What is it about?

Ever wonder what is sold to consumers as cooking oil down the aisle of Walmart? While it is common for blended oils to be sold, consumers are left to their own devices to figure out whether labels display real information or not. Here, a method is described that can be used as a universal standard to figure out the contents of edible oils. Using a large database of consumer oil products, machine learning is able to predict the composition of complex oil mixtures.

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Why is it important?

In a world where information is key to decision making, research work here can promote transparency and accurate labeling of goods sold in the market.


We published a paper that presents a deep learning method to uncover fatty acid patterns and detect fraudulent adulteration of edible oils

Kevin Lim

Read the Original

This page is a summary of: Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures, Nature Communications, October 2020, Springer Science + Business Media, DOI: 10.1038/s41467-020-19137-6.
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