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The analysis of selected physical and technological parameters of pork quality depending on intesity of the pigs growth in fattening

2021· article· en· W3132049496 on OpenAlexaboutno aff
Ondřej Bučko

Bibliographic record

VenueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsnot available
FundersEuropean Regional Development FundKultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR
KeywordsAnimal scienceSignificant differenceGrowth rateBiologyMedicineMathematicsInternal medicine

Abstract

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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), 411–420. 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), 91–99. 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), 213–220. 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), 376–383. 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), 2103–2116. 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(2–3 ), 258–267. 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), 414–421. · 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 – Animal Science , 59(3), 167–172. 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), 81–92. 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 piglets’ birth weight and consequences on subsequent performance. Livestock Production Science , 78(1), 63–70. DOI: 10.1016/S0301-6226(02)00181-1 Stupka, R. et al. (2013). Chov zvířat . 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), 2058–2065. 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.011
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.289
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
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