Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models.

Autor: Taiwo OR; Genomics Unit, Helix Biogen Institute, Ogbomosho, Oyo, Nigeria., Onyeaka H; School of Chemical Engineering, University of Birmingham, Edgbaston B15 2TT, Birmingham, UK., Oladipo EK; Genomics Unit, Helix Biogen Institute, Ogbomosho, Oyo, Nigeria.; Department of Microbiology, Laboratory of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, Osun, Nigeria., Oloke JK; Department of Natural Science, Microbiology Unit, Precious Cornerstone University, Ibadan, Oyo, Nigeria., Chukwugozie DC; Department of Microbiology, Federal University Otuoke, Otuoke, Bayelsa, Nigeria.
Jazyk: angličtina
Zdroj: International journal of microbiology [Int J Microbiol] 2024 May 17; Vol. 2024, pp. 6612162. Date of Electronic Publication: 2024 May 17 (Print Publication: 2024).
DOI: 10.1155/2024/6612162
Abstrakt: Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2024 Oluseyi Rotimi Taiwo et al.)
Databáze: MEDLINE
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