Luo Xiuzhi, Niu Changhe, Zhu Zhaoshuai, Hou Yuxin, Jiang Hong, Tang Xiuying
College of Mechanical Engineering, Xinjiang University, Urumqi 830046, China.
Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, No. 291, Nanchang South Road, Shayibak District, Urumqi 830046, China.
Foods. 2025 Apr 8;14(8):1295. doi: 10.3390/foods14081295.
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 was 0.893 and 0.878, respectively.
面团的延展性及其抗拉伸性（韧性）是重要指标，因为它们与面团质量直接相关。因此，本文使用自主研发的设备对面团薄片进行吹气，然后使用三维（3D）相机连续收集面团薄片形成气泡的点云图像。数据采集后，采用旋转算法、感兴趣区域（ROI）提取算法和统计滤波算法对原始点云图像进行处理。最后，提出了定向包围盒（OBB）算法来计算每个数据点的变形高度。并选择变形深度最大的点云图像作为数据输入到三维卷积神经网络（CNN）模型中。将卷积块注意力模块（CBAM）引入三维视觉几何组11（Vgg11）模型中构建增强型Vgg11。通过分别将基于体素点的数据和基于体素的数据输入这些模型，将其与其他经典的三维CNN模型（MobileNet、ResNet18和Vgg11）进行比较。结果表明，使用基于体素点数据的增强型三维Vgg11模型优于其他模型。对面团延展性和韧性的预测，准确率分别为0.893和0.878。
