Microfluidics in smart food safety.

Artificial intelligence (AI) Big data analytics Contaminant detection Food safety Internet of Things (IoT) Microfluidics Pathogen detection Smart technologies

Journal

Advances in food and nutrition research
ISSN: 1043-4526
Titre abrégé: Adv Food Nutr Res
Pays: United States
ID NLM: 9001271

Informations de publication

Date de publication:
2024
Historique:
medline: 6 8 2024
pubmed: 6 8 2024
entrez: 5 8 2024
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 39103216
pii: S1043-4526(24)00056-1
doi: 10.1016/bs.afnr.2024.06.008
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

305-354

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Liyuan Gong (L)

Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, United States.

Yang Lin (Y)

Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI, United States. Electronic address: yanglin@uri.edu.

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Classifications MeSH