ArticleCurrent research in food science2024
Rapid quantitative authentication and analysis of camellia oil adulterated with edible oils by electronic nose and FTIR spectroscopy.
Article in Current research in food science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI-Driven Food Fraud Detection Systems: A Critical Systematic Review of the Detection-Prevention Gap.Foods (Basel, Switzerland) · 2026Review
- Non-targeted authentication of camellia oil using gas chromatography-ion mobility spectrometry and one-class chemometrics.Analytical and bioanalytical chemistry · 2026Article
- Rapid and Accurate Quantification Detection of BHT in Edible Oils Using Raman Spectroscopy Combined with Chemometric Models.Foods (Basel, Switzerland) · 2026Article
- A Comprehensive Review on Minimally Destructive Quality and Safety Assessment of Agri-Food Products: Chemometrics-Coupled Mid-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2025Review
- Identification of Camellia Oil Adulteration With Excitation-Emission Matrix Fluorescence Spectra and Deep Learning.Journal of fluorescence · 2025Article
- Rapid Classification and Quantitative Prediction of Aflatoxin BMolecules (Basel, Switzerland) · 2025Article
- Influence of Exogenous Melatonin on the Physiological Traits ofPlants (Basel, Switzerland) · 2025Article
- Influence of electrohydrodynamics on the drying characteristics, microstructure and volatile composition of apricot abalone mushroom (Current research in food science · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
Camellia oil, recognized as a high-quality edible oil endorsed by the Food and Agriculture Organization, is confronted with authenticity issues arising from fraudulent adulteration practices. These practices not only pose health risks but also lead to economic losses. This study proposes a novel machine learning framework, referred to as a transformer encoder backbone with a support vector machine regressor (TES), coupled with an electronic nose (E-nose), for detecting varying adulteration levels in camellia oil. Experimental results indicate that the proposed TES model exhibits the best performance in identifying the adulterated concentration of camellia oi, compared with five other machine learning models (the support vector machine, random forest, XGBoost, K-nearest neighbors, and backpropagation neural network). The results obtained by E-nose detection are verified by complementary Fourier transform infrared (FTIR) spectroscopy analysis for identifying functional groups, ensuring accuracy and providing a comprehensive assessment of the types of adulterants. The proposed TES model combined with E-nose offers a rapid, effective, and practical tool for detecting camellia oil adulteration. This technique not only safeguards consumer health and economic interests but also promotes the application of E-nose in market supervision.
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