Development of experimental protocols and traceable spectral for rapid identification of foodborne pathogens by infrared imaging spectroscopy

麦吉尔大学食品科学和农业化学系和瓦里安加拿大公司的研究人员已经建立了合作伙伴关系，以开发快速、自动识别食源性细菌的新技术。食源性疾病仍然非常常见，根据加拿大公共卫生局的数据，每年有超过40%的加拿大人口受到影响。从公共卫生的角度来看，这些疾病不仅可能构成严重的健康风险，主要是对年幼、体弱和年长的人，而且还构成了医疗保健系统的巨大负担，占用了越来越稀缺的资源，否则这些资源可以用于其他健康问题。在经济方面，食源性疾病是导致生产力下降的主要原因，在加拿大，相关成本加上医疗费用估计达到每年13亿美元。各种细菌导致了很大比例的食源性疾病，而快速、经济有效的方法来识别这些细菌对公共卫生机构和食品工业都很重要。在这个CRD项目中，将开发一种新的基于红外成像光谱对细菌进行指纹识别的技术。将建立一个数据库或“光谱库”，其中包含大约200种常见食源性细菌的红外光谱指纹，并将开发数据分析算法，以便通过将其光谱指纹与数据库中的细菌进行匹配来识别从食品中分离出来的细菌。此外，还将建立标准化的实验方案，并实现红外图像采集、处理和解释的自动化，以便这项技术可以在微生物分析实验室中常规使用。该集成系统将由加拿大卫生部地区(魁北克)实验室的研究人员进行评估。这一为期三年的项目的成功完成，将为食品安全负责人提供一种快速鉴定食源性细菌的新的强大工具。

A partnership between researchers in the Department of Food Science and Agricultural Chemistry of McGill University and Varian Canada Inc. has been formed to develop new technology for the rapid, automated identification of foodborne bacteria. Foodborne illnesses remain very common occurrences, with more than 40% of the Canadian population being afflicted each year according to the Public Health Agency of Canada. From a public health perspective, such illnesses not only can constitute a serious health risk, primarily for the very young, the infirm, and the elderly, but also represent a substantial burden on the healthcare system, taking up increasingly scarce resources that could otherwise be applied toward other health concerns. In economic terms, foodborne illnesses are a leading cause of lost productivity, and the associated costs combined with medical expenses have been estimated to reach $1.3 billion/yr in Canada. Various species of bacteria are responsible for a large proportion of foodborne illnesses, and the availability of rapid, cost-effective methods to identify these species is important to both public health agencies and the food industry. In this CRD project, a new identification technology based on the use of infrared imaging spectroscopy to "fingerprint" bacteria will be developed. A database or "spectral library" containing the infrared spectral fingerprints of approximately 200 strains of common foodborne bacteria will be created, and data analysis algorithms will be developed to allow for identification of bacteria isolated from food by matching of their spectral fingerprints against those in the database. In addition, a standardized experimental protocol will be established, and infrared image acquisition, processing, and interpretation will be automated so that this technique can be routinely employed in microbiological analysis laboratories. The integrated system will be evaluated by researchers at Health Canada's regional (Quebec) laboratory. The successful completion of this three-year project will provide those responsible for food safety with a new and powerful tool for the rapid identification of foodborne bacteria.