Machine learning regression algorithms for predicting muscle, bone, carcass fat and commercial cuts in hairless lambs
The growth in demand and demand for quality in the sheep chain has generated the need for automation techniques in the meat industry and the need to obtain responses with greater speed and standardization. The research aimed to predict tissue characteristics of the carcass and commercial cuts based...
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Published in | Small ruminant research Vol. 236; p. 107290 |
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Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.07.2024
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Abstract | The growth in demand and demand for quality in the sheep chain has generated the need for automation techniques in the meat industry and the need to obtain responses with greater speed and standardization. The research aimed to predict tissue characteristics of the carcass and commercial cuts based on measurements obtained by VIA – see oimage analysis, carried out on cold carcasses of hairless lambs, using machine learning employing regressive techniques for variable selection. Information from 72 carcasses of castrated male lambs, aged between 8 and 11 months, with an average cold carcass weight of 16.13 ± 3.98 kg, was used. Images of the right side of the carcasses were captured from the dorsal and lateral views using a digital camera. From the ImageJ2 software, VIA data, measurements and shape descriptors (areas, perimeters, widths, lengths, convexities, solidities) were obtained, combined with cold carcass weight and used to generate four sets of data, called descriptor sets (DSs). Obtaining DS1, DS1’, DS2, DS2’, DS3, DS3’, DS4 AND DS4’. To generate these sets, a database was formed and divided into a training bank (with 70% of the observations) and a test bank (30% of the observations). Multiple linear regression models were developed using Stepwise, LASSO, and Elastic Net regression methods, combined with k-fold cross-validation, to evaluate the performance of the models. The accuracy of the estimates was based on RMSE, R2, Pearson correlation and bias metrics. For the variables tested in this study, the proposed shape descriptors were mostly efficient in predicting tissue and weight variables. DS1' with the LASSO technique presented the best adjustments for variables total muscle and fat followed by shoulder, loin and rib cuts. The descriptors tested by this study were able to predict with quality the vast majority of the characteristics tested, the variable cold carcass weight (CCW), introduced as additional predictor, promoted a consistent improvement in the fits of all models. DS1 presented greater constancy for the twenty-three predicted characteristics and Stepwise presented the worst predictive performance, in relation to LASSO and Elastic Net. Despite close adjustments between the generated models, in general, Elastic Net presented lower performance than LASSO.
•The proposed descriptors predicted cuts’ commercial weights and tissue composition•Predicted characteristics in a non-invasive way depend on associated descriptors•Descriptors that best predicted the muscular components contained the whole carcass•Cold carcass weight variable improved the adjustments of all models |
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AbstractList | The growth in demand and demand for quality in the sheep chain has generated the need for automation techniques in the meat industry and the need to obtain responses with greater speed and standardization. The research aimed to predict tissue characteristics of the carcass and commercial cuts based on measurements obtained by VIA – see oimage analysis, carried out on cold carcasses of hairless lambs, using machine learning employing regressive techniques for variable selection. Information from 72 carcasses of castrated male lambs, aged between 8 and 11 months, with an average cold carcass weight of 16.13 ± 3.98 kg, was used. Images of the right side of the carcasses were captured from the dorsal and lateral views using a digital camera. From the ImageJ2 software, VIA data, measurements and shape descriptors (areas, perimeters, widths, lengths, convexities, solidities) were obtained, combined with cold carcass weight and used to generate four sets of data, called descriptor sets (DSs). Obtaining DS1, DS1’, DS2, DS2’, DS3, DS3’, DS4 AND DS4’. To generate these sets, a database was formed and divided into a training bank (with 70% of the observations) and a test bank (30% of the observations). Multiple linear regression models were developed using Stepwise, LASSO, and Elastic Net regression methods, combined with k-fold cross-validation, to evaluate the performance of the models. The accuracy of the estimates was based on RMSE, R2, Pearson correlation and bias metrics. For the variables tested in this study, the proposed shape descriptors were mostly efficient in predicting tissue and weight variables. DS1' with the LASSO technique presented the best adjustments for variables total muscle and fat followed by shoulder, loin and rib cuts. The descriptors tested by this study were able to predict with quality the vast majority of the characteristics tested, the variable cold carcass weight (CCW), introduced as additional predictor, promoted a consistent improvement in the fits of all models. DS1 presented greater constancy for the twenty-three predicted characteristics and Stepwise presented the worst predictive performance, in relation to LASSO and Elastic Net. Despite close adjustments between the generated models, in general, Elastic Net presented lower performance than LASSO.
