Adulteration detection in cactus seed oil: Integrating analytical chemistry and machine learning approaches.

ABSTRACT: Economically motivated adulteration threatens both consumer rights and market integrity, particularly with high-value cold-pressed oils like cactus seed oil (CO). This study proposes a machine learning model that integrates analytical measurements, data simulations, and classification techniques to detect adulteration of CO with refined sunflower oil (SO) and determine the detectable limit of adulteration without measuring a huge number of different mixtures. First, pure CO and SO samples were analyzed for their fatty acid, triacylglycerol, and tocochromanol content using HPLC or GC. The resulting oil composition data served as the foundation for further simulations. Monte Carlo (MC) simulations outperformed Conditional Tabular Generative Adversarial Networks (CTGAN) in simulating realisti

SUBMITTER: El Harkaoui S 

PROVIDER: S-EPMC11821398 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature