Carne oscura, firme y seca (DFD). Causas, implicaciones y métodos de determinación
Dark-cutting meat. Causes, implications, and methods of determination
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Objetivo. Revisar las causas, consecuencias y métodos de determinación de la carne DFD con el fin de contribuir al conocimiento de esta anomalía para encontrar alternativas que contrarresten su presencia. Desarrollo. La carne DFD se presenta cuando las reservas de glucógeno muscular no son suficientes para que el pH descienda a su punto óptimo 24 h después del beneficio. Se estudian diversos factores ambientales e inherentes al animal que pueden estar interrelacionados y que serían los responsables de estrés y consecuente aparición de carne DFD. Así mismo, se revisan los diferentes métodos con los cuales se puede determinar esta condición. Consideraciones finales. El manejo de los animales pre- y pos-beneficio es determinante en la aparición de carnes DFD. Conocer los factores que influyen sobre su presencia y los métodos disponibles para su determinación puede contribuir con la disminución de esta anomalía y mejorar la calidad de las canales.
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Klurfeld DM. Research gaps in evaluating the relationship of meat and health. Meat Sci. 2015; 109:86–95. http://dx.doi.org/10.1016/j.meatsci.2015.05.022
Kamruzzaman M, Makino Y, Oshita S. Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review. Anal Chim Acta. 2015; 853(1):19–29. http://dx.doi.org/10.1016/j.aca.2014.08.043
García G, Zambrano W, Martínez G, Zambrano J. Alteraciones del pH y temperatura en la canal a causa de factores relacionados al transporte bovino previo al sacrificio. Rev Las Agrociencias. 2021; 26(Ed Esp)95–109. https://doi.org/10.33936/la_tecnica.v0i0.2524
Ponnampalam EN, Hopkins DL, Bruce H, Li D, Baldi G, Bekhit AE din. Causes and Contributing Factors to “Dark Cutting” Meat: Current Trends and Future Directions: A Review. Compr Rev Food Sci Food Saf. 2017; 16(3):400–430. https://doi.org/10.1111/1541-4337.12258
de Sousa Ribeiro CC, Contreras-Castillo CJ, Santos-Donado PR Dos, Venturini AC. New alternatives for improving and assessing the color of dark–cutting beef – a review. Sci Agric. 2022; 79(1):1–16. https://doi.org/10.1590/1678-992X-2020-0079
Prieto N, López-Campos O, Zijlstra RT, Uttaro B, Aalhus JL. Discrimination of beef dark cutters using visible and near infrared reflectance spectroscopy. Can J Anim Sci. 2014; 94(3):445–454. https://doi.org/10.4141/cjas-2014-024
Roberts JJ, Cozzolino D. An Overview on the Application of Chemometrics in Food Science and Technology—An Approach to Quantitative Data Analysis. Food Anal Methods. 2016; 9(12):3258–3267. http://dx.doi.org/10.1007/s12161-016-0574-7
Paredi G, Raboni S, Bendixen E, de Almeida AM, Mozzarelli A. “Muscle to meat” molecular events and technological transformations: The proteomics insight. J Proteomics. 2012; 75(14):4275–4289. http://dx.doi.org/10.1016/j.jprot.2012.04.011
Barragán-Hernández WA, Mahecha-Ledesma L, Olivera-Angel M, Angulo-Arizala J. Compositional and sensory quality of beef and its determination by near infrared. Agron Mesoamerican. 2021; 32(3):1000–1018. https://doi.org/10.15517/am.v32i3.40607
Aboah J, Lees N. Consumers use of quality cues for meat purchase: Research trends and future pathways. Meat Sci. 2020; 166:108142. https://doi.org/10.1016/j.meatsci.2020.108142
Purslow PP, Warner RD, Clarke FM, Hughes JM. Variations in meat colour due to factors other than myoglobin chemistry; a synthesis of recent findings (invited review). Meat Sci 2020; 159:107941. https://doi.org/10.1016/j.meatsci.2019.107941
Prill LL, Drey LN, Olson BA, Rice EA, Gonzalez JM, Vipham JL, et al. Visual Degree of Doneness Impacts Beef Palatability for Consumers with Different Degree of Doneness Preferences. Meat Muscle Biol. 2019; 3(1):411-423. https://doi.org/10.22175/mmb2019.07.0024
