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Raki H, Aalaila Y, Taktour A, Peluffo-Ordóñez DH. Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review. Foods 2023; 13:11. [PMID: 38201039 PMCID: PMC10777928 DOI: 10.3390/foods13010011] [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: 10/24/2023] [Revised: 11/27/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024] Open
Abstract
On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.
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Affiliation(s)
- Hind Raki
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
| | - Yahya Aalaila
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
| | - Ayoub Taktour
- Materials Sciences and Nanotechnoloy (MSN), University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco;
| | - Diego H. Peluffo-Ordóñez
- College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco; (Y.A.); (D.H.P.-O.)
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Yu H, Liu S, Qin H, Zhou Z, Zhao H, Zhang S, Mao J. Artificial intelligence-based approaches for traditional fermented alcoholic beverages' development: review and prospect. Crit Rev Food Sci Nutr 2022; 64:2879-2889. [PMID: 36310425 DOI: 10.1080/10408398.2022.2128034] [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
Traditional fermented alcoholic beverages (TFABs) have gained widespread acceptance and enjoyed great popularity for centuries. COVID-19 pandemics lead to the surge in health demand for diet, thus TFABs once again attract increased focus for the health benefits. Though the production technology is quite mature, food companies and research institutions are looking for transformative innovation in TFABs to make healthy, nutritious offerings that give a competitive advantage in current beverage market. The implementation of intelligent platforms enables companies and researchers to gather, store and analyze data in a more convenient way. The development of data collection methods contributed to the big data environment of TFABs, providing a fresh perspective that helps brewers to observe and improve the production steps. Among data analytical tools, Artificial Intelligence (AI) is considered to be one of the most promising methodological approaches for big data analytics and decision-making of automated production, and machine learning (ML) is an important method to fulfill the goal. This review describes the development trends and challenges of TFABs in big data era and summarize the application of AI-based methods in TFABs. Finally, we provide perspectives on the potential research directions of new frontiers in application of AI approaches in the supply chain of TFABs.
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Affiliation(s)
- Huakun Yu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, China
| | - Shuangping Liu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China
- Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China
- Luzhou Laojiao Group Co. Ltd, Luzhou, China
| | - Hui Qin
- Luzhou Laojiao Group Co. Ltd, Luzhou, China
| | - Zhilei Zhou
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China
- Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing, Zhejiang, China
| | - Hongyuan Zhao
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China
- Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, China
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China
| | - Suyi Zhang
- Luzhou Laojiao Group Co. Ltd, Luzhou, China
| | - Jian Mao
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and technology, Jiangnan University, Wuxi, China
- Southern Marine Science and Engineering Guangdong Laboratory, Guangzhou, China
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing, Zhejiang, China
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Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146986] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.
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