Determination of beef tenderness based on airflow pressure combined with structural light three-dimensional (3D) vision technology

Abstract

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.