The analysis of selected physical and technological parameters of pork quality depending on intesity of the pigs growth in fattening
Notice bibliographique
Résumé
Article Details: Received: 2020-10-30 | Accepted: 2020-11-27 | Available online: 2021-01-31 https://doi.org/10.15414/afz.2021.24.mi-prap.71-74 The experiment was conducted to compare the differences in the physical and technological quality of pork meat with different growth intensity. The pigs were divided in accordance with the average daily gain values as followed: a) the fast-growing group (R1> AGD + 0.5 SD), b) the medium-fast-growing group (R2= AGD ± 0.5 SD) and c) slow-growing group (R3< AGD - 0.5 SD). For group of gilts, we found a statistically significant difference (P ⤠0.05) in the drip loss value between the fast-growing group and the medium-growing group and the fast-growing group compared to the slow-growing group of gilts. Between the fast-growing group and the medium-growing group, as well as between the fast-growing group and the slow-growing group of gilts, the differences in shear force value were statistically highly significant at the level of P ⤠0.01. At the same time, in the colour of meat in redness              (a* value) were found statistically significant differences between groups of barrows according to the growth rate at the level of P ⤠0.05 and between fast and slow-growing gilts at the level of P ⤠0.01 and medium and slow-growing gilts at the level of P ⤠0.05. In addition, in the meat yellowness (b*) we also determined a statistically highly significant difference at the level of P ⤠0.01 between the fastest-growing group and medium fast-growing barrows and a significant difference at the level of P ⤠0.05 between the fast and slow-growing group of gilts. Keywords: fattening pigs, growth intensity in pig, pork quality References Brocks, L. et al. (1998). Histochemical characteristics in relation to meat quality properties in the Longissimus lumborum of fast and lean growing lines of Large White pigs. Meat Science , 50(4), 411â420. DOI: 10.1016/s0309-1740(98)00053-9 Correa, J.A. et al. (2006). Effects of slaughter weight on carcass composition and meat quality in pigs of two different growth rates. Meat Science , 72(1), 91â99. DOI: 10.1016/j.meatsci.2005.06.006 Duan, Y. et al. (2018). Effects of slaughter weight and growth rate on the longissimus muscle metabolic characteristics, and pork sensory quality in pigs of two sexes. Canadian Journal of Animal S cience, 98(2), 213â220. https://doi.org/10.1139/cjas-2017-0032 Georgsson, L. and Svendsen. J. (2002). Degree of competition at feeding differentially affects behavior and performance of group-housed growing-finishing pigs of different relative weights. Journal of Animal Science , 80(2), 376â383. https://doi.org/10.2527/2002.802376x He, Y. et al. (2016). Identifying factors contributing to slow growth in pigs. Journal of Animal Science , 94(5), 2103â2116. https://doi.org/10.2527/jas.2015-0005 Hovenier, R. (1993). Breeding for meat quality in pigs . Landbouwuniversiteit: Wageningen University & Research. Latorre, M.A. et al. (2008). The relationship within and between production performance and meat quality characteristics in pigs from three different genetic lines. Livestock Science ,  115(2â3 ), 258â267. https://doi.org/10.1016/j.livsci.2007.08.013 Li, Y. 2015. Indicators of Slow Growing Pigs. Swine Scientist. Retrieved October 10, 2020 from https://wcroc.cfans.umn.edu/sites/wcroc.cfans.umn.edu/files/indicators_of_slow_growing_pigs_2015.pdf Nissen, P.M. et al. (2004). Within litter variation in muscle fiber characteristics, pig performance, and meat quality traits. Journal of Animal Science , 82(2), 414â421. · DOI: 10.2527/2004.822414x Nissen, P.M. et al. (2009). Pig meat quality predicted by growth rate at farm level. Acta Agriculturae Scandinavica, Section A â Animal Science , 59(3), 167â172. https://doi.org/10.1080/09064700903254265 Oksbjerg, N. et al. (2000). Long-term changes in performance and meat quality of Danish Landrace pigs: a study on a current compared with an unimproved genotype. Animal Science , 71(Part: 1), 81â92. Quentin, M. et al. (2003). Growth, carcass composition and meat quality response to dietary concentrations in fast-, medium-and slow-growing commercial broilers. Animal Research, 52(1), 65-77. DOI: 10.1051/animres:2003005 Quiniou, N. et al. (2002). Variation of pigletsâ birth weight and consequences on subsequent performance. Livestock Production Science , 78(1), 63â70. DOI: 10.1016/S0301-6226(02)00181-1 Stupka, R. et al. (2013). Chov zvÃÅat . Praha : Powerprint. Suzuki, K. et al. (2005). Genetic parameter estimates of meat quality traits in Duroc pigs selected for average daily gain, longissimus muscle area, backfat thickness, and intramuscular fat content. Journal of Animal Science , 83(9), 2058â2065. DOI: 10.2527/2005.8392058x Wagner, C. (2007). Influence of selection for improved growth rate on pork quality. Iowa: Iowa State University. Wright, Ch. (2017). Variation in Pig Growth Rate and Live Weight. The pig site. Retrieved October 10, 2020 from https://www.thepigsite.com/articles/variation-in-pig-growth-rate-and-live-weight Zammerini, D. et al. (2009). Effect of pig growth rate and health status on meat eating quality. Cambridge University Press, 2009(1), 103. DOI: https://doi.org/10.1017/S1752756200029422
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».