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Instrumental texture measurement of meat in a laboratory research and on a production line

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Components of meat texture are especially important features for consumers. The systems with guaranteed repeatable quality must be associated with online, reliable and quick measurements of chosen, critical for consumers, quality features. In case of the texture features, the most important is tenderness. In laboratory conditions it is measured using shear test. However, it is a time-consuming and destructive method without possibility of measurement automation. Hence, in case of online measure¬ments, there is a necessity to use other methods. The most promising methods are near-infrared spectroscopy and computer image analysis, enabling measurement of a lot of features, inter alia texture features.
Słowa kluczowe
Twórcy
autor
  • Division of Engineering in Nutrition, Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS-SGGW), 159C Nowoursynowska Str., 02-776 Warsaw, Poland
autor
  • Department of Dietetics, Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS-SGGW), 159C Nowoursynowska Str., 02-776 Warsaw
  • Division of Engineering in Nutrition, Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS-SGGW), 159C Nowoursynowska Str., 02-776 Warsaw, Poland
  • Division of Engineering in Nutrition, Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS-SGGW), 159C Nowoursynowska Str., 02-776 Warsaw, Poland
  • Division of Engineering in Nutrition, Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS-SGGW), 159C Nowoursynowska Str., 02-776 Warsaw, Poland
Bibliografia
  • 1. Anderson N.M., Walker P.N. Measuring fat content of ground beef stream using on-line visible/NIR spectroscopy. Trans ASAE, 46(1), 2003, 117–124.
  • 2. Basset O., Buquet B., Abouelkaram S., Delachartre P., Culioli J. Application of texture image analysis for the classiffcation of bovine meat. Food Chem¬istry, 69, 2000, 437–445.
  • 3. Behrends J.M., Goodson K.J., Koohmaraie M., Shackelford S.D., Wheeler T.L., Morgan W.W., Reagan J.O., Gwartney B.L., Wise J.W., Savell J.W. Beef customer satisfaction: USDA quality grade and marination effects on consumer evalu¬ations of top round steaks. Journal of Animal Sci¬ence, 83, 2005, 662–670.
  • 4. Brosnan T., Sun D-W. Improving quality inspec¬tion of food products by computer vision – a re¬view. Journal of Food Engineering, 61, 2004, 3–16.
  • 5. Caine W.R., Aalhus J.L., Best D.R., Dugan M.E.R., Jeremiah L.E. Relationship of texture profile anal-ysis and Warner-Bratzler shear force with sensory characteristics of beef rib steaks. Meat Science, 64, 2003, 333–339.
  • 6. Craigie C.R., Navajas E.A., Purchas R.W., Maltin C.A., Bünger L., Hoskin S.O., Ross D.W., Morris S.T., Roehe R. A review of the development and use of video image analysis (VIA) for beef carcass evaluation as an alternative to the current EUROP system and other subjective systems. Meat Sci¬ence, 92(4), 2012, 307-318.
  • 7. Dinh T.N.T. Meat quality: understanding of meat tenderness and influence of fat content on meat fla-vor. Science & Technology Development, 9(12), 2006, 65-70.
  • 8. Guzek D., Głąbska D., Wierzbicka A. Analiza składowych barwy RGB wołowej zrazowej górnej po obróbce cieplnej prowadzonej w piecu kon¬wekcyjno-parowym, na podstawie barwy mięsa surowego. Journal of Research and Applications in Agricultural Engineering, 57(1), 2012a, 55–58.
  • 9. Guzek D., Głąbska D., Wierzbicka A. Zastoso¬wanie komputerowej analizy obrazu do prog¬nozowania barwy mięsa wołowego po obróbce cieplnej na przykładzie łopatki wołowej. Postępy Nauki i Techniki, 2, 2012b, 131–138.
  • 10. Henson S. The process of food quality belief for¬mation from a consumer perspective. W: T. Becker (Ed.), Quality policy and consumer behaviour in the European Union, 2000, pp. 73–89).
  • 11. Henson S., Northen J. Consumer assessment of the safety of beef at the point of purchase: a pan-Eu-ropean study. Journal of Agricultural Economics, 51(1), 2000, 90–105.
  • 12. Huffman, K.L., Miller M F., Hoover L.C., Wu C.K., Brittin H.C., Ramsey C.B. Effect of beef tenderness on consumer satisfaction with steaks consumed in the home and restaurant. Jorunal of Animal Science, 74, 1996, 91–97.
  • 13. Jackman P., Sun D-W., Allen P. Prediction of beef palatability from colour, marbling and surface tex-ture features of longissimus dorsi. Journal of Food Engineering, 96, 2010, 151–165.
  • 14. Jackman P., Sun D-W., Du Ch-J., Allen P. Predic¬tion of beef eating qualities from colour, marbling and wavelet surface texture features using homog¬enous carcass treatment. Pattern Recognition. 42 (5), 2009, 751–763.
  • 15. Liu Y., Lyon B.G., Windham W.R., Realini C.E., Pringle T.D.D., Duckett S. Prediction of color, tex¬ture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study. Meat Science, 65, 2003, 1107–1115.
  • 16. Lyford C., Thompson J., Polkinghorne R., Miller M., Nishimura T., Neath K., Allen P., Belasco E. Is willingness to pay (WTP) for beef quality grades affected by consumer demographics and meat con-sumption preferences? Australasian Agribusiness Review, 18, 2010, 1–17.
  • 17. Park B., Chen Y.R.. Real-time dual-wavelength image processing for poultrysafety inspection. Journal of Food Process Engineering, 23, 2000, 329-351.
  • 18. Rodbotten R., Mevik B.-H., Hildrum K.I. Prediction and classification of tenderness in beef from non-invasive diode array detected NIR spectra. Journal of Near Infrared Spectroscopy, 9, 2001, 199–210.
  • 19. Rust S.R., Price D.M., Subbiah J., Kranzler G., Hil¬ton G.G., Vanoverbeke D.L., Morgan J.B. Predict¬ing beef tenderness using near-infrared spectrosco¬py. Jorunal of Animal Science, 86, 2008, 211–219.
  • 20. Vote D. J., Belk K.E., Tatum J.D., Scanga J.A., Smith G.C. Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module. Jorunal of Animal Science, 81, 2003, 457–465.
  • 21. Wheeler T.L., Shackelford S.D., Koohmaraie M. Standardizing collection and interpretation of War¬ner-Bratzler shear force and sensory tenderness data. Proceedings Reciprocal Meat Conference, 50, 1997b, 68–77.
  • 22. Wheeler T.L., Shackelford S.D., Johnson L.P., Mill¬er M.F., Miller R.K., Koohmaraie M. A comparison of Warner-Bratzler shear force assessment within and among institutions. Journal of Animal Science, 75(9), 1997a, 2423–2432.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4edce7f8-5881-4d79-a83e-eb41daa7e72a
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