Friday, January 30, 2015

IBM wants to protect our food by sequencing its supply chains



For example, he explained, although many places along the food supply chain know to test for salmonella, they might not know to test for, or have any reason to expect, contamination by other substances. Those could be anything from other, foreign bacteria to chemicals such as the melamine found in Chinese milk and infant formula in 2008. Understanding the metagenomics of the product being sold and the factories producing it means companies and regulatory agencies might be able to spot a problem in the microbial ecosystem and then get to work determining what’s causing it.
According to the Centers for Disease Control, foodborne illnesses sicken one in six Americans each year and kill about 3,000 people in the United States. A 2012 study published in the Journal of Food Prediction estimates the annual economic impact of of foodborne illness at nearly $78 billion.
Analytics Process_blue backgroundIBM

Right now, Wesler predicts it will be about three to five years before the results of this research might be deployed commercially. The companies will spend the first couple years getting to know the baselines microbiomes of various areas and things, understanding what they’re composed of and how they react to changes in environments or to other stuff. Initial research will focus on Mars facilities, which span a range of products including candy, pet food, packaged food and coffee.
Hopefully, they’ll be able to build up a database of connections between bacteria, chemicals, heavy metals and other substances, and their reactions in the presence of each other. After that, Wesler thinks they’ll be able to begin working on a set of tests that makes sense for particular industries and that can be implemented in a reasonably easy manner.
Wesler calls this the “quintessential big data problem” because it involves analyzing so much data and is only really possible to solve now because of advances in the required technology. In this case, that’s not just cheap data storage and new data-analysis tools, but also better genetic-sequencing technologies. “One of the reasons we even think this is feasible is because of the rise of next-generation sequencing,” he said.
A major uptick in the capabilities of any piece of the chain could speed up the research, he said, but the current state of the art should be capable to work within the predicted time frame.

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