Gunders D. Wasted: How America is losing up to 40 percent of its food from farm to fork to landfill. NRDC Issue Pap; 2012. https://www.nrdc.org/sites/default/files/wasted-food-IP.pdf
Franco D, Mato A, Salgado FJ, López-Pedrouso M, Carrera M, Bravo S, et al. Tackling proteome changes in the longissimus thoracis bovine muscle in response to pre-slaughter stress. J Proteomics. 2015; 122:73–85. http://dx.doi.org/10.1016/j.jprot.2015.03.029
Beef Cattle Research Council. The 2010/11 National Beef Quality Audit: Canadá; 2010. https://www.beefresearch.ca/files/pdf/fact-sheets/nbqa_full_brochure_feb_2013.pdf
Beef Cattle Research Council. National Beef Quality Audit, 2010/11 Beef Carcass Audit Fact Sheet: Canadá; 2011. https://www.beefresearch.ca/files/pdf/fact-sheets/1181_CCA_NBQA_Factsheet_June_15_F.pdf
Mcgilchrist P, Perovic JL, Gardner GE, Pethick DW, Jose CG. The incidence of dark cutting in southern Australian beef production systems fluctuates between months. Anim Prod Sci. 2014; 54(10):1765–1769. https://doi.org/10.1071/AN14356
Riggs PK, Therrien DA, Vaughn RN, Rotenberry ML, Davis BW, Herring AD, et al. Differential Expression of MicroRNAs in Dark-Cutting Meat from Beef Carcasses. Appl. Sci. 2022; 12(7):3555. https://doi.org/10.3390/app12073555
Fuente-Garcia C, Aldai N, Sentandreu E, Oliván M, Franco D, García-Torres S, Sentandreu M. Assessment of caspase activity in post mortem muscle as a way to explain characteristics of DFD beef. J. Food Compos. Anal. 2022; 111:104599. https://doi.org/10.1016/j.jfca.2022.104599
Holdstock J, Aalhus JL, Uttaro BA, López-Campos Ó, Larsen IL, Bruce HL. The impact of ultimate pH on muscle characteristics and sensory attributes of the longissimus thoracis within the dark cutting (Canada B4) beef carcass grade. Meat Sci. 2014; 98(4):842–849. https://doi.org/10.1016/j.meatsci.2014.07.029
Leyva-García IA, Figueroa-Saavedra F, Sánchez-López E, Pérez-Linares C, Barreras-Serrano A. Impacto económico de la presencia de carne DFD en una planta de sacrificio Tipo Inspección Federal ( TIF ). Arch Med Vet. 2012; 44(1):39–42.
Loudon KMW, Lean IJ, Pethick DW, Gardner GE, Grubb LJ, Evans AC, et al. On farm factors increasing dark cutting in pasture fi nished beef cattle. Meat Sci. 2018; 144:110–117. https://doi.org/10.1016/j.meatsci.2018.06.011
Rosa A, Fonseca R, Balieiro JC, Poleti MD, Domenech-Pérez K, Farnetani B, et al. Incidence of DFD meat on Brazilian beef cuts. Meat Sci. 2016; 112:132–133. https://doi.org/10.1016/j.meatsci.2015.08.074
Patiño RM, Botero LM, Bohóquez W, Therán TM. Bienestar de Bovinos durante la fase de faenado en una planta de benefi cio de la región Caribe de Colombia. ACCB. 2019; 1(31):24–35. https://revistaaccb.org/r/index.php/accb/article/view/178
Ramanathan R, Lambert LH, Nair MN, Morgan B, Feuz R, Mafi G. Economic Loss, Amount of Beef Discarded, Natural Resources Wastage, and Environmental Impact Due to Beef Discoloration. Meat Muscle Biol. 2022; 6(1):13218. https://doi.org/10.22175/mmb.13218
Ramanathan R, Hunt MC, Mancini RA, Nair MN, Denzer ML, Suman SP, et al. Recent Updates in Meat Color Research: Integrating Traditional and High-Throughput Approaches. Meat Muscle Biol. 2020; 4(2):1-24. https://doi.org/10.22175/mmb.9598
Claudia Terlouw EM, Picard B, Deiss V, Berri C, Hocquette JF, Lebret B, et al. Understanding the determination of meat quality using biochemical characteristics of the muscle: Stress at slaughter and other missing keys. Foods. 2021; 10(1):1-24. https://doi.org/10.3390/foods10010084
Fraeye I, Kratka M, Vandenburgh H, Thorrez L. Sensorial and Nutritional Aspects of Cultured Meat in Comparison to Traditional Meat: Much to Be Inferred. Front Nutr. 2020; 7(35):1-7. https://doi.org/10.3389/fnut.2020.00035
