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Cited by 67Food Safety and HygieneFood Supply Chain TraceabilitySalmonella and Campylobacter epidemiology

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain

Open access
Donaghy, John A., Danyluk, Michelle D., Ross, Tom, Krishna, Bobby, Farber, Jeff·2021·Frontiers in Microbiology
DOI 10.3389/fmicb.2021.668196PMC PMC8177817
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Abstract

Foodborne pathogens are a major contributor to foodborne illness worldwide. The adaptation of a more quantitative risk-based approach, with metrics such as Food safety Objectives (FSO) and Performance Objectives (PO) necessitates quantitative inputs from all stages of the food value chain. The potential exists for utilization of big data, generated through digital transformational technologies, as inputs to a dynamic risk management concept for food safety microbiology. The industrial revolution in Internet of Things (IoT) will leverage data inputs from precision agriculture, connected factories/logistics, precision healthcare, and precision food safety, to improve the dynamism of microbial risk management. Furthermore, interconnectivity of public health databases, social media, and e-commerce tools as well as technologies such as blockchain will enhance traceability for retrospective and real-time management of foodborne cases. Despite the enormous potential of data volume and velocity, some challenges remain, including data ownership, interoperability, and accessibility. This paper gives insight to the prospective use of big data for dynamic risk management from a microbiological safety perspective in the context of the International Commission on Microbiological Specifications for Foods (ICMSF) conceptual equation, and describes examples of how a dynamic risk management system (DRMS) could be used in real-time to identify hazards and control Shiga toxin-producing Escherichia coli risks related to leafy greens.

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Crossref · 10.3389/fmicb.2021.668196

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Figures

Figure 1 A number of examples of the types of big data currently or potentially available across the leafy green food chain that can be integrated for use in traceback to determine implicated product source during outbreak situations, and can be used to inform dynamic risk management systems (DRMS).
Figure 1 A number of examples of the types of big data currently or potentially available across the leafy green food chain that can be integrated for use in traceback to determine implicated product source during outbreak situations, and can be used to inform dynamic risk management systems (DRMS).CC BY · PubMed Central
Figure 2 Stylized DRMS. Dynamic risk management involves the use of big data for quick identification of changing conditions resulting in potential changes to the hazard analysis and facilitates decision-making to manage those risks in real-time. (A) through (D) represent examples of how a DRMS could have been used to identify hazards and control Shiga toxin-producing Escherichia coli (STEC) risks related to leafy greens. (A) represents the baseline conceptual risk management in a fresh-cut leafy greens operation, where the initial hazard level, controlled by preventive measures during production is below the Food Safety Objectives (FSO), and (i) no introduction or cross-contamination occurs during harvest; (ii) there is a small reduction during washing, and (iii) potential increases in STEC populations are controlled by the cold chain. (B) represents STEC contamination of leafy greens during processing. The initial hazard level is controlled by preventive measures during production, and (i) no introduction or cross-contamination occurs during harvest, (ii) untreated water used during the wash step introduces a high level of STEC, and (iii) potential increases in STEC populations are controlled by the cold chain. In this case, DRMS would have had the real-time data surrounding the wash water (see Figure 1), flagged a concern, and allowed for risk management decision-making that could have prevented entry of this product into the supply chain. (C) represents STEC contamination of leafy greens during production. Due to a breakdown in pre-harvest preventive measures, the initial hazard level is higher than the baseline, which may be due to a combination of factors including (i) proximity of concentrated animal feeding operations (CAFOs), (ii) unusual weather patterns (i.e., winds and freezing), and/or (iii) contaminated water sources used in applications that contacted harvested product. For this scenario, (i) no introduction or cross-contamination occurs during harvest; (ii) there is a small reduction during washing, and (iii) potential increases in STEC populations are controlled by the cold chain. In this example, DRMS would have the real-time data around production practices (see Figure 1), flagged a concern, and allowed for risk management decision-making to prevent entry of this product into the supply chain. (D) represents a potential response to (C) using DRMS. When DRMS flagged a concern with production water use (see Figure 1), the risk management decision is made to treat the water (e.g., chemical treatment, UV light) that contacts the harvestable portion of the leafy greens (i.e., overhead irrigation, foliar sprays, and aerial sprays) prior to application to reduce the introduction of STEC from this source. The risk management decision to treat the irrigation water decreases STEC levels to below the FSO.
Figure 2 Stylized DRMS. Dynamic risk management involves the use of big data for quick identification of changing conditions resulting in potential changes to the hazard analysis and facilitates decision-making to manage those risks in real-time. (A) through (D) represent examples of how a DRMS could have been used to identify hazards and control Shiga toxin-producing Escherichia coli (STEC) risks related to leafy greens. (A) represents the baseline conceptual risk management in a fresh-cut leafy greens operation, where the initial hazard level, controlled by preventive measures during production is below the Food Safety Objectives (FSO), and (i) no introduction or cross-contamination occurs during harvest; (ii) there is a small reduction during washing, and (iii) potential increases in STEC populations are controlled by the cold chain. (B) represents STEC contamination of leafy greens during processing. The initial hazard level is controlled by preventive measures during production, and (i) no introduction or cross-contamination occurs during harvest, (ii) untreated water used during the wash step introduces a high level of STEC, and (iii) potential increases in STEC populations are controlled by the cold chain. In this case, DRMS would have had the real-time data surrounding the wash water (see Figure 1), flagged a concern, and allowed for risk management decision-making that could have prevented entry of this product into the supply chain. (C) represents STEC contamination of leafy greens during production. Due to a breakdown in pre-harvest preventive measures, the initial hazard level is higher than the baseline, which may be due to a combination of factors including (i) proximity of concentrated animal feeding operations (CAFOs), (ii) unusual weather patterns (i.e., winds and freezing), and/or (iii) contaminated water sources used in applications that contacted harvested product. For this scenario, (i) no introduction or cross-contamination occurs during harvest; (ii) there is a small reduction during washing, and (iii) potential increases in STEC populations are controlled by the cold chain. In this example, DRMS would have the real-time data around production practices (see Figure 1), flagged a concern, and allowed for risk management decision-making to prevent entry of this product into the supply chain. (D) represents a potential response to (C) using DRMS. When DRMS flagged a concern with production water use (see Figure 1), the risk management decision is made to treat the water (e.g., chemical treatment, UV light) that contacts the harvestable portion of the leafy greens (i.e., overhead irrigation, foliar sprays, and aerial sprays) prior to application to reduce the introduction of STEC from this source. The risk management decision to treat the irrigation water decreases STEC levels to below the FSO.CC BY · PubMed Central

