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

Food Science and Technology

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

K. Lim, K. Pan, et al.

Explore how machine learning can revolutionize the identification of plant oils and their mixtures using fatty acid profiles. This groundbreaking research by Kevin Lim, Kun Pan, Zhe Yu, and Rong Hui Xiao showcases a method that achieves impressive accuracy in detecting oil types, ensuring continuous advancement in oil profiling.

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Playback language: English
Abstract
This paper presents a machine learning method for identifying ten different plant oil types and their mixtures based on their fatty acid profiles. The method uses an unsupervised model to identify sub-clusters within oil types, followed by a supervised deep learning model trained on simulated oil mixtures. The model achieves a 50th percentile absolute error of 1.4–1.8% and a 90th percentile error of 4–5.4% for three-way mixtures. An online-training method allows for continuous improvement and adaptation to new oil profiles.
Publisher
Nature Communications
Published On
Oct 23, 2020
Authors
Kevin Lim, Kun Pan, Zhe Yu, Rong Hui Xiao
Tags
machine learning
plant oils
fatty acid profiles
supervised learning
unsupervised model
oil mixtures
deep learning

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