IBM and Cornell University to work together to keep global milk supply safe

By Mary Ellen Shoup

- Last updated on GMT

Cornell and IBM will create tools that detect anomalies in raw milk that present food safety hazards and possible fraud. Pic: Cornell University
Cornell and IBM will create tools that detect anomalies in raw milk that present food safety hazards and possible fraud. Pic: Cornell University

Related tags Food supply chain Milk

IBM Research and Cornell University have teamed up to help improve the safety of the global milk supply through genetic sequencing and big data analytics research.

Cornell University has also become the newest member of the Consortium for Sequencing the Food Supply Chain food safety initiative. The consortium is conducting a large-scale metagenomics study to categorize and understand microorganisms and the factors that influence their activity in various food matrices.

Bio-Rad Laboratories and Mars make up the consortium​ which was created in 2015.

Improving food safety and preventing fraud

The goal of the collaboration between IBM and Cornell University is to help minimize the chance that a food hazard will reach consumers, and provide a tool to assist against food fraud in the global dairy industry.

The USDA estimated that Americans consume more than 600 pounds of milk and milk-based products per person per year and dairy products topped the list of the most food safety recalls last year.

The research partnership will leverage artificial intelligence and machine learning, to gain new insights into how microorganisms interact within a particular environment, Jeff Welser, vice president and director at IBM Research – Almaden, said.

While many food producers already have rigorous processes in place to ensure food safety hazards are managed appropriately, this pioneering application of genomics will be designed to enable a deeper understanding and characterization of microorganisms on a much larger scale than has previously been possible.

Spotting anomalies in raw milk

Raw milk is the main ingredient in consumer dairy products, but samples are usually tested for a limited range of bacteria. The research project seeks to detect previously unknown anomalies that may present a safety risk to the dairy supply chain.

Characterizing what is “normal” ​for a food ingredient can help spot when something goes awry much earlier in the process and prevent food safety hazards.

The Consortium for Sequencing the Food Supply Chain is expanding this range of testing and detecting of bacteria using the community of microbes known as the microbiome to characterize the food samples at a much higher resolution.

The research process

The research project will collect genetic data from the microbiome of raw milk samples in a “real-world” ​scenario at Cornell’s dairy processing plant and farm in Ithaca, New York.

The facility encompasses the full dairy supply chain – from farm to processing to consumer. This initial data collection will form a raw milk baseline and be used to further expand existing consortium bioinformatic analytical tools. 


By sequencing and analyzing the DNA and RNA of food microbiomes, researchers plan to create new tools that can help monitor raw milk to detect anomalies that represent food safety hazards and possible fraud.

The collaboration will help “develop new ways to help keep our food supply safe before fraud or contamination hits by developing advanced algorithms, applying machine learning and mathematical modeling to sequence data,” ​Kristen Beck, technical lead researcher for the Consortium for Sequencing the Food Supply Chain, IBM Research – Almaden said.

“Safe food is the first step toward human health.”

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