Exploring deep learning potential to authenticate vegetable oils and detect fraud using a micro-spectroscopic technique and chemometrics

) of 0.97, indicating an excellent agreement between the reference and predicted values. The findings demonstrate that portable NIR microspectroscopy combined with advanced chemometric and machine learning models offers a rapid, non-destructive, and reliable approach for both the classification and quantification of vegetable oil fraud. This approach shows strong potential for routine screening and on-site authenticity assessment of edible oils, particularly in resource-limited and decentralised food control environments.