A review of Canadian and international food safety systems: Issues and recommendations for the future

Food Safety Governance and Guardianship: The Role of the Private Sector in Addressing the EU Ethylene Oxide Incident

Sesame seeds within the European Union (EU) are classified as foods not of animal origin. Two food safety issues associated with sesame seeds have emerged in recent years, i.e., Salmonella contamination and the presence of ethylene oxide. Fumigation with ethylene oxide to reduce Salmonella in seeds and spices is not approved in the EU, so its presence in sesame seeds from India was a sentinel incident sparking multiple trans-European product recalls between 2020-2021. Following an interpretivist approach, this study utilises academic and grey sources including data from the EU Rapid Alert System for Food and Feed (RASFF) database to inform a critical appraisal of current EU foods not of animal origin legislation and associated governance structures and surveillance programs. This is of particular importance as consumers are encouraged towards plant-based diets. This study shows the importance of collaborative governance utilizing data from company testing and audits as well as official regulatory controls to define the depth and breadth of a given incident in Europe. The development of reflexive governance supported by the newest technology (e.g., blockchain) might be of value in public-private models of food safety governance. This study contributes to the literature on the adoption of risk-based food safety regulation and the associated hybrid public-private models of food safety governance where both regulators and private organizations play a vital role in assuring public health.

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles

Rapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D ¹H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer ("deep") artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.