YALÇIN BOZKURT, SERKAN ÖZKAYA, Bürol KILIÇ

Comparison of two different methots to predict meat quality and prediction possibility using digital ımage analysis

Bu çalışmada sığır karkaslarının kas renginin dijital görüntü analizleri yöntemi kullanılarak belirlenmesi amaçlanmış ve 14 adet sığır karkası kullanılmıştır. Kesimden 24 saat sonra bu karkasların Longissimus kası alanından kas rengi ölçümleri (L*, a*, b* değerleri) hem kolorimetre ölçümleri ile hem de dijital görüntü analizi yöntemiyle alınmış ve ayrıca pH ölçümlerini içeren veriler de elde edilmiştir. Analizler sonucunda L*, a*, b* ‘nin dijital görüntü ve kolorimetre değerleri arasında büyük farklılık bulunmuştur (sırasıyla 25.6±3.37, 3.01±3.38 ve 2.25±3.56). L* değerleri arasındaki farklılık istatistiki olarak önemli (P0.05). Ayrıca pH ve a* değeri arasındaki korelasyon (r=0.83) önemli bulunmuştur (P

Anahtar Kelimeler:

karkas kalitesi, et kalitesi, karşılaştırmalar, öngörü, metodoloji, renk, sığır eti

Et kalitesinin tahmininde iki farklı metodun karşılaştırılması ve dijital görüntü analizi yöntemi ile tahmin olasılığı

The objective of this study was to determine muscle colour of beef carcasses using digital image analysis. Fourteen beef carcasses were selected from a slaughterhouse. The data collected on these carcasses included colourmeter measurements and digital images and measurements of muscle colour (L*, a*, b* values) and muscle pH from longissimus muscle at 24 hours after slaughtering. The discrepancies between colourmeter and digital image analysis values of L*, a*, b* were large (25.6±3.37, 3.01±3.38 and 2.25±3.56, respectively). There were significant differences between L* values (P<0.05) but there were non-significant differences between a* and b* values (P>0.05). The correlation coefficient was found significant (P<0.05) between pH and a* values (r=0.83). The results showed that prediction ability of digital image analysis was low for prediction of muscle colour. However, it was concluded that red value (a*) can be predicted by digital image analysis and there is a need for further studies in order to develop better techniques to use for prediction.

Keywords:

carcass quality, meat quality, comparisons, prediction, methodology, colour, beef,

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1. Daley WDR, Babbitt SS: Machine vision: Quality control by computer. Misset World Poultry, 7 (4): 20-21, 1991.
2. McDonald T, Chen YR: Separating connected muscle tissue in images of beef carcass rib eyes. Transactions of the ASAE, 33 (6): 2059-2065, 1990.
3. Cross HR, Belk KE: Objective measurements of carcass and meat quality. Meat Sci, 36, 191-202, 1994.
4. Gardner TL, Dolezal HG, Allen DM: Utilization of Video Image Analysis in Predicting Beef Carcass Lean Product Yields. 1995 Animal Science Research Report-Oklahoma State. pp. 61-67, 1995.
5. Monin G: Recent methods for predicting quality of whole meat. Meat Sci, 49, 231-243, 1998.
6. Albrecht E, Wegner J, Ender K: A new technique for objective evaluation of marbling in beef. Fleischwirtschaft, 4, 11-163, 1996.
7. Irie M, Izumo A, Mohri S: Rapid method of determining water holding capacity in meat using video image analysis and simple formulae. Meat Sci, 42, 95-102, 1996.
8. Hodgson RR, Belk KE, Savell JW, Cross HR, Williams FL: Development of a quantitative quality grading system for mature cow carcasses. J Anim Sci, 70, 1840, 1992.
9. Hilton GG, Tatum JD, Williams SE, Belk KE, Williams FL, Wise JW, Smith GC: An evaluation of current and alternative systems for quality grading carcasses of mature slaughter cows. J Anim Sci, 76, 2094, 1998.
10. Tatum JD, George MH, Belk KE, Smith GC: Development of a "Palatability Assurance Critical Control Points" (PACCP) Model to Reduce the Incidence of Beef Palatability Problems- Final Report to the National Cattlemen’s Beef Association. Colorado State University, Fort Collins, CO. 1997.
11. Wulf DM, O’Connor SF, Tatum JD, Smith GC: Using objective measures of muscle color to predict beef longissimus tenderness. J Anim Sci, 75, 684, 1997.
12. Minitab: Statistical Software Bortland Inc. 2001.
13. Rook AJ, Dhanoa MS, Gill M: Prediction of the voluntary intake of grass silages by beef cattle. 3. Percision of alternative prediction models. Anim Prod, 50, 455-466, 1990.
14. Smoler E, Rook AJ, Sutton JD, Beever DE: Prediction of milk protein concentration from elements of metabolizable protein system. J Dairy Sci, 81, 1619- 1623, 1998.
15. Bozkurt Y, Ap Dewi I: An evaluation of equations based on metabolizable energy and ARC protein schemes to predict liveweight gain of housed beef cattle. Suranaree J Sci Techno, 8, 15-30, 2001.
16. Fuentes-Pila J, Ibanez M, De Miguel JM: Predicting average feed intake of lactating Holstein cows fed totally mixed rations. J Dairy Sci, 86, 309-323, 2003.
17. Yan T, Angnew RE, Murphy JJ, Ferris CP, Gordon FJ: Evaluation of different energy feding system with production data from lactating dairy cows offered grass silage-based diets. J Dairy Sci, 86, 1415-1418, 2003.
18. Bozkurt Y, Ozkaya S: An assessment of the ARC metabolizable energy system to predict live weight gain of Brown Swiss cattle grown under feedlot conditions in Turkey. J Biol Sci, 5 (4): 411-416, 2005.
19. Bozkurt Y: Prediction of body weight from body size measurements in Brown Swiss cattle fed under small-scale farming conditions. J Appl Anim Res, 29, 29-32. 2006.
20. Bozkurt Y: An evaluation of the ME system model to predict performance of different breeds of feedlot beef cattle fed under two different feeding periods. Asian J Anim Vet Adv, 3 (4): 187-196, 2008.
21. Schutte BR, Biju N, Kranzler GA, Dolezal HG: Color video image analysis for augmenting beef carcass grading. Animal Research Report. pp. 32-36. http://www.ansi.okstate. edu/research/1998rr/07.html. 1998. Accessed: 14.10.2006.
22. Dasiewicz K, Slowinski M, Sakowski T, Oprzadek J, Wisnisch A, Dymnicki E, Sloniewski K: The attempt of video image analysis use for estimation of meat quality of beef breeds bulls. Food Science and Technology, 6 (2), 2003. www.ejpau.media.pl/series/volume6/issue2/food/art-12.html. Accessed: 14.10.2006.