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Citations

Cites

Show all 56 references

Research works printed but not linked: 2

  1. Armbruster, 2014, Informatics to Support International Food Safety, 127
  2. Hasnan, 2018, Short Review: Application Areas of Industry 4.0 Technologies

Cited by

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    “Simultaneously, real-time monitoring technologies integrating bioinformatics with multi-omics and digital health data are starting to allow for the long-term tracking of probiotic strains and host responses, especially in translational and post-market settings [159,160].”

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Metadata

Authors
Donaghy, J. A., Danyluk, M. D., Ross, T., Krishna, B., & Farber, J.
Affiliations
  • Nestlé (Switzerland) (CH)
  • University of Florida (US)
  • University of Tasmania (AU)
  • University of Guelph (CA)
Corresponding author
Donaghy, John A.
ORCID
Venue
Frontiers in Microbiology
Publisher
Frontiers Media SA
Published
2021-05-21
Volume
12
Pages
668196-668196
ISSN
1664-302X (ISSN-L)
DOI
10.3389/fmicb.2021.668196
PMC
PMC8177817
OpenAlex
W3161815819
Cited by
67
References
56
Citations by year
2026: 16 · 2025: 14 · 2024: 13 · 2023: 8 · 2022: 15 · 2021: 1
FWCI
5.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Microbiology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Food Safety and Hygiene
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Food Supply Chain Traceability
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Salmonella and Campylobacter epidemiology
Keywords
big data, dynamic risk management, food safety, foodborne pathogens, quantitative risk assessment, food safety objectives, performance goals, Internet of Things, food traceability, blockchain, Shiga toxin-producing Escherichia coli, leafy vegetables, data interoperability

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CoChat. (2026). Citation check: Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain. https://app.cochat.ai/papers/10.3389/fmicb.2021.668196
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Donaghy, J. A., Danyluk, M. D., Ross, T., Krishna, B., & Farber, J. (2021). Big Data Impacting Dynamic Food Safety Risk Management in the Food Chain. Frontiers in Microbiology. https://doi.org/10.3389/fmicb.2021.668196