Assessment of egg freshness by prediction of Haugh unit and albumen pH using an artificial neural network
Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were coll...
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| Published in | Journal of food measurement & characterization Vol. 12; no. 3; pp. 1449 - 1459 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.09.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2193-4126 2193-4134 |
| DOI | 10.1007/s11694-018-9760-1 |
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| Abstract | Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25 ± 2 °C and 20 ± 3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables (
p
< 0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively. |
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| AbstractList | Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25 ± 2 °C and 20 ± 3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables (p < 0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively. Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25 ± 2 °C and 20 ± 3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables ( p < 0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively. |
| Author | Nematinia, Elham Abdanan Mehdizadeh, Saman |
| Author_xml | – sequence: 1 givenname: Elham surname: Nematinia fullname: Nematinia, Elham organization: Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Ramin Agriculture and Natural Resources University of Khuzestan – sequence: 2 givenname: Saman orcidid: 0000-0002-4798-8031 surname: Abdanan Mehdizadeh fullname: Abdanan Mehdizadeh, Saman email: saman.abdanan@gmail.com, s.abdanan@ramin.ac.ir organization: Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Ramin Agriculture and Natural Resources University of Khuzestan |
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| Cites_doi | 10.1093/ps/79.12.1725 10.13031/2013.19167 10.1111/j.1365-2621.2003.tb05776.x 10.1006/bioe.2002.0122 10.1016/0893-6080(89)90020-8 10.1080/10942912.2015.1075215 10.1111/j.1365-2621.2010.02482.x 10.1007/s13197-014-1509-1 10.1093/ps/84.11.1774 10.1111/j.1750-3841.2006.00008.x 10.1002/jsfa.3454 10.1007/s00217-005-0179-7 10.3382/ps.2009-00028 10.1093/ps/83.10.1619 10.1016/j.aca.2009.07.049 10.1016/j.compag.2007.08.005 10.4314/wsa.v34i2.183640 10.1093/ps/85.3.550 10.1093/japr/14.3.548 10.1093/ps/84.10.1653 10.1093/ps/80.8.1240 10.1002/jsfa.2528 10.1016/j.jfoodeng.2008.04.013 10.1016/j.chemolab.2005.04.012 10.1111/j.1365-2621.2002.tb11398.x 10.1007/s11947-009-0265-0 10.1016/j.jfoodeng.2010.01.018 10.1515/ijfe-2015-0220 |
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| Keywords | Computer vision Freshness Artificial neural network Egg Haugh unit |
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| SubjectTerms | Albumen Algorithms Artificial neural networks Bayesian analysis Chemical properties Chemistry Chemistry and Materials Science Chemistry/Food Science Computer vision Correlation coefficient Correlation coefficients egg weight Eggs Engineering Food Science Freshness image analysis Neural networks Original Paper Parameters pH effects prediction Protein sources storage time Vision systems Yolk |
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