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Effect of Genetic Crossing and Nutritional Management on the Mineral Composition of Carcass, Blood, Leather, and Viscera of Sheep

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Abstract

The effect of genetic crossing and nutritional management on weight gain and the concentration of minerals and trace elements in the carcass, blood, leather, and viscera of sheep were evaluated. Several statistical strategies were used to evaluate the different elemental composition characteristics of pure breed animals, i.e., White Dorper (ODO), Ile de France (OIF), Texel (OTX), and Santa Inês (OSI), as well as their crossbreeds 1/2 White Dorper and 1/2 Santa Inês (ODS), 1/2 Ile de France, and 1/2 Santa Inês (OIS), 1/2 Texel × 1/2 Santa Inês (OTS). Three different diets were evaluated AL (ad libitum), R75, and R63 (75 and 63 g of dry matter/kg of the animal metabolic weight, respectively). Levels of Ca, Cu, Fe, K, Mg, Mn, P, S, and Zn were determined by inductively coupled plasma optical emission spectrometry (ICP OES). The concentration of inorganic elements in the different body components was not affected by the diet (P > 0.05), and OTX and OTS were the breeds with the highest weight gain. Random forest importance models demonstrated that Zn in the carcass, K, Ca, and Zn in blood, and K in leather are most associated with separating the different evaluated sheep’s breeds. Crossbreeding the native Santa Inês breed with sheep of exotic breeds produces animals well adapted to confinement.

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References

  1. FAO, Food and Agriculture Organization of the United Nations (2020) FAOSTAT database. Retrieved from: http://www.fao.org/faostat/en/#data/QA/visualize. Accessed 24.09.2020

  2. Gootwine E (2020) Genetics and breeding of sheep and goats. In: Animal Agriculture. Elsevier Inc.Chapter 10. https://doi.org/10.1016/b978-0-12-817052-6.00010-0

  3. Andrade JC, Sobral LA, Ares G, Deliza R (2016) Understanding consumers’ perception of lamb meat using free word association. Meat Sci 117:68–74. https://doi.org/10.1016/j.meatsci.2016.02.039

    Article  PubMed  Google Scholar 

  4. Hermuche PM, Maranhão RLA, Guimarães RF, Carvalho OAJ, Gomes RAT, Paiva SR, McManus C (2013) Dynamics of sheep production in Brazil. ISPRS Int J Geo-Inf 2:665–679. https://doi.org/10.3390/ijgi2030665

    Article  Google Scholar 

  5. IBGE (2018) Instituto Brasileiro de Geografía e Estadítica. https://www.ibge.gov.br/estatisticas/economicas/agricultura-e-pecuaria.html. Accessed 05.09.20

  6. Pires MP, Farah MM, Carreño LOD, Utsunomiya ATH, Ono RK, Bertipaglia TS, Fonseca R (2015) Estimativas de parâmetros genéticos para características de crescimento em ovinos da raça Suffolk no Brasil. Arq Bras Med Vet Zootec 67:1119–1124. https://doi.org/10.1590/1678-4162-6949

    Article  Google Scholar 

  7. Ximenes LJF, Martins GA, Morais OR (2010) Ciência e Tecnologia na Pecuária de Caprinos e Ovinos. 1st ed. Fortaleza, Brasil, pp 107, 421

  8. Cardoso MTM, Landim AV, Louvandini H, McManus C (2013) Performance and carcass quality in three genetic groups of sheep in Brazil. R Bras Zootec 42:734–742. https://doi.org/10.1590/S1516-35982013001000007

    Article  Google Scholar 

  9. Costa RG, Batista ASM, Madruga MS, Neto SG, Queiroga RCRE, Araujo JTF, Villarroel AS (2009) Physical and chemical characterization of lamb meat from different genotypes submitted to diet with different fibre contents. Small Rumin Res 81:29–34. https://doi.org/10.1016/j.smallrumres.2008.10.007

    Article  Google Scholar 

  10. Dantas NLB, Souza BB, Da Silva MR, Silva GA, Pires JPS, Batista LF, Souza MF, Furtado DA (2019) Effect of the environment and diet on the physiological variables of sheep in the Brazilian semi-arid region. Semina: Cienc Agrar. https://doi.org/10.5433/1679-0359.2019v40n2p971

  11. Esteves GIF, Peripolli V, Tanure CB, Souza JR, Louvandini H, McManus C (2018) Carcass and cut traits in nulliparous and lambed female sheep of different ages and genetic groups. Acta Sci Anim Sci 40:34862. https://doi.org/10.4025/actascianimsci.v40i1.34862

