Prediction of Extensibility and Toughness of Wheat-Flour Dough Using Bubble Inflation–Structured Light Scanning 3D Imaging Technology and the Enhanced 3D Vgg11 Model

作者：Xiuzhi Luo, Changhe Niu, Zhaoshuai Zhu, Yuxin Hou, Hong Jiang, Xiuying Tang · 发表于：Foods · 年份：2025 · DOI：10.3390/foods14081295 · 被引用次数：4 · 研究领域：Industrial Vision Systems and Defect Detection、Microbial Metabolism and Applications

The extensibility of dough and its resistance to extension (toughness) are important indicators, since they are directly linked to dough quality. Therefore, this paper used an independently developed device to blow sheeted dough, and then a three-dimensional (3D) camera was used to continuously collect point cloud images of sheeted dough forming bubbles. After data collection, the rotation algorithm, region of interest (ROI) extraction algorithm, and statistical filtering algorithm were used to process the original point cloud images. Lastly, the oriented bounding box (OBB) algorithm was proposed to calculate the deformation height of each data point. And the point cloud image with the largest deformation depth was selected as the data to input into the 3D convolutional neural network (CNN) models. The Convolutional Block Attention Module (CBAM) was introduced into the 3D Visual Geometry Group 11 (Vgg11) model to build the enhanced Vgg11. And we compared it with the other classical 3D CNN models (MobileNet, ResNet18, and Vgg11) by inputting the voxel-point-based data and the voxel-based data separately into these models. The results showed that the enhanced 3D Vgg11 model using voxel-point-based data was superior to the other models. For prediction of dough extensibility and toughness, the Rp was 0.893 and 0.878, respectively.