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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: 0] [Impact Index Per Article: 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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Neo YT, Chia WY, Lim SS, Ngan CL, Kurniawan TA, Chew KW. Smart systems in producing algae-based protein to improve functional food ingredients industries. Food Res Int 2023; 165:112480. [PMID: 36869493 DOI: 10.1016/j.foodres.2023.112480] [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: 10/24/2022] [Revised: 12/29/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.
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Affiliation(s)
- Yi Ting Neo
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Wen Yi Chia
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Siew Shee Lim
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Cheng Loong Ngan
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor Darul Ehsan, Malaysia
| | | | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62, Nanyang Drive, Singapore 637459, Singapore.
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Kansou K, Laurier W, Charalambides MN, Della-Valle G, Djekic I, Feyissa AH, Marra F, Thomopoulos R, Bredeweg B. Food modelling strategies and approaches for knowledge transfer. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.01.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Mavani NR, Ali JM, Othman S, Hussain MA, Hashim H, Rahman NA. Application of Artificial Intelligence in Food Industry—a Guideline. FOOD ENGINEERING REVIEWS 2021. [PMCID: PMC8350558 DOI: 10.1007/s12393-021-09290-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.
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Affiliation(s)
- Nidhi Rajesh Mavani
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Jarinah Mohd Ali
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Suhaili Othman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
- Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, 43400 Selangor, Malaysia
| | - M. A. Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Haslaniza Hashim
- Department of Food Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
| | - Norliza Abd Rahman
- Department of Chemical and Process Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM, Selangor 43600 Bangi, Malaysia
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Suciu I, Ndiaye A, Baudrit C, Fernandez C, Kondjoyan A, Mirade P, Sicard J, Tournayre P, Bohuon P, Buche P, Courtois F, Guillard V, Athes V, Flick D, Plana-Fattori A, Trelea C, Trystram G, Delaplace G, Curet S, Della Valle D, Pottier L, Chiron H, Guessasma S, Kansou K, Kristiawan M, Della Valle G. A digital learning tool based on models and simulators for food engineering (MESTRAL). J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2020.110375] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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A Knowledge-Based System as a Sustainable Software Application for the Supervision and Intelligent Control of an Alcoholic Fermentation Process. SUSTAINABILITY 2020. [DOI: 10.3390/su122310205] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
One goal of specialists in food processing is to increase production efficiency in accordance with sustainability by optimising the consumption of raw food materials, water, and energy. One way to achieve this purpose is to develop new methods for process monitoring and control. In the winemaking industry, there is a lack of procedures regarding the common work based on knowledge acquisition and intelligent control. In the present article, we developed and tested a knowledge-based system for the alcoholic fermentation process of white winemaking while considering the main phases: the latent phase, exponential growth phase, and decay phase. The automatic control of the white wine’s alcoholic fermentation process was designed as a system on three levels. Level zero represents the measurement and adjustment loops of the bioreactor. At the first level of control, the three phases of the process are detected functions of the characteristics of the fermentation medium (the initial substrate concentration, the nitrogen assimilable content, and the initial concentration of biomass) and, thus, functions on the phase’s duration. The second level achieves the sequence supervision of the process (the operation sequence of a fermentation batch) and transforms the process into a continuous one. This control level ensures the quality of the process as well as its diagnosis. This software application can be extended to the industrial scale and can be improved by using further artificial intelligence techniques.
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Skjerdal T, Gefferth A, Spajic M, Estanga EG, de Cecare A, Vitali S, Pasquali F, Bovo F, Manfreda G, Mancusi R, Trevisiani M, Tessema GT, Fagereng T, Moen LH, Lyshaug L, Koidis A, Delgado-Pando G, Stratakos AC, Boeri M, From C, Syed H, Muccioli M, Mulazzani R, Halbert C. The STARTEC Decision Support Tool for Better Tradeoffs between Food Safety, Quality, Nutrition, and Costs in Production of Advanced Ready-to-Eat Foods. BIOMED RESEARCH INTERNATIONAL 2017; 2017:6353510. [PMID: 29457031 PMCID: PMC5804369 DOI: 10.1155/2017/6353510] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 07/20/2017] [Accepted: 08/27/2017] [Indexed: 01/20/2023]
Abstract
A prototype decision support IT-tool for the food industry was developed in the STARTEC project. Typical processes and decision steps were mapped using real life production scenarios of participating food companies manufacturing complex ready-to-eat foods. Companies looked for a more integrated approach when making food safety decisions that would align with existing HACCP systems. The tool was designed with shelf life assessments and data on safety, quality, and costs, using a pasta salad meal as a case product. The process flow chart was used as starting point, with simulation options at each process step. Key parameters like pH, water activity, costs of ingredients and salaries, and default models for calculations of Listeria monocytogenes, quality scores, and vitamin C, were placed in an interactive database. Customization of the models and settings was possible on the user-interface. The simulation module outputs were provided as detailed curves or categorized as "good"; "sufficient"; or "corrective action needed" based on threshold limit values set by the user. Possible corrective actions were suggested by the system. The tool was tested and approved by end-users based on selected ready-to-eat food products. Compared to other decision support tools, the STARTEC-tool is product-specific and multidisciplinary and includes interpretation and targeted recommendations for end-users.
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