Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures

Kevin Lim (), Kun Pan, Zhe Yu and Rong Hui Xiao
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Kevin Lim: WIL@NUS Corporate Lab
Kun Pan: Yihai Kerry Arawana Oils, Grains & Food Co., Ltd
Zhe Yu: Yihai Kerry Arawana Oils, Grains & Food Co., Ltd
Rong Hui Xiao: Yihai Kerry Arawana Oils, Grains & Food Co., Ltd

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Previous studies have shown that each edible oil type has its own characteristic fatty acid profile; however, no method has yet been described allowing the identification of oil types simply based on this characteristic. Moreover, the fatty acid profile of a specific oil type can be mimicked by a mixture of 2 or more oil types. This has led to fraudulent oil adulteration and intentional mislabeling of edible oils threatening food safety and endangering public health. Here, we present a machine learning method to uncover fatty acid patterns discriminative for ten different plant oil types and their intra-variability. We also describe a supervised end-to-end learning method that can be generalized to oil composition of any given mixtures. Trained on a large number of simulated oil mixtures, independent test dataset validation demonstrates that the model has a 50th percentile absolute error between 1.4–1.8% and a 90th percentile error of 4–5.4% for any 3-way mixtures of the ten oil types. The deep learning model can also be further refined with on-line training. Because oil-producing plants have diverse geographical origins and hence slightly varying fatty acid profiles, an online-training method provides also a way to capture useful knowledge presently unavailable. Our method allows the ability to control product quality, determining the fair price of purchased oils and in-turn allowing health-conscious consumers the future of accurate labeling.

Date: 2020
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DOI: 10.1038/s41467-020-19137-6

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