A novel, non-destructive approach for real-time detection of starch gelatinization using YOLO-based deep learning models

Abstract

This research proposes a novel, non-destructive, vision-based approach for real-time detection and confirmation of starch gelatinization using the state-of-the-art YOLO (You Only Look Once) deep learning models to overcome the limitations of traditional methods, which are manual and subjective for industrial control. The primary contribution of this work is twofold: it introduces a novel image-based framework for starch gelatinization detection and, under controlled laboratory conditions, demonstrates the potential of YOLO models as an accurate tool for automated, non-contact process monitoring. A custom dataset was developed by capturing temporal images of a heated potato starch solution, with each frame annotated as “Non-Gelatinized” or “Gelatinized”. Four YOLO architectures (v8, v9, v11, v12) were trained and evaluated on the developed dataset. All models demonstrated exceptional and nearly identical performance, achieving maximum precision, recall, and F1-scores (1.0), alongside a high mean average precision (mAP@0.5 of 0.995). They also achieved perfect recall (100%) in localizing the reaction zone and converged to very low final losses (around 0.1). While all models excelled, YOLOv8 achieved the highest precision (99.97%) and fastest training time, whereas YOLOv12 showed superior initial learning stability over 20 epochs. The models were successfully validated on a continuous video stream, accurately identifying the gelatinization onset in real-time.