College of Engineering, China Agricultural university, NO 17 Qinghua East Road, Beijing 100083, PR China.
College of Engineering, China Agricultural university, NO 17 Qinghua East Road, Beijing 100083, PR China.
Meat Sci. 2023 Aug;202:109206. doi: 10.1016/j.meatsci.2023.109206. Epub 2023 Apr 27.
The main factor affecting beef quality, consumer satisfaction, and purchase decisions is beef tenderness. In this study, a rapid nondestructive testing method for beef tenderness based on airflow pressure combined with structural light 3D vision technology was proposed. The structural light 3D camera was used to scan the 3D point cloud deformation information of the beef surface after the airflow acted on it for 1.8 s. Six deformation characteristics and three point cloud characteristics of the beef surface depression region were obtained by using denoising, point cloud rotation, point cloud segmentation, point cloud descending sampling, alphaShape, and other algorithms. A total of nine characteristics were mainly concentrated in the first five principal components (PCs). Therefore, the first five PCs were put into three different models. The results showed that the Extreme Learning Machine (ELM) model had a comparatively higher prediction effect for the prediction of beef shear force, with a root mean square error of prediction (RMSEP) of 11.1389 and a correlation coefficient (R) of 0.8356. In addition, the correct classification accuracy of the ELM model for tender beef achieved 92.96%. The overall classification accuracy reached 93.33%. Consequently, the proposed methods and technology can be applied for beef tenderness detection.
影响牛肉品质、消费者满意度和购买决策的主要因素是牛肉的嫩度。在这项研究中，提出了一种基于气流压力结合结构光 3D 视觉技术的牛肉嫩度快速无损检测方法。结构光 3D 相机用于扫描气流作用 1.8 秒后牛肉表面的 3D 点云变形信息。通过去噪、点云旋转、点云分割、点云降采样、alphaShape 等算法，获得了牛肉表面凹陷区域的六个变形特征和三个点云特征。共有九个特征主要集中在前五个主成分（PC）中。因此，将前五个 PC 放入三个不同的模型中。结果表明，极端学习机（ELM）模型对牛肉剪切力的预测具有较高的预测效果，预测的均方根误差（RMSEP）为 11.1389，相关系数（R）为 0.8356。此外，ELM 模型对嫩牛肉的正确分类准确率达到 92.96%。整体分类准确率达到 93.33%。因此，所提出的方法和技术可用于牛肉嫩度检测。
