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

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 camer...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Meat science Ročník 202; s. 109206
Hlavní autori: Luo, Xiuzhi, Xiong, Lijian, Gao, Xin, Hou, Yuxin, He, Meng, Tang, Xiuying
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.08.2023
Predmet:
3D vision technology
air flow
Airflow pressure
Animals
beef
beef quality
Beef tenderness
cameras
Cattle
computer vision
Consumer Behavior
consumer satisfaction
data collection
deformation
Food Technology - methods
Meat
Muscle, Skeletal - chemistry
Point cloud data
prediction
Technology
vision
Warner-Bratzler shear force
3D vision technology
Beef tenderness
Point cloud data
Airflow pressure
Warner-Bratzler shear force
ISSN:0309-1740, 1873-4138, 1873-4138
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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. •A beef tenderness detection device was developed independently.•A series of point cloud image processing algorithms were used to segment the space deformation region.•Six phenotypic characteristics and three point cloud characteristics of beef spatial deformation were obtained and analyzed.•Three different prediction and classification models were established and compared.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0309-1740
1873-4138
1873-4138
DOI:10.1016/j.meatsci.2023.109206