    Article  Google Scholar 

  12. Garcia IFF, Almeida AK, Costa TIR, Junior IL, Ribeiro JS, Souza FA (2010) Carcass characteristics and cuts of Santa Inês lambs fed different roughage proportions and fat source. Rev Bras Zootec 39:1322–1327. https://doi.org/10.1590/S1516-35982010000600022

    Article  Google Scholar 

  13. Mortimer SI, Van der Werf JHJ, Jacob RH, Pannier L, Pearse KL, Gadner GE, Warner RD, Geesink GH, Edwards JEH, Ponnampalam EN, Ball AJ, Gilmour AR, Pethick DW (2014) Genetic parameters for meat quality traits of Australian lamb meat. Meat Sci 96:1016–1024. https://doi.org/10.1016/j.meatsci.2013.09.007

    Article  CAS  PubMed  Google Scholar 

  14. Souza DA, Selaive-Villarroel AB, Pereira ES, Silva EMC, Oliveira RL (2016) Effect of the Dorper breed on the performance, carcass and meat traits of lambs bred from Santa Inês sheep. Small Rumin Res 145:76–80. https://doi.org/10.1016/j.smallrumres.2016.10.017

    Article  Google Scholar 

  15. Issakowicz J, Issakowicz ACKS, Bueno MS, Costa RLD, Geraldo AT, Abdalla AL, McManaus C, Louvandini H (2018) Crossbreeding locally adapted hair sheep to improve productivity and meat quality. Sci Agric 75:288–295. https://doi.org/10.1590/1678-992x-2016-0505

    Article  CAS  Google Scholar 

  16. Souza BC, Sena LS, Loureiro D, Raynal JT, Sousa TJ, Bastos BL, Meyer R, Portela RW (2016) Determinação de valores de referência séricos para os electrólitos magnésio, cloretos, cálcio e fósforo em ovinos das raças Dorper e Santa Inês. Pesqui Vet Bras 36:167–173. https://doi.org/10.1590/S0100-736X2016000300004

    Article  Google Scholar 

  17. Jaborek JR, Zerby HN, Moeller SJ, Wick MP, Fluharty FL, Garza H, Garcia LG, England EM (2018) Effect of energy source and level, and animal age and sex on meat characteristics of sheep. Small Rumin Res 166:53–60. https://doi.org/10.1016/j.smallrumres.2018.07.005

    Article  Google Scholar 

  18. Nassu RT, Tullio RR, Berndt A, Francisco VC, Diesel TA, Alencar MM (2017) Effect of the genetic group, production system and sex on the meat quality and sensory traits of beef from crossbred animals. Trop Anim Health Prod 49:1289–1294. https://doi.org/10.1007/s11250-017-1327-3

    Article  CAS  PubMed  Google Scholar 

  19. Santos VC, Ezequiel JMB, Morgado ES, Junior SCS (2013) Características da Carcaça e da Carne de Cordeiros Alimentados om Subprodutos de Oleaginosas. Acta Sci Anim Sci 35. https://doi.org/10.4025/actascianimsci.v35i4.20403

  20. Albuquerque FHMAR, Oliveira LS (2015) Produção de Ovinos de Corte: Terminação de Cordeiros no Semiárido, 1st edn. Brasil, Brasilia (Chapter 2)

    Google Scholar 

  21. Pilecco VM, Carvalho S, Pellegrini LG, Mello RO, Pacheco OS, Pellegrin ACRS, Moro AB, Lopez AF, Mello VL (2018) Carcaça e componentes não carcaça de cordeiros terminados em confinamento com caroço de algodão na dieta. Arq Bras Med Vet Zootec 70:1935–1942. https://doi.org/10.1590/1678-4162-9433

    Article  Google Scholar 

  22. Shen X, Song C, Wu T (2020) Effects of Nano-copper on antioxidant function in copper-deprived Guizhou black goats. Biol Trace Elem Res. https://doi.org/10.1007/s12011-020-02342-1

  23. Novoselec J, Klir Ž, Domaćinović M, Lončarić Z, Antunović Z (2018) Biofortification of feedstuffs with microelements in animal nutrition. Poljoprivreda. https://doi.org/10.18047/poljo.24.1.4

  24. Schweinzer V, Iwersen M, Drillich M, Wittek T, Tichy A, Mueller A, Krametter-Froetscher R (2017) Macromineral and trace element supply in sheep and goats in Austria. Vet Med. https://doi.org/10.17221/243/2015-VETMED

