Y. Bouzembrak

发表

A holistic approach to food safety risks: Food fraud as an example.

H. J. van der Fels-Klerx, G. Kleter, E. V. van Asselt, 2016, Food research international.

A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks

S. Tofail, H. Bouwmeester, Finbarr Murphy, 2016, Nanoscale Research Letters.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Finbarr Murphy, Martin Mullins, Yamine Bouzembrak, 2017, Nanotoxicology.

Role of analytical testing for food fraud risk mitigation – A commentary on implementation of analytical fraud testing

H. Marvin, Y. Bouzembrak, N. Luijckx, 2021, Current research in food science.

Effective sampling strategy to detect food and feed contamination: Herbs and spices case

H. J. Fels-Klerx, Y. Bouzembrak, 2018 .

A system approach towards prediction of food safety hazards: Impact of climate and agrichemical use on the occurrence of food safety hazards

H. Marvin, Y. Bouzembrak, 2020 .

Expert-driven methodology to assess and predict the effects of drivers of change on vulnerabilities in a food supply chain: Aquaculture of Atlantic salmon in Norway as a showcase

G. Kleter, R. Hannisdal, H. Marvin, 2020 .

Designing a monitoring program for aflatoxin B1 in feed products using machine learning

H. J. van der Fels-Klerx, Y. Bouzembrak, A. O. Oude Lansink, 2022, npj Science of Food.

Global media as an early warning tool for food fraud; an assessment of MedISys-FF

L. Manning, A. Gavai, H. Marvin, 2022, Food Control.

Exploring the role of green and Industry 4.0 technologies in achieving sustainable development goals in food sectors.

A. Hassoun, M. Prieto, G. Bono, 2022, Food research international.

Automatic classification of literature in systematic reviews on food safety using machine learning

A. Gavai, H. Marvin, Y. Bouzembrak, 2021, Current Research in Food Science.

Big data in food safety: An overview

Yamine Bouzembrak, Martijn Staats, Hans J P Marvin, 2017, Critical reviews in food science and nutrition.

Internet of Things in food safety: Literature review and a bibliometric analysis

Yamine Bouzembrak, Hans J.P. Marvin, Anand Gavai, 2019 .

Supply Chain Network Design under Uncertainty with Evidence Theory

Eric Lefevre, Ahmed Samet, Yamine Bouzembrak, 2017, Logist. Res..

Applicability of a food chain analysis on aquaculture of Atlantic salmon to identify and monitor vulnerabilities and drivers of change for the identification of emerging risks

Hans J.P. Marvin, Yamine Bouzembrak, Gijs A. Kleter, 2019, EFSA Supporting Publications.

Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling

Yamine Bouzembrak, Hans J.P. Marvin, H. Marvin, 2016 .

Application of machine learning to the monitoring and prediction of food safety: A review.

H. J. van der Fels-Klerx, Y. Bouzembrak, Xinxin Wang, 2021, Comprehensive reviews in food science and food safety.

Big Data in food safety- A review

Anand Gavai, Ningjing Liu, Leonieke M. van den Bulk, 2020, Current Opinion in Food Science.

Finding unknown stimulants applied in food supplements using Artificial Intelligence

Yamine Bouzembrak, Hans Mol, Lucas Heuvel, 2021 .

Artificial intelligence to detect unknown stimulants from scientific literature and media reports

A. Gavai, H. Mol, H. Marvin, 2021 .

Automated food safety early warning system in the dairy supply chain using machine learning

Yamine Bouzembrak, Anand Gavai, Hans J.P. Marvin, 2020, Food Control.

Determination and Metrics for Emerging Risks Identification DEMETER: Final Report

R. Comber, A. Fischer, L. Frewer, 2020 .

Impact of drivers of change, including climatic factors, on the occurrence of chemical food safety hazards in fruits and vegetables: A Bayesian Network approach

Yamine Bouzembrak, Hans J.P. Marvin, H. Marvin, 2019, Food Control.

Digitalisation and Artificial Intelligence for sustainable food systems

H. J. van der Fels-Klerx, H. Vrolijk, B. Tekinerdogan, 2022, Trends in Food Science & Technology.

Bayesian Networks modeling of diarrhetic shellfish poisoning in Mytilus edulis harvested in Bantry Bay, Ireland.

H. Marvin, Y. Bouzembrak, F. Butler, 2022, Harmful algae.

Applying federated learning to combat food fraud in food supply chains.

A. Gavai, R. Kaliyaperumal, J. van Soest, 2023, NPJ science of food.

Digital transformation in the agri-food industry: recent applications and the role of the COVID-19 pandemic

J. M. Regenstein, A. Hassoun, R. Bhat, 2023, Frontiers in Sustainable Food Systems.

Predicting Nitrogen Excretion of Dairy Cattle with Machine Learning

Herman Mollenhorst, Roel F. Veerkamp, Claudia Kamphuis, 2020, ISESS.

Identification of potential vulnerable points and paths of contamination in the Dutch broiler meat trade network

A. Kassahun, Shuai Hao, H. Marvin, 2020, PloS one.

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools.

G. Kleter, Y. Bouzembrak, Wenjuan Mu, 2024, Comprehensive reviews in food science and food safety.

Data driven food fraud vulnerabilities assessment using Bayesian Network: spices supply chain.

Y. Bouzembrak, L. Manning, N. Liu, 2024, Food Control.