Research of food safety risk assessment methods based on big data

Author(s):  
Yongjun Ma ◽  
Yangyang Hou ◽  
Yushan Liu ◽  
Yonghao Xue
AI & Society ◽  
2021 ◽  
Author(s):  
Salvatore Sapienza ◽  
Anton Vedder

AbstractBig data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed data raises unique questions regarding the interplay of multiple regulatory systems alongside food safety legislation. Provisions aiming to preserve the confidentiality of data and protect personal information are juxtaposed to norms prescribing the public disclosure of scientific information. This research is intended to provide guidance for data governance and data ownership issues that unfold from the ongoing transformation of the technical and legal domains of food safety risk assessment. Following the reconstruction of technological advances in data collection and analysis and the description of recent amendments to food safety legislation, emerging concerns are discussed in light of the individual, collective and social implications of the deployment of cutting-edge Big Data collection and analysis techniques. Then, a set of principle-based recommendations is proposed by adapting high-level principles enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment. The proposed set of recommendations adopts Safety, Accountability, Fairness, Explainability, Transparency as core principles (SAFETY), whereas Privacy and data protection are used as a meta-principle.


2019 ◽  
Vol 82 (3) ◽  
pp. 513-521
Author(s):  
KAVITA WALIA ◽  
ANSDEEP KAPOOR ◽  
JEFFREY M. FARBER

ABSTRACT This qualitative risk assessment (QRA) was conducted to estimate the microbiological risk associated with the consumption of Moringa oleifera leaf powder (MLP) by infants and children ages 6 to 23 months to prevent or treat undernutrition in Siem Reap, Cambodia, and Madhya Pradesh, India. This QRA follows the Codex Alimentarius Commission principles and guidelines for risk assessment and takes into account all known microbial hazards that are associated with MLP. A comprehensive literature search was carried out for foodborne pathogens isolated from MLP and other dried foods of similar consistency, such as dried herbs and spices and flour. From this literature search, the following pathogens were identified and considered for this microbiological QRA: Bacillus cereus, Escherichia coli, Campylobacter spp., Clostridium perfringens, Cronobacter spp., Listeria monocytogenes, Salmonella spp., and Staphylococcus aureus. Results suggest that when cereal slurry (porridge) fortified with MLP is boiled (a rolling boil for 5 min) prior to consumption, the food safety risk to undernourished infants and children of B. cereus, C. perfringens type A, Cronobacter, enterohemorrhagic E. coli, L. monocytogenes, Salmonella spp., and S. aureus is low to moderate, with only a moderate to serious risk posed by C. perfringens type C. However, when the fortified porridge is not boiled before consumption, the food safety risk is increased for all of the evaluated pathogens. Overall, the QRA suggests that MLP presents a risk to undernourished infants and children. However, this risk can be mitigated when the powder is stored under the appropriate conditions to ensure there is no ingress of moisture and then processed in a hygienic manner to reduce contamination and/or cross-contamination by following hazard analysis critical control point or similar procedures (even in a home setting) including a heat treatment, i.e., boiling, to further reduce microbial hazards.


2019 ◽  
Vol 59 (2) ◽  
pp. 250-253
Author(s):  
Chenzhi Wang ◽  
Qinling Du ◽  
Tianwei Yao ◽  
Hongmin Dong ◽  
Dingtao Wu ◽  
...  

Risk Analysis ◽  
2020 ◽  
Vol 40 (S1) ◽  
pp. 2218-2230
Author(s):  
Felicia Wu, ◽  
Joseph V. Rodricks

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