Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

被引:25
作者
Gbashi, Sefater [1 ]
Njobeh, Patrick Berka [1 ]
机构
[1] Univ Johannesburg, Fac Sci, Dept Biotechnol & Food Technol, Doornfontein Campus,POB 17011, ZA-2028 Gauteng, South Africa
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 08期
基金
新加坡国家研究基金会;
关键词
artificial intelligence; machine learning; food integrity; food safety; food quality control; food hazards; nutritional health; SYSTEM; QUALITY; CLASSIFICATION; ADULTERATION; PREDICTION; FRAUD;
D O I
10.3390/app14083421
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system.
引用
收藏
页数:28
相关论文
共 116 条
[1]  
agshift, AgShift Hydra-Power the Transformation of Food through AI
[2]  
Alavi N., 2012, International Journal of Agricultural Technology, V8, P1243
[3]   Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model [J].
Alfian, Ganjar ;
Syafrudin, Muhammad ;
Farooq, Umar ;
Ma'arif, Muhammad Rifqi ;
Syaekhoni, M. Alex ;
Fitriyani, Norma Latif ;
Lee, Jaeho ;
Rhee, Jongtae .
FOOD CONTROL, 2020, 110
[4]  
[Anonymous], ESTIMATING BURDEN FO
[5]  
Asay C.D., 2019, Wm. Mary L. Rev, V61, P1187
[6]   DyMnO3/Fe2O3 nanocomposites: simple sol-gel auto-combustion technique and photocatalytic performance for water treatment [J].
Baladi, Mahin ;
Ghanbari, Mojgan ;
Valian, Movlud ;
Salavati-Niasari, Masoud .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 28 (09) :11066-11076
[7]  
Balamurugan S., 2019, International Journal of Engineering and Advanced Technology, V9, P2995, DOI DOI 10.35940/IJEAT.A1379.109119
[8]   A Blockchain-Driven Food Supply Chain Management Using QR Code and XAI-Faster RCNN Architecture [J].
Bhatia, Surbhi ;
Albarrak, Abdulaziz Saad .
SUSTAINABILITY, 2023, 15 (03)
[9]   Application of Bayesian Networks in the development of herbs and spices sampling monitoring system [J].
Bouzembrak, Yamine ;
Camenzuli, Louise ;
Janssen, Esmee ;
van der Fels-Klerx, H. J. .
FOOD CONTROL, 2018, 83 :38-44
[10]   Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling [J].
Bouzembrak, Yamine ;
Marvin, Hans J. P. .
FOOD CONTROL, 2016, 61 :180-187