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

文献类型： 外文期刊

第一作者： Luo, Xiuzhi

作者： Luo, Xiuzhi;Jiang, Hong;Niu, Changhe;Zhu, Zhaoshuai;Zhu, Zhaoshuai;Hou, Yuxin;Tang, Xiuying

作者机构：

关键词： 3D CNN; dough extensibility and toughness; bubble inflation; 3D scanning imaging technology; CBAM; point cloud images

期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）

ISSN：

年卷期： 2025 年 14 卷 8 期

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收录情况： SCI

摘要： 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.

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