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Annual Review of Biomedical Data Science

Volume 4, 2021

Data Science in the Food Industry

Abstract

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers’ lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers’ health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

Keyword(s): big data, food microbiology, food safety, machine learning, omics
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2021-07-20
2024-04-19
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Data Science in the Food Industry
Annual Review of Biomedical Data Science 4, 341 (2021); https://doi.org/10.1146/annurev-biodatasci-020221-123602
/content/journals/10.1146/annurev-biodatasci-020221-123602
/content/journals/10.1146/annurev-biodatasci-020221-123602
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