  25. Shen X, Song C (2020) Responses of Chinese merino sheep (Junken type) on copper-deprived natural pasture. Biol Trace Elem Res. https://doi.org/10.1007/s12011-020-02214-8

  26. Gharibzahedi SMT, Jafari SM (2017) The importance of minerals in human nutrition: bioavailability, food fortification, processing effects and nanoencapsulation. Trends Food Sci Technol 62:119–132. https://doi.org/10.1016/j.tifs.2017.02.017

    Article  CAS  Google Scholar 

  27. Statistical Analysis Systems- SAS (2012) user’s guide: statistics, version 9, v.3

  28. R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing URL, Vienna, Austria, https://www.R-project.org/

  29. Krijthe JH (2015) Rtsne: t-distributed stochastic neighbor embedding using a Barnes-Hut implementation. https://github.com/jkrijthe/Rtsne

  30. Konopka T (2020) Umap: uniform manifold approximation and projection. R package version 0.2.5.0. https://cran.r-project.org/package=umap

  31. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  32. Fowler SM, Morris S, Hopkins DL (2019) Nutritional composition of lamb retail cuts from the carcasses of extensively finished lambs. Meat Sci 154:126–132. https://doi.org/10.1016/j.meatsci.2019.04.016

    Article  CAS  PubMed  Google Scholar 

  33. Higuera JM, Silva ABS, Nogueira ARA (2019) Multi-energy calibration: a practical method for determination of macro and micro nutrients in meat by ICP OES. J Braz Chem Soc. https://doi.org/10.21577/0103-5053.20190171

  34. Kasap A, Kaić A, Širić I, Antunović Z, Mioč B (2018) Proximate and mineral composition of M. longissimus thoracis et lumborum of suckling lambs from three Croatian indigenous breeds reared in outdoor conditions. Ital J Anim Sci. https://doi.org/10.1080/1828051X.2017.1377643

  35. Miguélez E, Zumalacárregui JM, Osorio MT, Figueira AC, Fonseca B, Mateo J (2008) Quality traits of suckling-lamb meat covered by the protected geographical indication “Lechazo de Castilla y León” European quality label. Small Rumin Res 77:65–70. https://doi.org/10.1016/j.smallrumres.2008.02.002

    Article  Google Scholar 

  36. Pannier L, Pethick DW, Boyce MD, Ball AJ, Jacob RH, Gardner GE (2014) Associations of genetic and non-genetic factors with concentrations of iron and zinc in the longissimus muscle of lamb. Meat Sci 96:1111–1119. https://doi.org/10.1016/j.meatsci.2013.08.013

    Article  CAS  PubMed  Google Scholar 

  37. Ponnampalam EN, Kerr MG, Butler KL, Cottrell JJ, Dunshea FR, Jacobs JL (2019) Filling the out of season gaps for lamb and hogget production: diet and genetic influence on carcass yield, carcass composition and retail value of meat. Meat Sci 148:156–163. https://doi.org/10.1016/j.meatsci.2018.08.027

    Article  CAS  PubMed  Google Scholar 

  38. Nalyanya KM, Rop RK, Onyuka AS, Birech Z, Okonda JJ (2020) Variation of elemental concentration in leather during post-tanning operation using energy dispersive X-ray fluorescence spectroscopy: principal component analysis approach. Int J Environ An Ch:1–13. https://doi.org/10.1080/03067319.2020.1746292

  39. Neiva AM, Sperança MA, Costa VC, Jacinto MAC, Pereira-Filho ER (2018) Determination of toxic metals in leather by wavelength dispersive X-ray fluorescence (WDXRF) and inductively coupled plasma optical emission spectrometry (ICP OES) with emphasis on chromium. Environ Monit Assess 190:618. https://doi.org/10.1007/s10661-018-6990-y

    Article  CAS  PubMed  Google Scholar 

  40. Aslan A, Üzüm NO (2015) Determining the heavy metal contents of natural and artificial upholstery leathers. Tekstil ve Konfeksiyon 25(1):33–37

    Google Scholar 

  41. Litwińczuk Z, Domaradzki P, Florek M, Zółkiewski P, Staszowska A (2015) Content of macro and microelements in the meat of young bulls of three native breeds (Polish red, white-backed and Polish black-and-white) in comparison with Simmental and Polish Holstein-Friesian. Ann Anim Sci 15:977–985. https://doi.org/10.1515/aoas-2015-0058

