid, convenient, accurate analysis and detection technologies have become the future development trend. We critically synthesizing the current knowledge of various rapid detection technology, and briefly touched upon the problem which still exist in research process. The review showed that the application of novel materials promotes the development of rapid detection technology, high-throughput and portability would be popular study directions in the future. Of course, the ultimate aim of the research is how to industrialization these technologies and apply to the market.
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Affiliation(s)
- Xue-Xia Jia
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China.,State Key Laboratory of Food Nutrition and Safety, China International Scientific & Technological Cooperation Base for Health Biotechnology, College of Food Engineering and Biotechnology, Tianjin University of Science & Technology, Tianjin, P.R. China
| | - Shuang Li
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Dian-Peng Han
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Rui-Peng Chen
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Zi-Yi Yao
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Bao-An Ning
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Zhi-Xian Gao
- Institute of Environmental and Operational Medicine, Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin, P.R. China
| | - Zhen-Chuan Fan
- State Key Laboratory of Food Nutrition and Safety, China International Scientific & Technological Cooperation Base for Health Biotechnology, College of Food Engineering and Biotechnology, Tianjin University of Science & Technology, Tianjin, P.R. China
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45
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Abstract
Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment. In this article, we provide a gentle introduction to machine learning in the context of food safety and an overview of recent developments and applications. With many of these applications still in their nascence, general and domain-specific pitfalls and challenges associated with machine learning have begun to be recognized and addressed, which are critical to prospective use and future deployment of large data sets and their associated machine learning models for food safety applications.
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Affiliation(s)
- Xiangyu Deng
- Center for Food Safety, University of Georgia, Griffin, Georgia 30223, USA;
| | - Shuhao Cao
- Department of Mathematics and Statistics, Washington University, St. Louis, Missouri 63105, USA;
| | - Abigail L Horn
- Department of Preventive Medicine, University of Southern California, Los Angeles, California 90032, USA;
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46
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Sense–Analyze–Respond–Actuate (SARA) Paradigm: Proof of Concept System Spanning Nanoscale and Macroscale Actuation for Detection of Escherichia coli in Aqueous Media. ACTUATORS 2020. [DOI: 10.3390/act10010002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Foodborne pathogens are a major concern for public health. We demonstrate for the first time a partially automated sensing system for rapid (~17 min), label-free impedimetric detection of Escherichia coli spp. in food samples (vegetable broth) and hydroponic media (aeroponic lettuce system) based on temperature-responsive poly(N-isopropylacrylamide) (PNIPAAm) nanobrushes. This proof of concept (PoC) for the Sense-Analyze-Respond-Actuate (SARA) paradigm uses a biomimetic nanostructure that is analyzed and actuated with a smartphone. The bio-inspired soft material and sensing mechanism is inspired by binary symbiotic systems found in nature, where low concentrations of bacteria are captured from complex matrices by brush actuation driven by concentration gradients at the tissue surface. To mimic this natural actuation system, carbon-metal nanohybrid sensors were fabricated as the transducer layer, and coated with PNIPAAm nanobrushes. The most effective coating and actuation protocol for E. coli detection at various temperatures above/below the critical solution temperature of PNIPAAm was determined using a series of electrochemical experiments. After analyzing nanobrush actuation in stagnant media, we developed a flow through system using a series of pumps that are triggered by electrochemical events at the surface of the biosensor. SARA PoC may be viewed as a cyber-physical system that actuates nanomaterials using smartphone-based electroanalytical testing of samples. This study demonstrates thermal actuation of polymer nanobrushes to detect (sense) bacteria using a cyber-physical systems (CPS) approach. This PoC may catalyze the development of smart sensors capable of actuation at the nanoscale (stimulus-response polymer) and macroscale (non-microfluidic pumping).
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47
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Xu Y, Li X, Zeng X, Cao J, Jiang W. Application of blockchain technology in food safety control:current trends and future prospects. Crit Rev Food Sci Nutr 2020; 62:2800-2819. [PMID: 33307729 DOI: 10.1080/10408398.2020.1858752] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Blockchain technology is a distributed ledger technology and is expected to face some difficulties and challenges in various industries due to its transparency, decentralization, tamper-proof nature, and encryption security. Food safety has been paid increasing attention in recent years with economic development. Based on a systematic literature critical analysis, the causes of food safety problems and the state-of-the-art blockchain technology overview, including the definition of blockchain, development history, classification, structure, characteristics, and main applications, the feasibility and application prospects of blockchain technology in plant food safety, animal food safety, and processed food safety were proposed in this review. Finally, the challenges of the blockchain technology itself and the difficulties in the application of food safety were analyzed. This study contributes to the extant literature in the field of food safety by discovering the excellent potential of blockchain technology and its implications for food safety control. Our results indicated that blockchain is a promising technology toward a food safety control, with many ongoing initiatives in food products, but many food-related issues, barriers, and challenges still exist. Nevertheless, it is expected to provide a feasible solution for controlling food safety risks.
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Affiliation(s)
- Yan Xu
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Xiangxin Li
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Xiangquan Zeng
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Jiankang Cao
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
| | - Weibo Jiang
- College of Food Science and Nutritional Engineering, China Agricultural, University, Beijing, PR, China
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48
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49
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Chen X, Voigt T. Implementation of the Manufacturing Execution System in the food and beverage industry. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109932] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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50
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Faustman C, Aaron D, Negowetti N, Leib EB. Ten years post-GAO assessment, FDA remains uninformed of potentially harmful GRAS substances in foods. Crit Rev Food Sci Nutr 2020; 61:1260-1268. [PMID: 32338036 DOI: 10.1080/10408398.2020.1756217] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
It has been approximately 10 years since the Government Accountability Office (GAO) published its report to Congress entitled, FDA Should Strengthen Its Oversight of Food Ingredients Determined to be Generally Recognized as Safe (GRAS), which strongly criticized FDA noting that its "oversight process does not help ensure the safety of all new GRAS determinations." Congress requested GAO to undertake this audit as a result of concerns that GRAS substances added to foods did not require FDA approval. Since 2010, FDA has addressed only a few of the criticisms regarding its process for establishing a food substance as GRAS. However, several of the most important GAO recommendations remain unaddressed, and most critically, FDA has chosen to remain uninformed about food substances self-determined as GRAS by manufacturers. In its 2016 final rule Substances Generally Recognized as Safe, FDA did not take the opportunity to include a provision for creation of a master list of all GRAS chemicals used in food, nor did the FDA request the authority to do so from Congress. FDA cannot fulfill its statutory obligation for ensuring the chemical safety of the U.S. food supply if it does not know which substances, in which quantities, have been added to foods.
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Affiliation(s)
- Cameron Faustman
- Department of Animal Science, University of Connecticut, Storrs, Connecticut, USA
| | - Daniel Aaron
- Food Law and Policy Clinic, Harvard Law School, Cambridge, Massachusetts, USA
| | - Nicole Negowetti
- Animal Law and Policy Program, Harvard Law School, Cambridge, Massachusetts, USA
| | - Emily Broad Leib
- Food Law and Policy Clinic, Harvard Law School, Cambridge, Massachusetts, USA
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