Sierra V, Olivan M. Role of Mitochondria on Muscle Cell Death and Meat Tenderization. Recent Pat Endocr Metab Immune Drug Discov. 2013; 7(2):120–129. https://dx.doi.org/10.2174/1872214811307020005
Lana A, Zolla L. Proteolysis in meat tenderization from the point of view of each single protein: A proteomic perspective. J Proteomics. 2016; 147:85–97. http://dx.doi.org/10.1016/j.jprot.2016.02.011
England EM, Matarneh SK, Oliver EM, Apaoblaza A, Scheffler TL, Shi H, et al. Excess glycogen does not resolve high ultimate pH of oxidative muscle. Meat Sci. 2016; 114:95–102. https://doi.org/10.1016/j.meatsci.2015.10.010
McKeith RO, King DA, Grayson AL, Shackelford SD, Gehring KB, Savell JW, et al. Mitochondrial abundance and efficiency contribute to lean color of dark cutting beef. Meat Sci. 2016; 116:165–173. https://doi.org/10.1016/j.meatsci.2016.01.016
England EM, Matarneh SK, Scheffler TL, Wachet C, Gerrard DE. pH inactivation of phosphofructokinase arrests postmortem glycolysis. Meat Sci. 2014; 98(4):850–857. https://doi.org/10.1016/j.meatsci.2014.07.019
Zhang M, Dunshea FR, Warner RD, Digiacomo K, Chauhan SS, Warner RD. Impacts of heat stress on meat quality and strategies for amelioration : a review. Int J Biometeorol. 2020; 64:1613–1628. https://doi.org/10.1007/s00484-020-01929-6
AMSA. Research Guidelines for Cookery, Sensory Evaluation, and Instrumental Tenderness Measurements of Meat. American Meat Science Association Educational Foundation. 2015. https://meatscience.org/docs/default-source/publications-resources/amsa-sensory-and-tenderness-evaluation-guidelines/research-guide/2015-amsa-sensory-guidelines-1-0.pdf?sfvrsn=6
Ramanathan R, Suman SP, Faustman C. Biomolecular Interactions in Postmortem Skeletal Muscles Governing Fresh Meat Color : A Review. J. Agric. Food Chem. 2020; 68(46):12779-12787. https://doi.org/10.1021/acs.jafc.9b08098
Contreras-Castillo CJ, Lomiwes D, Wu G, Frost D, Farouk MM. The effect of electrical stimulation on post mortem myofibrillar protein degradation and small heat shock protein kinetics in bull beef. Meat Sci. 2016; 113:65–72. https://doi.org/10.1016/j.meatsci.2015.11.012
Wang LL, Yu QL, Han L, Ma XL, Song R De, Zhao SN, et al. Study on the effect of reactive oxygen species-mediated oxidative stress on the activation of mitochondrial apoptosis and the tenderness of yak meat. Food Chem. 2018; 244:394–402. http://dx.doi.org/10.1016/j.foodchem.2017.10.034
Joo ST, Kim GD, Hwang YH, Ryu YC. Control of fresh meat quality through manipulation of muscle fiber characteristics. Meat Sci. 2013; 95(4):828–836. https://doi.org/10.1016/j.meatsci.2013.04.044
Mouzo D, Rodríguez-vázquez R, Lorenzo JM, Franco D, Zapata C, López-pedrouso M. Proteomic application in predicting food quality relating to animal welfare . A review. Trends Food Sci Technol. 2020; 99:520–530. https://doi.org/10.1016/j.tifs.2020.03.029
Loredo-Osti J, Sánchez-López E, Barreras-Serrano A, Figueroa-Saavedra F, Pérez-Linares C, Ruiz-Albarrán M, et al. An evaluation of environmental, intrinsic and pre- and post-slaughter risk factors associated to dark-cutting beef in a Federal Inspected Type slaughter plant. Meat Sci. 2019; 150:85–92. https://doi.org/10.1016/j.meatsci.2018.12.007
Silva LHP, Assis DEF, Estrada MM, Assis GJF, Zamudio GDR, Carneiro GB, et al. Carcass and meat quality traits of Nellore young bulls and steers throughout fattening. Livest Sci. 2019; 229:28–36. https://doi.org/10.1016/j.livsci.2019.09.012
Mahmood S, Basarab JA, Dixon WT, Bruce HL. Relationship between phenotype, carcass characteristics and the incidence of dark cutting in heifers. Meat Sci. 2016; 121:261–271. https://doi.org/10.1016/j.meatsci.2016.06.020
King DA, Shackelford SD, Kuehn LA, Kemp CM, Rodriguez AB, Thallman RM, et al. Contribution of genetic influences to animal-to-animal variation in myoglobin content and beef lean color stability. J Anim Sci. 2010; 88(3):1160–1167. https://doi.org/10.2527/jas.2009-2544.
