PolyCheck. Using advanced data science to design a better protocol to detect and monitor listeria in the food preparation industry
PolyCheck. Using advanced data science to design a better protocol to detect and monitor listeria in the food preparation industry
批准号:
10009393
负责人:
金额:
$13.07万
依托单位:
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2021
资助国家:
英国
项目状态:
已结题
起止时间:
2021 至 --
中文摘要
李斯特菌是英国最致命的食源性疾病。它通常在食物收获、加工、制备、包装、运输或储存在受_L污染的环境中时传播。单核细胞增多症。这些环境可能受到原材料、水、土壤和进入的空气的污染。在过去的几十年里,对于从事食品制备和加工的公司来说,有效地检测和监控肠道菌群已经被证明是难以捉摸和困难的。FoodScan(PolyChord在食品安全方面的第一个项目)已经被证明能够比其他标准方法更好地监控肠球菌和沙门氏菌的生长/死亡。我们将利用我们在食品扫描项目中学到的知识,应用类似的方法来改善对单核细胞增生性李斯特菌的监测。在这一新方法中,PolyChord将与一家领先的食品制备公司和世界知名的生物学家Seamus Fanning教授密切合作,开发一种数据驱动的方法,用于检测和监测进入英国食品加工厂的产品中的单核细胞增生性李斯特菌。这项研究的成果将被称为PolyCheck,这是一种利用数据科学识别食品中李斯特菌的新方法,也是朝着建立更好的解决食品制备行业李斯特菌问题的协议迈出的重要一步。我们利用在FoodScan项目中学到的知识,应用我们先进的数据科学PolyChord,结合尖端的精密食品科学,开发出一种数据驱动的方法来检测和监测大肠杆菌。我们将与一家领先的食品制备公司和Seamus Fanning教授密切合作,将联合收割机环境数据与基因组测序相结合。结果将被称为PolyCheck,这是朝着建立更好的解决食品制备行业李斯特菌问题的协议迈出的实际一步。
英文摘要
Listeria is the deadliest food-borne disease in the UK. It is generally transmitted when food is harvested, processed, prepared, packed, transported or stored in environments contaminated with _L. monocytogenes_. These environments can be contaminated by raw materials, water, soil, and incoming air. Detecting and monitoring listeria effectively for companies engaged in food preparation and processing has proved elusive and difficult in the past decades.FoodScan (PolyChord's first project in food safety) has already proved capable of better monitoring the growth/death of enterococcus and salmonella than other standard methods. We will use what we have learnt in the FoodScan project to apply similar methodology to improving the monitoring of listeria monocytogenes. In this new approach, PolyChord will be closely collaborating with a leading food preparation company and Prof. Seamus Fanning - a world-renowned biologist - to develop a data driven approach for detecting and monitoring listeria monocytogenes in produce entering food processing factories in the UK. The outcoming result will be called PolyCheck, a new way of using data science to identify listeria in incoming foods and a major step towards establishing a better protocol for addressing Listeria in the food preparation industry.We use what we have learnt in the FoodScan project, applying our advanced data science, PolyChord, combined with cutting-edge of precision food science to develop a data driven approach to detecting and monitoring listeria. We will be working closely with a leading food preparation company and Prof. Seamus Fanning to combine environmental data with genomic sequencing. The outcoming result will be called PolyCheck, which is a practical step towards establishing a better protocol for addressing Listeria in the food preparation industry.
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国内基金
海外基金
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批准号:52073127
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项目类别:面上项目
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资助金额:58.0万元
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批准年份:2020
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负责人:Alidad Amirfazli
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依托单位:
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批准号:31070748
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项目类别:面上项目
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资助金额:34.0万元
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批准年份:2010
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负责人:Christine Nardini
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依托单位: