Machine learning regression algorithms for predicting muscle, bone, carcass fat and commercial cuts in hairless lambs

IF 1.6 3区 农林科学 Q2 AGRICULTURE, DAIRY & ANIMAL SCIENCE Small Ruminant Research Pub Date : 2024-05-22 DOI:10.1016/j.smallrumres.2024.107290
Samanta do Nascimento Monteiro , Alinne Andrade Pereira , Carolina Sarmanho Freitas , Gabriel Xavier Serrão , Marco Antônio Paula de Sousa , Alyne Cristina Sodré Lima , Luciara Celi da Silva Chaves Daher , Thomaz Cyro Guimarães de Carvalho Rodrigues , Welligton Conceição da Silva , Éder Bruno Rebelo da Silva , André Guimarães Maciel e Silva , Andréia Santana Bezerra da Silva , Jamile Andréa Rodrigues da Silva , José de Brito Lourenco-Junior
{"title":"Machine learning regression algorithms for predicting muscle, bone, carcass fat and commercial cuts in hairless lambs","authors":"Samanta do Nascimento Monteiro ,&nbsp;Alinne Andrade Pereira ,&nbsp;Carolina Sarmanho Freitas ,&nbsp;Gabriel Xavier Serrão ,&nbsp;Marco Antônio Paula de Sousa ,&nbsp;Alyne Cristina Sodré Lima ,&nbsp;Luciara Celi da Silva Chaves Daher ,&nbsp;Thomaz Cyro Guimarães de Carvalho Rodrigues ,&nbsp;Welligton Conceição da Silva ,&nbsp;Éder Bruno Rebelo da Silva ,&nbsp;André Guimarães Maciel e Silva ,&nbsp;Andréia Santana Bezerra da Silva ,&nbsp;Jamile Andréa Rodrigues da Silva ,&nbsp;José de Brito Lourenco-Junior","doi":"10.1016/j.smallrumres.2024.107290","DOIUrl":null,"url":null,"abstract":"<div><p>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, R<sup>2</sup>, 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.</p></div>","PeriodicalId":21758,"journal":{"name":"Small Ruminant Research","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0921448824000968/pdfft?md5=b94e7bd0f9b533d82f49b43b5f68ade4&pid=1-s2.0-S0921448824000968-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Small Ruminant Research","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921448824000968","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于预测无毛羔羊肌肉、骨骼、胴体脂肪和商业切割的机器学习回归算法
羊肉产业链需求的增长和对质量的要求催生了肉类行业对自动化技术的需求,也催生了以更快的速度和更高的标准化获得响应的需求。这项研究旨在根据 VIA(见图像分析)获得的测量结果预测胴体和商品肉的组织特征,该分析是在无毛羔羊的冷胴体上进行的,使用机器学习,采用回归技术进行变量选择。使用了 72 只阉割雄性羔羊的胴体信息,这些羔羊的年龄在 8 到 11 个月之间,平均冷胴体重量为 16.13 ± 3.98 千克。使用数码相机从背侧和侧视图拍摄羔羊尸体右侧的图像。通过 ImageJ2 软件获取 VIA 数据、测量值和形状描述符(面积、周长、宽度、长度、凸度、实心度),并与冷胴体重量相结合,生成四组数据,称为描述符集(DS)。获得 DS1、DS1'、DS2、DS2'、DS3、DS3'、DS4 和 DS4'。为了生成这些描述集,我们建立了一个数据库,并将其分为训练库(含 70% 的观测数据)和测试库(含 30% 的观测数据)。使用逐步法、LASSO 和弹性网回归法开发了多元线性回归模型,并结合 k 倍交叉验证来评估模型的性能。估算的准确性基于 RMSE、R2、Pearson 相关性和偏差指标。对于本研究中测试的变量,所提出的形状描述符在预测组织和重量变量方面大多是有效的。采用 LASSO 技术的 "DS1 "对总肌肉和脂肪变量的调整效果最好,其次是肩部、腰部和肋部切口。本研究测试的描述因子能够高质量地预测绝大多数测试特征,作为附加预测因子引入的冷胴体重量(CCW)变量使所有模型的拟合效果得到持续改善。与 LASSO 和 Elastic Net 相比,DS1 对 23 个预测特征的稳定性更高,而 Stepwise 的预测性能最差。尽管生成模型之间的调整很接近,但总体而言,Elastic Net 的性能低于 LASSO。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Small Ruminant Research
Small Ruminant Research 农林科学-奶制品与动物科学
CiteScore
3.10
自引率
11.10%
发文量
210
审稿时长
12.5 weeks
期刊介绍: Small Ruminant Research publishes original, basic and applied research articles, technical notes, and review articles on research relating to goats, sheep, deer, the New World camelids llama, alpaca, vicuna and guanaco, and the Old World camels. Topics covered include nutrition, physiology, anatomy, genetics, microbiology, ethology, product technology, socio-economics, management, sustainability and environment, veterinary medicine and husbandry engineering.
期刊最新文献
The relationship of anogenital distance with fertility and anti-müllerian hormone in Awassi and Cukurova meat ewes Effects of mutation in myostatin exon 3 on growth and carcass characteristics of Savak Akkaraman sheep Evaluation of pregnancy toxemia in goats: Metabolic profile, hormonal findings, and redox balance Estimates of genetic parameters and genetic trends for growth traits in Menz sheep under community-based breeding programs Behavioural and neuroendocrine responses in alpacas (Vicugna pacos) to the shearing procedure
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1