Kawecki K, Stangierski J, Niedźwiedź J, Grześ B. The impact of environmental factors on the occurrence of DFD-type of beef in commercial abattoirs. Emirates J Food Agric. 2020; 32(7):533–542. https://doi.org/10.9755/ejfa.2020.v32.i7.2125
Marenčić D, Ivanković A, Kozačinski L, Popović M. The effect of sex and age at slaughter on the physicochemical properties of baby-beef meat. Vet Arh. 2018; 88(1):101–110. https://doi.org/10.24099/vet.arhiv.160720
Jacinto-valderrama RA, Sicca G, Sampaio L, Lucia M, Lima P, Noely J. Immunocastration on performance and meat quality of Bos indicus (Nellore) cattle under different nutritional systems. Sci. agric. 2021; 78(2):e20190136. http://dx.doi.org/10.1590/1678-992X-2019-0136
Gardner GE, Hopkins DL, Greenwood PL, Cake MA, Boyce MD, Pethick DW. Sheep genotype, age and muscle type affect the expression of metabolic enzyme markers. Aust J Exp Agric. 2007; 47(10):1180–1189. https://doi.org/10.1071/EA07093
Greenwood PL, Harden S, Hopkins DL. Myofibre characteristics of ovine longissimus and semitendinosus muscles are influenced by sire breed, gender, rearing type, age and carcass weight. Aust J Exp Agric. 2007; 47(10):1137–1146. https://doi.org/10.1071/EA06324
Pérez Linares, Serrano, F. Figueroa Saavedra AB. Management Factores de manejo asociados a carne DFD en bovinos en climadesertico. Arch Zootec 2008; 57(220):545–547.
Steel C, Lees AM, Tarr G, Warner R, Dunshea F, Cowley F, et al. The impact of weather on the incidence of dark cutting in Australian feedlot cattle. Int J Biometeorol. 2022; 66(2):263–274. https://doi.org/10.1007/s00484-021-02180-3
Munilla ME, Vittone JS, Lado M, Romera SA, Teira GA. Efecto de las prácticas durante el manejo pre-faena sobre el rendimiento de la carne de bovinos. Rev Vet. 2021; 32(1):48. http://dx.doi.org/10.30972/vet.3215633
Diro M, Mekete B, Gebremedhin EZ. Effect of pre-slaughter beef cattle handling on welfare and beef quality in Ambo and Guder markets and abattoirs, Oromia Regional State, Ethiopia. Ethiop J Sci Technol. 2021; 14(2):89–104. https://doi.org/10.4314/ejst.v14i2.1
Osti JL, Serrano AB, Saavedra FF, Linares CP, Ruiz-Albarrán M. Evaluación de los componentes del manejo antes, durante y después de la matanza y su asociación con la presencia de carne DFD en bovinos del noreste de México. Rev. Mex. Cienc. Pecu. 2021; 12(3):773-788. https://doi.org/10.22319/rmcp.v12i3.4866
Herrán L, Romero M, Herrán L. Interacción humano-animal y prácticas de manejo bovino en subastas colombianas. Rev Investig Vet del Peru. 2017; 28(3):571–585. https://doi.org/10.15381/rivep.v28i3.13360
Lawrie RA, Ledward DA. Lawrie’s meat science. 7th ed. Cambridge: CRC Press; 2006
Cordoba, C. Correa, G. Barahona, R. Tarazona A. Comportamiento de machos cebú en corrales presacrificio y su relación con el pH de la carne. Arch. Zootec. 66(256):579-586.
