This is Scientific American's 60 Second Science.

I'm Christopher Intagiyata.

The Food and Drug Administration has to recall hundreds of foods every year,

like cookie snack packs with chunks of blue plastic hiding inside, or

salmonella tainted taco seasoning, or curry powder laced with lead.

It can take months before a recall is issued, but now researchers have come up with a method that

might fast track that process, leading to early detection and ultimately faster recalls.

The system relies on the fact that people increasingly buy foods and spices online and people

tend to write reviews of products they buy online which are like breadcrumbs to food

safety officials sniffing out dangerous products. The researchers linked FDA food recalls from 2012 to 2014 to

Amazon reviews of those same products. They then train machine learning algorithms to

differentiate between reviews for recalled items and

reviews for items that had not been flagged.

And the trained algorithms were able to predict FDA recalls

three quarters of the time.

They also identified another 20,000 reviews for possibly unsafe foods, most of which had never been recalled.

The results are in the Journal of the American Medical Informatics Association.

The World Health Organization estimates that 600 million people worldwide get sick annually

from contaminated food, and more than 400,000 people die from it.

So having tools that can enable us to detect this a lot

faster and hopefully investigate and do recall it faster would be useful not

just in the US but in other countries around the world as well.

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