scholarly journals Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model

AI & Society ◽  
2021 ◽  
Author(s):  
Salvatore Sapienza ◽  
Anton Vedder
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
Food Safety ◽  
Data Collection ◽  
Scientific Information ◽  
Safety Risk ◽  
Advantages And Disadvantages ◽  
Data Collection And Analysis ◽  
Food Safety Risk

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.

scholarly journals Improving Food safety in Supply Chain based on Big Data

2018 ◽  
Vol 53 ◽  
pp. 03084
Author(s):  
Gang Liu ◽  
Guang Li ◽  
Rui Yang ◽  
Li Guo
Keyword(s):  
Big Data ◽  
Food Safety ◽  
Data Collection ◽  
Safety Management ◽  
Rapid Development ◽  
Warning System ◽  
Risk Warning ◽  
Data Collection And Analysis ◽  
Data Source ◽  
Food Safety Risk

With the rapid development of big data collection and analysis, these tools are increasingly applied to food safety and quality. Big data can play an important role in improving food safety management. This paper will deeply analyze the food safety risk warning system based on big data management. The research results show that the food safety management system based on big data includes data source, data collection and storage, data analysis and application of analysis results.

scholarly journals Enlisting Supervised Machine Learning in Mapping Scientific Uncertainty Expressed in Food Risk Analysis

2017 ◽  
Vol 48 (3) ◽  
pp. 608-641 ◽  
Author(s):  
Akos Rona-Tas ◽  
Antoine Cornuéjols ◽  
Sandrine Blanchemanche ◽  
Antonin Duroy ◽  
Christine Martin
Keyword(s):  
Machine Learning ◽  
Food Safety ◽  
Supervised Machine Learning ◽  
Risk Assessments ◽  
Scientific Uncertainty ◽  
Empirical Measure ◽  
Taxonomic Structure ◽  
Safety Risk ◽  
Food Safety Risk

Recently, both sociology of science and policy research have shown increased interest in scientific uncertainty. To contribute to these debates and create an empirical measure of scientific uncertainty, we inductively devised two systems of classification or ontologies to describe scientific uncertainty in a large corpus of food safety risk assessments with the help of machine learning (ML). We ask three questions: (1) Can we use ML to assist with coding complex documents such as food safety risk assessments on a difficult topic like scientific uncertainty? (2) Can we assess using ML the quality of the ontologies we devised? (3) And, finally, does the quality of our ontologies depend on social factors? We found that ML can do surprisingly well in its simplest form identifying complex meanings, and it does not benefit from adding certain types of complexity to the analysis. Our ML experiments show that in one ontology which is a simple typology, against expectations, semantic opposites attract each other and support the taxonomic structure of the other. And finally, we found some evidence that institutional factors do influence how well our taxonomy of uncertainty performs, but its ability to capture meaning does not vary greatly across the time, institutional context, and cultures we investigated.

Qualitative Microbiological Risk Assessment of Moringa oleifera Leaf Powder To Be Used To Treat Undernutrition in Infants and Children in Cambodia and India: A Review

2019 ◽  
Vol 82 (3) ◽  
pp. 513-521
Author(s):  
KAVITA WALIA ◽  
ANSDEEP KAPOOR ◽  
JEFFREY M. FARBER
Keyword(s):  
Risk Assessment ◽  
Food Safety ◽  
Literature Search ◽  
Moringa Oleifera ◽  
Infants And Children ◽  
Salmonella Spp ◽  
Home Setting ◽  
Safety Risk ◽  
Leaf Powder ◽  
Food Safety Risk

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.

scholarly journals Spoilage Bacteria Identification and Food Safety Risk Assessment of Whole Soybean Curd

2019 ◽  
Vol 59 (2) ◽  
pp. 250-253
Author(s):  
Chenzhi Wang ◽  
Qinling Du ◽  
Tianwei Yao ◽  
Hongmin Dong ◽  
Dingtao Wu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Food Safety ◽  
Safety Risk ◽  
Bacteria Identification ◽  
Soybean Curd ◽  
Spoilage Bacteria ◽  
Whole Soybean Curd ◽  
Food Safety Risk

Forty Years of Food Safety Risk Assessment: A History and Analysis

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