Camellia oil is a high-value edible seed oil, recommended by the Food and Agriculture Organization (FAO). It is essential to develop accurate and rapid analytical methods to authenticate camellia oil due to its susceptibility to adulteration. Recently, hyphenated chromatography-mass spectrometry, especially high-resolution mass spectrometry using chemometrics, has become a promising platform for the identification of camellia oil. Based on the compositional analysis, the fatty acid, sterol, phenol, and tocopherol profiles (or fingerprints) were utilized as predictor variables for assessing authenticity. The review systematically summarizes the workflow of chromatography-mass spectrometry technologies and comprehensively investigates recent metabolomic applications combined with chemometrics for camellia oil authentication. Metabolomics has significantly improved our understanding of camellia oil composition at the molecular level, contributing to its identification and full characterization. Hence, its integration with standard analytical methods is essential to enhance the tools available for public and private laboratories to assess camellia oil authenticity. Integrating metabolomics with artificial intelligence is expected to accelerate drug discovery by identifying new metabolic pathways and biomarkers, promising to revolutionize medicine.
Keywords: Authenticity; hyphenated chromatography-mass spectrometry; machine learning; metabolome; multivariate modeling.
Hyphenated chromatography-MS represents a precise method for camellia oil characterizationMetabolomics-based HRMS enhances camellia oil identification at the molecular levelMachine learning shows promise in ensuring camellia oil authenticityFatty acids, phenols, and sterols are metabolic markers for camellia oil authenticationMultivariate modeling detects camellia oil fraud through diverse data analysis.