Metabolomic-guided comprehensive food analysis and smart food traceability

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Food fraud is conservatively estimated to cause $65B USD in damage to the global food industry every year. Food fraud not only causes economic damage, but also impairs consumers' trust in government and the food industry, potentially hides harmful ingredients in the products, and prevents consumers from making wise decisions based on their nutritional needs as well as religious and other beliefs (e.g., vegetarian). This issue has become more serious in recent years due to the increasingly complex global food supply chain. To address this issue, our group will develop new and effective analytical methods and integrate them with a global information sharing system to detect fraudulent products and share the results to all stakeholders (i.e., consumers, food industry and government) in a timely manner. The long-term research objective of my program is to push the advancement of analytical techniques for the detection of fraudulent foods and integrate the advanced analytical methods with information technologies including the Internet of Things and artificial intelligence. This will allow instant results sharing along the food value chain and best protects all stakeholders from food fraud. For this 5-year research plan, I will use metabolomics to identify novel food markers that can achieve reliable food authentication. Targeting these novel food markers, I will develop two rapid analytical methods for food authentication suitable for different application scenarios. The first method is a cheap and disposable paper-based microfluidic sensor that can be applied in the field to screen food authenticity, independent of laboratory-scale instrumentation. The second method is a one-step, semi-quantitative analysis based on a laboratory scale instrument (i.e., ambient ionization source-mass spectrometry). To achieve instant results sharing, a web-based user interface will be developed to process the data collected by the two analytical methods, interpret the results, and share the results with stakeholders in an automated and instant manner. Plant-based milk alternatives will be used as a model food commodity for these proof-of-concept research activities. The outcomes of these proposed research activities include a comprehensive database for chemical profiles of foods that are useful for the development of novel analytical methods for food authenticity, as well as food safety and quality. The two rapid analytical methods that will be developed will provide inspection agencies, the food industry and consumers with more powerful tools to investigate food fraud issues. Moreover, the web-based user interface will lay a foundation for a successful Internet of Things-based food traceability platform that will be developed over the long term in my research program which will provide a comprehensive platform to address food fraud. These outcomes will protect Canadian consumers, the food industry as well as governments from the impact of food fraud.