Food Safety Tech reports that more than 350 workers (out of 2600 workers) at the Foster Farms poultry processing facility located in Livingston, California, have tested positive for the novel coronavirus. The outbreak has resulted in the death of eight employees. Because of increasing deaths and uncontrolled COVID-19 cases, the decision was made to order the Livingston Plant to close until acceptable safety measures are in place. The plant will close until September 7. During the closure, the facility will be deep cleaned, and employees will engage in a new round of testing. Merced County states that an employee cannot return to work until two negative COVID-19 test results within seven days. @ https://foodsafetytech.com/news_article/uncontrolled-covid-19-cases-foster-farms-temporarily-shuts-down-plant-following-eight-worker-deaths/
ruth
ruth
The Canadian Food Inspection Agency (CFIA) announced that Les Jardins Picoudi recalls Picoudi brand microgreens from the marketplace due to possible Salmonella contamination. The products were shipped to Quebec and New Brunswick. Inspectors from the CFIA and personnel from the Quebec agriculture department (MAPAQ) trigger this recall and are investigating the situation in search of the source of the contamination. No illnesses have been reported to date in connection with this recall. The recalled products include Organic Broccoli Microgreens in 35-gram size with UPC number 8 13526 00001 6 and 75-gram packages with UPC number 8 13526 00011 5. Also recalled is Organic Arugula Microgreens in 35-gram packages with UPC number 8 13526 00006 1, and in 75-gram packages with UPC number 8 13526 00016 0. Finally, Organic Coriander Microgreens is recalled, sold in 35-gram packages with UPC number 8 13526 00005 4, and in 75-gram packages with UPC number 8 13526 00015 3.@ https://www.inspection.gc.ca/food-recall-warnings-and-allergy-alerts/2020-08-28/eng/1598664773844/1598664780346
Les Jardins Picoudi is recalling Picoudi brand microgreens from the marketplace due to possible Salmonella contamination.
ruth
Dr. Stephen M. Hahn, Commissioner of Food and Drugs, wrote bout the agency initiative to leverage the use of artificial intelligence (AI) as part of the FDA’s New Era of Smarter Food Safety. The ultimate goal is to see if AI can improve the FDA’s ability to quickly and efficiently identify products that may pose a threat to public health. The FDA launched a pilot program in the spring of 2019 to learn the added benefits of using AI, specifically machine learning (ML), in our import-screening processes. The FDA decided to test the new approach on imported seafood to assess the utility of using AI/ML to better target seafood at the border that may be unsafe. The proof of concept resulted in exciting results, suggesting that this approach has real potential to be a tool that expedites the clearance of lower risk seafood shipments and identifies those that are higher risk. The proof of concept demonstrated that AI/ML could almost triple the likelihood of identifying a shipment containing products of public health concern. Technology Modernization Action Plan (TMAP) provides a foundation for the development of the FDA’s ongoing strategy. The pilot also allows learning how to utilize the knowledge needed from the vast volume of data. One of the primary goals of our pilot is to assess the ability of AI/ML to more quickly, efficiently, and comprehensively take advantage of all the data and information residing in our systems. @ https://www.fda.gov/news-events/fda-voices/import-screening-pilot-unleashes-power-data-and-leverages-artificial-intelligence
FDA is leveraging our use of artificial intelligence as part of the FDA’s New Era of Smarter Food Safety initiative.
ruth
Scientists at the University of Aberdeen in Scotland (Departments of Physics and Biological Sciences) have developed a method that could help to identify the source of food poisoning. They developed a minimal multilocus distance (MMD) method which rapidly deals with large data sets such as whole genome sequence (WGS) well as methods for optimally selecting loci. The MMD method can be used to train the computer to identify likely sources of origin of a Campylobacter infection with high speed and accuracy. The methods are generic, easy to implement for WGS and proteomic data and have wide application. It is wise to employ a number of methods on each dataset to decide which set of loci are optimal. The performance of different locus selection strategies can be tested relatively fast with the MMD method. @ https://www.nature.com/articles/s41598-020-68740-6?proof=t
Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.