D. J. Newman
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
D. J. Newman has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
J. H. Liu
North Dakota State University, Fargo
1 shared publicationJ. M. Young
North Dakota State University, Fargo
1 shared publicationJ.-H. Liu
Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
1 shared publicationJ.M. Young
Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
1 shared publicationL. A. Bachmeier
North Dakota State University, Fargo
1 shared publicationL.A. Bachmeier
Department of Animal Sciences, North Dakota State University, Fargo, ND 58102, USA
1 shared publication
Latest Publications
- 1 September 2018
- Published by Elsevier in Meat Science
- Vol. 143, 18-23
Predicting pork loin intramuscular fat using computer vision system
- 1 September 2018
- Published by Elsevier in Meat Science
- Vol. 143, 18-23
Predicting pork loin intramuscular fat using computer vision system
- 1 April 2016
- Published by Oxford University Press (OUP) in Journal of Animal Science
- Vol. 94 (suppl_2) , 67
143 Predicting pork color scores using machine vision and support vector machine technologies
The objective was to investigate the ability of image color features to predict subjective pork color scores (NPB, 2011). Enhanced retail pork center-cut loin chops in overwrapped packages were purchased and transported to North Dakota State University for analysis. Chops were assessed on the cross-sectional surface for subjective color score by one trained observer (score 2, n = 75; score 3, n = 284; score 4, n = 240; score 5, n = 86) and instrumental color (CIE L*, a*, and b*) using a Minolta Colorimeter (CR-410, 50 mm diameter orifice, 2° observer, C light source; Minolta Company, Ramsey, NJ). Images of pork loin samples were then acquired using a machine vision system which include a charge-coupled device (CCD) camera (MV-VS141FM/C, Micro-vision Ltd., China) with a 5mm C-mount lens (aperture of f/1.4 to 16C, H0514-MP2 1/2” fixed Lens, computer CBC Americas Corp, USA). After background and muscle segmentation from images, 18 image color features (mean and standard deviation of R, G, B, H, S, I, L*, a*, b*) were extracted from 3 different color spaces: RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), and L*a*b*. Color features were submitted to partial least squares (PLS) regression and support vector machine (SVM) regression analyses to establish prediction models for different color scores. A subsample (80%) of data were used to train the PLS and SVM models, which were then validated on the remaining 20% of the data. For color score 2, the accuracy of the PLS model was 86.7% classified correctly and the SVM model was 93.3% classified correctly. For color score 3, the PLS model predicted 81.5% correctly and the SVM model predicted 79.6% correctly. For color score 4, PLS model predicted 89.6% correctly and the SVM model predicted 91.7% correctly. For color score 5, the SVM model predicted the highest accuracy of 72.7% while the PLS model's prediction accuracy was 90.9%. Image color features isolated through the development of PLS and SVM models show potential as a means to predict pork color scores.
- 1 April 2016
- Published by Oxford University Press (OUP) in Journal of Animal Science
- Vol. 94 (suppl_2) , 67
143 Predicting pork color scores using machine vision and support vector machine technologies
The objective was to investigate the ability of image color features to predict subjective pork color scores (NPB, 2011). Enhanced retail pork center-cut loin chops in overwrapped packages were purchased and transported to North Dakota State University for analysis. Chops were assessed on the cross-sectional surface for subjective color score by one trained observer (score 2, n = 75; score 3, n = 284; score 4, n = 240; score 5, n = 86) and instrumental color (CIE L*, a*, and b*) using a Minolta Colorimeter (CR-410, 50 mm diameter orifice, 2° observer, C light source; Minolta Company, Ramsey, NJ). Images of pork loin samples were then acquired using a machine vision system which include a charge-coupled device (CCD) camera (MV-VS141FM/C, Micro-vision Ltd., China) with a 5mm C-mount lens (aperture of f/1.4 to 16C, H0514-MP2 1/2” fixed Lens, computer CBC Americas Corp, USA). After background and muscle segmentation from images, 18 image color features (mean and standard deviation of R, G, B, H, S, I, L*, a*, b*) were extracted from 3 different color spaces: RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), and L*a*b*. Color features were submitted to partial least squares (PLS) regression and support vector machine (SVM) regression analyses to establish prediction models for different color scores. A subsample (80%) of data were used to train the PLS and SVM models, which were then validated on the remaining 20% of the data. For color score 2, the accuracy of the PLS model was 86.7% classified correctly and the SVM model was 93.3% classified correctly. For color score 3, the PLS model predicted 81.5% correctly and the SVM model predicted 79.6% correctly. For color score 4, PLS model predicted 89.6% correctly and the SVM model predicted 91.7% correctly. For color score 5, the SVM model predicted the highest accuracy of 72.7% while the PLS model's prediction accuracy was 90.9%. Image color features isolated through the development of PLS and SVM models show potential as a means to predict pork color scores.