Nondestructive determination of common indicators of beef for freshness assessment using airflow-three dimensional (3D) machine vision technique and machine learning

Assessment of beef freshness is critical to ensure a safe and sustainable beef supply chain. This work aims to investigate the feasibility of the viscoelasticity of beef to evaluate its freshness using the airflow-3D machine vision technique and machine learning models. The 3D camera was continuousl...

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Veröffentlicht in:Journal of food engineering Jg. 340; S. 111305
Hauptverfasser: Luo, Xiuzhi, Sun, Qinming, Yang, Tianxi, He, Ke, Tang, Xiuying
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2023
Schlagworte:
air flow
Airflow-3D machine vision
algorithms
Beef
cameras
computer vision
deformation
Four-element viscoelastic models
freshness
pH value
Point cloud image processing algorithm
prediction
supply chain
total volatile basic nitrogen
TVB-N content
viscoelasticity
Airflow-3D machine vision
pH value
TVB-N content
Four-element viscoelastic models
Beef
Point cloud image processing algorithm
ISSN:0260-8774, 1873-5770
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Zusammenfassung:Assessment of beef freshness is critical to ensure a safe and sustainable beef supply chain. This work aims to investigate the feasibility of the viscoelasticity of beef to evaluate its freshness using the airflow-3D machine vision technique and machine learning models. The 3D camera was continuously used to acquire deformation images under the action of the airflow. The obtained images were preprocessed using region of interest (ROI) segmentation, filtering denoising, and down-sampling. And then the depth and volume of processed images were obtained by the Oriented Bounding Box (OBB) algorithm and volume algorithms, separately. Two four-element viscoelastic models were established to fit depth and volume respectively for obtaining viscoelastic characteristics. Finally, regression models were built and compared using viscoelastic characteristics to determine the optimum prediction models and methods for two freshness indicators. The backpropagation neural network (BPNN) and support vector machine regression (SVR) based on selected features were the best prediction models for pH and total volatile basic nitrogen (TVB-N) content evaluation in beef, and the correlation coefficients (Rc and Rp) of the calibration set and prediction set were 0.7636, 0.9036, and 0.7669, 0.8388, respectively. •A test electronic instrument based on airflow-3D machine vision was independently developed.•The depth and volume of spatial deformation were quantified by algorithms of point cloud image quantization.•Two four-element viscoelastic models of the creep recovery phase were established to extract viscoelastic characteristics.•Machine learning models for simultaneously predicting two common freshness indicators were built.•The prediction effects of different viscoelastic feature fusions and different models were compared.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0260-8774
1873-5770
DOI:10.1016/j.jfoodeng.2022.111305