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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| Published in: | Journal of food engineering Vol. 340; p. 111305 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.03.2023
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| ISSN: | 0260-8774, 1873-5770 |
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| Abstract | 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. |
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| AbstractList | 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. 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 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. |
| ArticleNumber | 111305 |
| Author | Yang, Tianxi Tang, Xiuying He, Ke Luo, Xiuzhi Sun, Qinming |
| Author_xml | – sequence: 1 givenname: Xiuzhi surname: Luo fullname: Luo, Xiuzhi email: hermione.x.luo@cau.edu.cn organization: College of Engineering, China Agricultural University, NO 17 Qinghua East Road, Beijing 100083, PR China – sequence: 2 givenname: Qinming surname: Sun fullname: Sun, Qinming email: 1179909120@qq.com organization: College of Engineering, China Agricultural University, NO 17 Qinghua East Road, Beijing 100083, PR China – sequence: 3 givenname: Tianxi orcidid: 0000-0003-4197-9262 surname: Yang fullname: Yang, Tianxi email: tianxi.yang@ubc.ca organization: Food Nutrition and Health Program, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada – sequence: 4 givenname: Ke surname: He fullname: He, Ke email: 1433104752@qq.com organization: College of Engineering, China Agricultural University, NO 17 Qinghua East Road, Beijing 100083, PR China – sequence: 5 givenname: Xiuying orcidid: 0000-0002-8045-9780 surname: Tang fullname: Tang, Xiuying email: txying@cau.edu.cn organization: College of Engineering, China Agricultural University, NO 17 Qinghua East Road, Beijing 100083, PR China |
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| Keywords | Airflow-3D machine vision pH value TVB-N content Four-element viscoelastic models Beef Point cloud image processing algorithm |
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| SubjectTerms | 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 |
| Title | Nondestructive determination of common indicators of beef for freshness assessment using airflow-three dimensional (3D) machine vision technique and machine learning |
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