Big Data Analytics

Cornell, IBM Research collaborate to safeguard milk and — in the process — the world’s global food supply

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Milk. You probably have it in your fridge, but might not realize that the study of this single beverage can produce insights that could reduce foodborne illness and waste across the entire food system. Next-generation sequencing and big data analytics will identify new ways to eliminate hazards in the food supply chain — using milk as a model system — as Cornell and IBM Research team on an exciting collaboration to protect the global food supply.

Keeping food safe is one of the challenging tasks the world faces today. The Centers for Disease Control and Prevention (CDC) estimate that each year, roughly one in six Americans (or 48 million people) contracts a foodborne illness, leading to 128,000 hospitalizations, and 3,000 deaths.  In addition to the negative impact that foodborne illness has on human health and well-being, these numbers also are a distressing indicator of lost productivity and economic potential, translating to billions of dollars lost each year.

Because of the homogeneity of milk and its use as ingredients in many other products, it provides a great model for strategies other sectors of the food industry can apply to further improve food safety and quality.

Here at Cornell, we have a legacy of expertise in the dairy industry through a long collaboration with the local New York dairy industry, which is one of the top dairy producers in the nation. Now, we have the unique opportunity to take something that we know a lot about – dairy — and use it as a model for learning more about how to monitor the broader food supply chain using novel genomics technologies.

While a Ph.D. candidate at Cornell, I spent a summer interning with the Industrial and Applied Genomics Group in IBM’s Accelerated Discovery Lab. I had the opportunity to work alongside scientists and engineers from a diverse array of backgrounds who were all committed to tackling food safety, quality, and sustainability issues. We worked as a part of the Consortium for Sequencing the Food Supply Chain, which examines the global food chain — from farms, transport, processing facilities and distribution channels to restaurants and grocery stories — and applies genomics and analytics techniques to mitigate food borne illness and other risks in food management.

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An illustration of the metagenomics techniques Cornell and IBM Research will use to sequence all DNA present in sample of milk.

Now back at Cornell to complete my degree, I am very excited that Cornell is the first academic institution to join the Consortium for Sequencing the Food Supply Chain. Cornell is collaborating with IBM Research to minimize the chance that a food hazard will reach the final consumer, while also preventing food fraud and reducing food spoilage. By employing metagenomics techniques that involve sequencing all DNA present in a food or environmental sample such as milk, we hope to characterize baseline microbial communities from farm to fork and detect safety and quality anomalies. In this case, we’ll be using milk as a model food matrix.

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The Cornell Teaching Dairy Barn

Cornell has a broad team that has expertise in dairy from farm to finished product and earlier this year was recognized with the inaugural International Dairy Foods Association (IDFA) Food Safety Leadership award. In addition to state-of-the-art lab facilities in a brand new food science research building at Cornell and many industry collaborations, we have access to Cornell’s Teaching Dairy Barn as well as the Cornell Dairy Processing Plant. Through these resources, we can carefully control factors of the milk samples that will undergo metagenomics sequencing and thus be able to better assess metagenomics sequencing as a food safety tool. This research, powered by milk, has the potential to dramatically change how we approach the issues of foodborne illness, food spoilage, and food waste across the industry.

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