    Article  CAS  Google Scholar 

  42. ARCO (2020) Associação Brasileira de Criadores de Ovinos. Padrões raciais. http://www.arcoovinos.com.br/index.php/mn-srgo/mn-padroesraciais. Accessed in 27.04.2020

  43. Patel N, Bergamaschi M, Magro L, Petrini A, Bittante G (2019) Relationships of a detailed mineral profile of meat with animal performance and beef quality. Animals (Basel). 9. https://doi.org/10.3390/ani9121073

  44. Sousa MAP, Lima ACS, Araújo JC, Grimarães CMC, Joele MRSP, Borges I, Daher LCC, Silva AGM (2019) Tissue composition and allometric growth of carcass of lambs Santa Inês and crossbreed with breed Dorper. Trop Anim Health Prod 51:1903–1908. https://doi.org/10.1007/s11250-019-01886-2

    Article  PubMed  Google Scholar 

  45. Carter JA, Long CS, Smith BP, Smith TL, Donati GL (2019) Combining elemental analysis of toenails and machine learning techniques as a non-invasive diagnostic tool for the robust classification of type-2 diabetes. Expert Syst Appl 115:245–255. https://doi.org/10.1016/j.eswa.2018.08.002

    Article  Google Scholar 

  46. Carter JA, Sloop JT, Donati GL (2020) Non-analyte signals and supervised learning to evaluate matrix effects and predict analyte recoveries in inductively coupled plasma optical emission spectrometry. J Anal At Spectrom 35:679–692. https://doi.org/10.1039/D0JA00007H

    Article  CAS  Google Scholar 

  47. Allegretta I, Marangoni B, Manzari P, Porfido C, Terzano R, Pascale O, Senesi GS (2020) Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms. Talanta. 212:120785. https://doi.org/10.1016/j.talanta.2020.120785

    Article  CAS  PubMed  Google Scholar 

  48. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometrics and Intelligent Laboratory 2:37–52. https://doi.org/10.1016/0169-7439(87)80084-9

    Article  CAS  Google Scholar 

  49. Kobak D, Berens P (2019) The art of using t-SNE for single-cell transcriptomics. Nat Commun 10:5416. https://doi.org/10.1038/s41467-019-13056-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. McInnes L, Healy J, Melville J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv. https://arxiv.org/pdf/1802.03426

  51. Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  52. Goldstein BA, Polley EC, Briggs FBS (2011) Random forests for genetic association studies. Stat Appl Genet Mol Biol 1691. https://doi.org/10.2202/1544-6115

  53. Zhang S, Tan Z, Liu J, Xu Z, Du Z (2020) Determination of the food dye indigotin in cream by near-infrared spectroscopy technology combined with random forest model. Spectrochim Acta A 227:117551. https://doi.org/10.1016/j.saa.2019.117551

    Article  CAS  Google Scholar 

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Acknowledgments

The authors would like to thank the National Council for Scientific and Technological Development, the São Paulo Research Foundation, FAPESP, the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance code 001), the National Institute of Science and Applied Analytical Technologies (INCTAA), and the Graduate School of Arts and Sciences at Wake Forest University for their support.

Funding

The authors would like to thank the National Council for Scientific and Technological Development for the fellowship provided to J. M. H., A. B. S. S., and A.R.A.N. (CNPq, grants 141315/2017-2, 153125/2016-0, 409852/2018-0 and 308178/2018-1). We are also grateful to the São Paulo Research Foundation (FAPESP, grants 2018/26145-9 and 2011/51564-6).

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Julymar M. Higuera: investigation, validation, writing- original draft preparation. Ana Beatriz S. Silva: investigation, formal analysis, writing- original draft preparation. Wignez Henrique: methodology, conceptualization. Sergio N. Esteves: conceptualization, visualization. Waldomiro Barioni Jr.: formal analysis. George L. Donati: formal analysis, resources. Ana Rita A. Nogueira: funding acquisition, supervision, writing- reviewing and editing.

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Correspondence to Ana Rita A. Nogueira.

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This study was carried out following the Brazilian legislation on experimentation involving the use of animals adopted by the National Council of Experimental Control (CONCEA). It was approved by the Ethics Committee In Animal Use (CEUA) of Embrapa Pecuária Sudeste under approval PRT no. 08/2016.

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Higuera, J.M., Silva, A.B.S., Henrique, W. et al. Effect of Genetic Crossing and Nutritional Management on the Mineral Composition of Carcass, Blood, Leather, and Viscera of Sheep. Biol Trace Elem Res 199, 4133–4144 (2021). https://doi.org/10.1007/s12011-020-02543-8

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