A High-Throughput, Model-Free Marker Library Approach for Multivariate Adulteration Detection in Vegetable Oils: From Metabolomic Discovery to Regulatory Screening

Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, flaxseed, soybean, peanut, industrial hemp seed, and sunflower seed oils) using untargeted metabolomics via UHPLC-Q-TOF-MS. We identified 34 characteristic markers, including 9 confirmed by reference standards, such as hydroxytyrosol in olive oil, camelliasaponins in camellia oil, and sesamin in sesame oil, which are uniquely present in specific oils and absent in others. The method enables reliable qualitative screening of adulteration at levels as low as 5% without dependence on chemometric models. Validation using binary and multicomponent blends confirmed its robustness and specificity. In commercial sample analysis, adulteration was detected in 16.0% of olive oils (4/25) and 12.7% of camellia oils (7/55), with results consistent with regulatory findings. This work establishes the first integrated marker library for simultaneous screening of nine vegetable oils, offering a standardized, high-throughput tool for large-scale market surveillance that bridges the gap between discovery-based omics and routine regulatory practice.