Application of 3D morphology and colour analysis for the assessment of acrylamide and hydroxymethylfurfural content in multi-grain biscuits

Thermal processing of foods high in carbohydrates presents safety concerns due to the formation of acrylamide and hydroxymethylfurfural (HMF). Traditional computer vision systems, which rely on two-dimensional (2D) colour proxies, lose accuracy in complex multi-grain matrices where surface browning and chemical changes often do not align. This research assessed a new multimodal framework that combines standard colour analysis with morphological descriptors obtained through monocular depth estimation using the "Depth Anything V2 ″ algorithm. Biscuits made from six different flours (refined wheat, whole wheat, rye, oat, spelt, and buckwheat) were baked under various conditions. Levels of acrylamide and HMF were measured using LC-ESI-MS/MS and LC-DAD. A total of 35 features, including chromatic indices, 2D geometry, and three-dimensional topographical characteristics like quadratic roughness and mean curvature, were extracted from digital elevation models. Random Forest models significantly outperformed linear models, with global multimodal Random Forest models achieving a MAPE of 13.5% (R 2 =0.93) for HMF and a MAPE of 7.7% (R 2 =0.86) for acrylamide. Morphological descriptors proved to be crucial physical indicators of thermal stress in the crust model, capturing structural changes that traditional colourimetry cannot detect. This approach offers a non-destructive and cost-effective method for industrial compliance with Regulation (EU) 2017/2158 using affordable RGB imaging sensors.