Predicting Beef Carcass Fatness Using an Image Analysis System
The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and ap...
Ausführliche Beschreibung
Autor*in: |
José A. Mendizabal [verfasserIn] Guillerno Ripoll [verfasserIn] Olaia Urrutia [verfasserIn] Kizkitza Insausti [verfasserIn] Beatriz Soret [verfasserIn] Ana Arana [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2021 |
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Übergeordnetes Werk: |
In: Animals - MDPI AG, 2011, 11(2021), 10, p 2897 |
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Übergeordnetes Werk: |
volume:11 ; year:2021 ; number:10, p 2897 |
Links: |
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DOI / URN: |
10.3390/ani11102897 |
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Katalog-ID: |
DOAJ014721880 |
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10.3390/ani11102897 doi (DE-627)DOAJ014721880 (DE-599)DOAJ9974b36f0d3a4755a10185162481cf42 DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 José A. Mendizabal verfasserin aut Predicting Beef Carcass Fatness Using an Image Analysis System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (<i<y</i<-axis) on the carcass fat area (<i<x</i<-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R<sup<2</sup< = 0.72; <i<p</i< < 0.001) than from the visual fatness scores (R<sup<2</sup< = 0.66; <i<p</i< < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness. carcass fatness image analysis prediction young bulls Veterinary medicine Zoology Guillerno Ripoll verfasserin aut Olaia Urrutia verfasserin aut Kizkitza Insausti verfasserin aut Beatriz Soret verfasserin aut Ana Arana verfasserin aut In Animals MDPI AG, 2011 11(2021), 10, p 2897 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:11 year:2021 number:10, p 2897 https://doi.org/10.3390/ani11102897 kostenfrei https://doaj.org/article/9974b36f0d3a4755a10185162481cf42 kostenfrei https://www.mdpi.com/2076-2615/11/10/2897 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 10, p 2897 |
spelling |
10.3390/ani11102897 doi (DE-627)DOAJ014721880 (DE-599)DOAJ9974b36f0d3a4755a10185162481cf42 DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 José A. Mendizabal verfasserin aut Predicting Beef Carcass Fatness Using an Image Analysis System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (<i<y</i<-axis) on the carcass fat area (<i<x</i<-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R<sup<2</sup< = 0.72; <i<p</i< < 0.001) than from the visual fatness scores (R<sup<2</sup< = 0.66; <i<p</i< < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness. carcass fatness image analysis prediction young bulls Veterinary medicine Zoology Guillerno Ripoll verfasserin aut Olaia Urrutia verfasserin aut Kizkitza Insausti verfasserin aut Beatriz Soret verfasserin aut Ana Arana verfasserin aut In Animals MDPI AG, 2011 11(2021), 10, p 2897 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:11 year:2021 number:10, p 2897 https://doi.org/10.3390/ani11102897 kostenfrei https://doaj.org/article/9974b36f0d3a4755a10185162481cf42 kostenfrei https://www.mdpi.com/2076-2615/11/10/2897 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 10, p 2897 |
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10.3390/ani11102897 doi (DE-627)DOAJ014721880 (DE-599)DOAJ9974b36f0d3a4755a10185162481cf42 DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 José A. Mendizabal verfasserin aut Predicting Beef Carcass Fatness Using an Image Analysis System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (<i<y</i<-axis) on the carcass fat area (<i<x</i<-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R<sup<2</sup< = 0.72; <i<p</i< < 0.001) than from the visual fatness scores (R<sup<2</sup< = 0.66; <i<p</i< < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness. carcass fatness image analysis prediction young bulls Veterinary medicine Zoology Guillerno Ripoll verfasserin aut Olaia Urrutia verfasserin aut Kizkitza Insausti verfasserin aut Beatriz Soret verfasserin aut Ana Arana verfasserin aut In Animals MDPI AG, 2011 11(2021), 10, p 2897 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:11 year:2021 number:10, p 2897 https://doi.org/10.3390/ani11102897 kostenfrei https://doaj.org/article/9974b36f0d3a4755a10185162481cf42 kostenfrei https://www.mdpi.com/2076-2615/11/10/2897 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 10, p 2897 |
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10.3390/ani11102897 doi (DE-627)DOAJ014721880 (DE-599)DOAJ9974b36f0d3a4755a10185162481cf42 DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 José A. Mendizabal verfasserin aut Predicting Beef Carcass Fatness Using an Image Analysis System 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (<i<y</i<-axis) on the carcass fat area (<i<x</i<-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R<sup<2</sup< = 0.72; <i<p</i< < 0.001) than from the visual fatness scores (R<sup<2</sup< = 0.66; <i<p</i< < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness. carcass fatness image analysis prediction young bulls Veterinary medicine Zoology Guillerno Ripoll verfasserin aut Olaia Urrutia verfasserin aut Kizkitza Insausti verfasserin aut Beatriz Soret verfasserin aut Ana Arana verfasserin aut In Animals MDPI AG, 2011 11(2021), 10, p 2897 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:11 year:2021 number:10, p 2897 https://doi.org/10.3390/ani11102897 kostenfrei https://doaj.org/article/9974b36f0d3a4755a10185162481cf42 kostenfrei https://www.mdpi.com/2076-2615/11/10/2897 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 10, p 2897 |
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Predicting Beef Carcass Fatness Using an Image Analysis System |
abstract |
The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (<i<y</i<-axis) on the carcass fat area (<i<x</i<-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R<sup<2</sup< = 0.72; <i<p</i< < 0.001) than from the visual fatness scores (R<sup<2</sup< = 0.66; <i<p</i< < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness. |
abstractGer |
The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (<i<y</i<-axis) on the carcass fat area (<i<x</i<-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R<sup<2</sup< = 0.72; <i<p</i< < 0.001) than from the visual fatness scores (R<sup<2</sup< = 0.66; <i<p</i< < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness. |
abstract_unstemmed |
The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (<i<y</i<-axis) on the carcass fat area (<i<x</i<-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R<sup<2</sup< = 0.72; <i<p</i< < 0.001) than from the visual fatness scores (R<sup<2</sup< = 0.66; <i<p</i< < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness. |
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container_issue |
10, p 2897 |
title_short |
Predicting Beef Carcass Fatness Using an Image Analysis System |
url |
https://doi.org/10.3390/ani11102897 https://doaj.org/article/9974b36f0d3a4755a10185162481cf42 https://www.mdpi.com/2076-2615/11/10/2897 https://doaj.org/toc/2076-2615 |
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author2 |
Guillerno Ripoll Olaia Urrutia Kizkitza Insausti Beatriz Soret Ana Arana |
author2Str |
Guillerno Ripoll Olaia Urrutia Kizkitza Insausti Beatriz Soret Ana Arana |
ppnlink |
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SF - Animal Culture |
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doi_str |
10.3390/ani11102897 |
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up_date |
2024-07-04T00:13:58.389Z |
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