Laser Light Scattering-Enhanced Deep Computer Vision Method for the Detection of Trace Mineral Oil in Vegetable Oils

Mineral oil contamination in vegetable oils poses a serious threat to food safety and consumer health. In this study, we reported an on-site compatible analytical strategy based on laser light scattering-enhanced deep computer vision for detecting mineral oil contamination in vegetable oils. The strategy integrates saponification-induced phase and turbidity contrast with laser-enhanced scattering visualization to transform trace mineral oil into visually discriminative signals. To accurately analyze these signals, we proposed a novel, lightweight, and efficient deep learning model (Oil-MobileNet). After optimizing chemical reaction conditions, the effects of three illumination sources (green laser, red laser, and laser-free) on image acquisition were systematically examined. Subsequently, Oil-MobileNet was evaluated on binary and multiclass classification. Comparative analyses with four baseline models demonstrated that the combination of green laser illumination and Oil-MobileNet achieved the best classification performance, enabling reliable discrimination of mineral oil contamination down to 0.05% (v/v) under the defined operational criteria. This practical detection capability outperformed human visual inspection with green laser (0.1%) and commercially available saponification-based kits (0.9-3%). The contaminated level prediction models were also established using these architectures. In addition, interpretability studies were conducted to elucidate the model's decision-making mechanism. Finally, the well-trained models were deployed in a user-friendly graphical user interface for the accurate determination of mineral oil contamination.