ArticleNature communications2020
Pattern recognition based on machine learning identifies oil adulteration and edible oil mixtures.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Artificial intelligence in food safety.NPJ science of food · 2026Review
- Rapid Determination of Palmitic Acid Content in Edible Oils Using Vis-NIR Reflectance Spectroscopy and Deep Learning Models.Foods (Basel, Switzerland) · 2026Article
- Phytosterol Profiling as a Tool for Edible Oil Authentication: Challenges and Prospects.Foods (Basel, Switzerland) · 2026Review
- Natural diversity in fatty acid composition and nutritional quality of African oil palm germplasm reveals opportunities for nutritional trait breeding.Frontiers in plant science · 2026Article
- Ratio Quantification of Geranium and Rose Essential Oil Mixtures via Deep Learning Analysis of Complex Raman Spectra.ACS measurement science au · 2025Article
- The Journey of Artificial Intelligence in Food Authentication: From Label Attribute to Fraud Detection.Foods (Basel, Switzerland) · 2025Review
- Effects of Consuming Repeatedly Heated Edible Oils on Cardiovascular Diseases: A Narrative Review.Current medicinal chemistry · 2024Review
- Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market.Foods (Basel, Switzerland) · 2023Article
- Non-targeted detection of food adulteration using an ensemble machine-learning model.Scientific reports · 2022Article
- Article
- The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration.Food chemistry · 2022Article
- Rapid Iodine Value Estimation Using a Handheld Raman Spectrometer for On-Site, Reagent-Free Authentication of Edible Oils.ACS omega · 2022Article
- End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses.Plant phenomics (Washington, D.C.) · 2022Article
- Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints.Foods (Basel, Switzerland) · 2021Article
- Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration.Biosensors · 2021Article
- Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers.PeerJ. Computer science · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 50
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.