Literature DB >> 35464250

Impact and prospect of the fourth industrial revolution in food safety: Mini-review.

Sang-Soon Kim1, Sangoh Kim2.   

Abstract

The fourth industrial revolution represented by big data and artificial intelligence (AI), already had a significant impact on the food industry. In this review, the impacts and prospects of the 4th industrial revolution in food safety were discussed. First, the general process and characteristics of AI application from data collection to visualization are covered. Additionally, various data collection and analysis methods are discussed, with emphasis on the collection of high variety, volume, and velocity data and visualization. Available literature presents examples of machine learning applications in food samples that are mostly associated with the classification of agricultural food items through convolutional neural networks. Based on these examples, the prospects of the 4th industrial revolution in food safety are categorized as follows: prediction of food safety risk, detection of foodborne pathogens, and food safety management. This mini-review will help understand the relationship between the 4th industrial revolution and food safety. © The Korean Society of Food Science and Technology 2022.

Entities:  

Keywords:  Artificial intelligence; Big data; Food industry; Food safety; Machine learning

Year:  2022        PMID: 35464250      PMCID: PMC8994800          DOI: 10.1007/s10068-022-01047-6

Source DB:  PubMed          Journal:  Food Sci Biotechnol        ISSN: 1226-7708            Impact factor:   2.391


  8 in total

Review 1.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 2.  Big data in food safety: An overview.

Authors:  Hans J P Marvin; Esmée M Janssen; Yamine Bouzembrak; Peter J M Hendriksen; Martijn Staats
Journal:  Crit Rev Food Sci Nutr       Date:  2017-07-24       Impact factor: 11.176

3.  Development of an improved selective and differential medium for isolation of Salmonella spp.

Authors:  Sang-Hyun Park; Sangryeol Ryu; Dong-Hyun Kang
Journal:  J Clin Microbiol       Date:  2012-07-18       Impact factor: 5.948

4.  Strain-Level Metagenomic Analysis of the Fermented Dairy Beverage Nunu Highlights Potential Food Safety Risks.

Authors:  Aaron M Walsh; Fiona Crispie; Kareem Daari; Orla O'Sullivan; Jennifer C Martin; Cornelius T Arthur; Marcus J Claesson; Karen P Scott; Paul D Cotter
Journal:  Appl Environ Microbiol       Date:  2017-08-01       Impact factor: 4.792

Review 5.  Recent (2011-2017) foodborne outbreak cases in the Republic of Korea compared to the United States: a review.

Authors:  Sang-Oh Kim; Sang-Soon Kim
Journal:  Food Sci Biotechnol       Date:  2021-02-06       Impact factor: 2.391

6.  Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model.

Authors:  Hanhyeok Im; Seung-Ho Hwang; Byoung Sik Kim; Sang Ho Choi
Journal:  Proc Natl Acad Sci U S A       Date:  2021-05-18       Impact factor: 11.205

7.  Detection and Identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via Machine Learning Based FTIR Spectroscopy.

Authors:  Murat Bağcıoğlu; Martina Fricker; Sophia Johler; Monika Ehling-Schulz
Journal:  Front Microbiol       Date:  2019-04-26       Impact factor: 5.640

8.  Real-time analysis and predictability of the health functional food market using big data.

Authors:  Sang-Soon Kim; Seokwon Lim; Sangoh Kim
Journal:  Food Sci Biotechnol       Date:  2021-11-26       Impact factor: 2.391

  8 in total

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