•The proposed descriptors predicted cuts’ commercial weights and tissue composition•Predicted characteristics in a non-invasive way depend on associated descriptors•Descriptors that best predicted the muscular components contained the whole carcass•Cold carcass weight variable improved the adjustments of all models |
ArticleNumber | 107290 |
Author | Daher, Luciara Celi da Silva Chaves Pereira, Alinne Andrade Freitas, Carolina Sarmanho Silva, Jamile Andréa Rodrigues da da Silva, Welligton Conceição da Silva, Éder Bruno Rebelo Monteiro, Samanta do Nascimento Serrão, Gabriel Xavier Lourenco-Junior, José de Brito de Sousa, Marco Antônio Paula Bezerra da Silva, Andréia Santana Lima, Alyne Cristina Sodré Rodrigues, Thomaz Cyro Guimarães de Carvalho Silva, André Guimarães Maciel e |
Author_xml | – sequence: 1 givenname: Samanta do Nascimento surname: Monteiro fullname: Monteiro, Samanta do Nascimento organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 2 givenname: Alinne Andrade surname: Pereira fullname: Pereira, Alinne Andrade organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 3 givenname: Carolina Sarmanho surname: Freitas fullname: Freitas, Carolina Sarmanho organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 4 givenname: Gabriel Xavier surname: Serrão fullname: Serrão, Gabriel Xavier organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 5 givenname: Marco Antônio Paula surname: de Sousa fullname: de Sousa, Marco Antônio Paula organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 6 givenname: Alyne Cristina Sodré surname: Lima fullname: Lima, Alyne Cristina Sodré organization: Federal Institute of Amapá, Amapá, Brazil – sequence: 7 givenname: Luciara Celi da Silva Chaves surname: Daher fullname: Daher, Luciara Celi da Silva Chaves organization: Federal Rural University of Amazonia, Department of Animal Science, Belém, Pará, Brazil – sequence: 8 givenname: Thomaz Cyro Guimarães de Carvalho surname: Rodrigues fullname: Rodrigues, Thomaz Cyro Guimarães de Carvalho organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 9 givenname: Welligton Conceição surname: da Silva fullname: da Silva, Welligton Conceição organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 10 givenname: Éder Bruno Rebelo surname: da Silva fullname: da Silva, Éder Bruno Rebelo organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 11 givenname: André Guimarães Maciel e surname: Silva fullname: Silva, André Guimarães Maciel e organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 12 givenname: Andréia Santana surname: Bezerra da Silva fullname: Bezerra da Silva, Andréia Santana email: andreiazootecnistaufra@gmail.com organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil – sequence: 13 givenname: Jamile Andréa Rodrigues da surname: Silva fullname: Silva, Jamile Andréa Rodrigues da organization: Federal Rural University of Amazonia, Department of Animal Science, Belém, Pará, Brazil – sequence: 14 givenname: José de Brito surname: Lourenco-Junior fullname: Lourenco-Junior, José de Brito organization: Federal University of Pará, Department of Animal Science, Castanhal, Pará, Brazil |
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Keywords | LASSO Regression Elastic net Video image analysis Modeling Carcass Stepwise |
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Title | Machine learning regression algorithms for predicting muscle, bone, carcass fat and commercial cuts in hairless lambs |
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