Pérez-linares C, Barrera A, Sánchez E, Bárbara S, Figueroa-Saavedra F. Efecto del cambio en el manejo antemortem sobre la presencia de carne DFD en ganado bovino. Rev MVZ Cordoba. 2015; 20(3):4688-4697. https://doi.org/10.21897/rmvz.39
Arik E, Karaca S. The effect of some pre-slaughter factors on meat quality of bulls slaughtered in a commercial abattoir in Turkey. Indian J Anim Res. 2017; 51(3):557–63.
Cobo GC, Romero HM. Importancia de la interacción hombre-animal durante el presacrificio bovino: Revisión. Biosalud. 2012; 11(2):79–91.
Carrasco-García AA, Pardío-Sedas VT, León-Banda GG, Ahuja-Aguirre C, Paredes-Ramos P, Hernández-Cruz BC, et al. Effect of stress during slaughter on carcass characteristics and meat quality in tropical beef cattle. Asian-Australasian J Anim Sci. 2020; 33(10):1656–1665. https://doi.org/10.5713/ajas.19.0804
Clinquart A, Ellies-Oury MP, Hocquette JF, Guillier L, Santé-Lhoutellier V, Prache S. Review: On-farm and processing factors affecting bovine carcass and meat quality. Animal. 2022; 16:100426. https://doi.org/10.1016/j.animal.2021.100426
Kim YHB, Ma D, Setyabrata D, Farouk MM, Lonergan SM, Huff-Lonergan E, et al. Understanding postmortem biochemical processes and post-harvest aging factors to develop novel smart-aging strategies. Meat Sci. 2018; 144:74–90. https://doi.org/10.1016/j.meatsci.2018.04.031
McGilchrist P, Alston CL, Gardner GE, Thomson KL, Pethick DW. Beef carcasses with larger eye muscle areas, lower ossification scores and improved nutrition have a lower incidence of dark cutting. Meat Sci. 2012; 92(4):474–480. https://doi.org/10.1016/j.meatsci.2012.05.014
Young OA, West J, Hart AL, Van Otterdijk FFH. A method for early determination of meat ultimate pH. Meat Sci. 2004; 66(2):493–498. https://doi.org/10.1016/S0309-1740(03)00140-2
Kademi HI, Ulusoy BH, Hecer C. Applications of miniaturized and portable near infrared spectroscopy (NIRS) for inspection and control of meat and meat products. Food Rev Int. 2019; 35(3):201–220. https://doi.org/10.1080/87559129.2018.1514624
Mancini RA, Hunt MC. Current research in meat color. Meat Sci. 2005; 71(1):100-121. https://doi.org/10.1016/j.meatsci.2005.03.003
Li S, Zamaratskaia G, Roos S, Båth K, Meijer J, Borch E, et al. Inter-relationships between the metrics of instrumental meat color and microbial growth during aerobic storage of beef at 4°C. Acta Agric Scand A Anim Sci. 2015; 65(2):97–106.
Hodgen J. Comparison of nix color sensor and nix color sensor pro to standard meat science research colorimeters. Meat Sci. 2016; 112:159. https://doi.org/10.1016/j.meatsci.2015.08.129
Holman BWB, Collins D, Kilgannon AK, Hopkins DL. The e ff ect of technical replicate (repeats) on Nix Pro Color Sensor TM measurement precision for meat : A case-study on aged beef colour stability. Meat Sci. 2018; 135:42–45. https://doi.org/10.1016/j.meatsci.2017.09.001
Holman BWB, Hopkins DL. A comparison of the Nix Colour Sensor ProTM and HunterLab MiniScanTM colorimetric instruments when assessing aged beef colour stability over 72 h display. Meat Sci. 2019; 147:162–165. https://doi.org/10.1016/j.meatsci.2018.09.009
Prieto N, Pawluczyk O, Dugan MER, Aalhus JL. A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products. Appl Spectrosc. 2017; 71(7):1403–1426. https://doi.org/10.1177/0003702817709299
Farmer LJ, Farrell DT. Review: Beef-eating quality: A European journey. Animal. 2018; 12(11):2424–2433. https://doi.org/10.1017/S1751731118001672
Ma J, Sun D, Pu H, Cheng J, Wei Q. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol. 2019; 10:197–220. https://doi.org/10.1146/annurev-food-032818-121155
Tomasevic I, Tomovic V, Milovanovic B, Lorenzo J, Đorđević V, Karabasil N, et al. Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties. Meat Sci. 2019; 148:5–12. https://doi.org/10.1016/j.meatsci.2018.09.015
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