Post-mortem identification of meat with abnormal autolysis by non-invasive methods
https://doi.org/10.21323/2414-438X-2025-10-3-226-236
Abstract
The theme under consideration is of great interest for researchers and practical specialists engaged in the development of methods for identification of meat with different courses of autolysis. In this review, modern non-invasive methods for meat quality assessment are presented. The authors describe methods developed for identification of meat with nontraditional course of autolysis, including determination of electric properties of meat (electrical conductivity, electrical resistance, electrical impedance), optical methods (light scattering, reflection, absorption, Raman spectroscopy, fluorescence spectroscopy, visible/near/mid-infrared spectroscopy), investigation of physical parameters of meat (determination of meat color coordinates using spectrocolorimeters, nuclear magnetic resonance, ultrasound spectroscopy and others). The results of studies carried out by various researchers on the use of the proposed methods for meat sorting into quality groups and certainty of the obtained data are presented. It is shown that meat quality can be predicted using the obtained values of electrical parameters and optical spectra. Analysis of published materials shows that up to now there is no definite answer to the question about a choice of a method for identification of meat quality group. This problem requires further research and discussion.
About the Authors
L. S. Kudryashov
V.M. Gorbatov Federal Research Center for Food Systems
Russian Federation
Russian Federation
Leonid S. Kudryashov, Doctor of Technical Sciences, Professor, Chief Researcher
26, Talalikhin str., 109316, Moscow
O. A. Kudryashova
All-Russian Scientific Research Institute of Poultry Processing Industry — Branch of the Federal Scientific Center “All-Russian Research and Technological Poultry Institute” of Russian Academy of Sciences
Russian Federation
Russian Federation
Olga A. Kudryashova, Candidate of Technical Sciences, Leading Researcher, Scientific Laboratory of Normative and Technical Developments and Expertise
Rzhavki township, Moscow region, 142552
References
1. Chizzolini, R., Novelli, E., Badiani, A., Rosa, P., Delbono, G. (1993). Objective measurements of pork quality: Evaluation of various techniques. Meat Science, 34(1), 49–77. https://doi.org/10.1016/0309-1740(93)90018-d
2. Joo, S. T., Kauffman, R. G., Warner, R. D., Borggaard, C., Stevenson-Barry, J. M., Lee, S. et al. (2000). Objectively predicting ultimate quality of post-rigor pork musculature: I. initial comparison of techniques. Asian-Australasian Journal of Animal Sciences, 13(1), 68–76. https://doi.org/10.5713/ajas.2000.68
3. Tatulov, Yu.V., Kuritsyn, N.I., Mittelshtein, T.M., Vjlodchenko, R.V. (August 27–31, Budapest, Hungary, 1979). The effect of post-mortem changes in meat upon its processing characteristics. Proceedings 25th European Meeting of Meat Research Workers. Hungary, 1979.
4. Kudryashov, L.S. (1992). Meat aging and salting. Kemerovo: Kuzbassuzdat, 1992. (In Russian)
5. Silva, J. A., Patarata, L., Martins, C. (1999). Influence of ultimate pH on bovine meat tenderness during ageing. Meat Science, 52, 453–459. https://doi.org/10.1016/s0309-1740(99)00029-7
6. Byrne, C. E., Troy, D. J., Buckley, D. J. (2000). Postmortem changes in muscle electrical properties of bovine M. longissimus dorsi and their relationship to meat quality attributes and pH fall. Meat Science, 54(1), 23–34. https://doi.org/10.1016/s0309-1740(99)00055-8
7. Lepetit, J., Damez, J.-L., Moreno, M.V., Clerjon, S., Favier, R. (August 26–31, 2001). Electrical anisotropy of beef meat during ageing. Proceedings of 47th international congress of meat science and technology. Kraków. Poland, 2001.
8. Grossi, M., Riccò, B. (2017). Electrical impedance spectroscopy (EIS) for biological analysis and food characterization: A review. Journal of Sensors and Sensor Systems, 6(2), 303–325. https://doi.org/10.5194/jsss-6-303-2017
9. Scheier, R., Scheeder, M., Schmidt, H. (2015). Prediction of pork quality at the slaughter line using a portable Raman device. Meat Science, 103, 96–103. https://doi.org/10.1016/j.meatsci.2015.01.009
10. Andersen, P. V., Wold, J. P., Gjerlaug-Enger, E., Veiseth-Kent, E. (2018). Predicting post-mortem meat quality in porcine longissimus lumborum using Raman, near infrared and fluorescence spectroscopy. Meat Science, 145, 94–100. https://doi.org/10.1016/j.meatsci.2018.06.016
11. Brøndum, J., Munck, L., Henckel, P., Karlsson, A., Tornberg, E., Engelsen, S. B. (2000). Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy. Meat Science, 55(2), 177–185. https://doi.org/10.1016/s0309-1740(99)00141-2
12. Hoving-Bolink, A. H., Vedder, H. W., Merks, J. W. M., de Klein, W. J. H., Reimert, H. G. M., Frankhuizen, R. et al. (2005). Perspective of NIRS measurements early post mortem for prediction of pork quality. Meat Science, 69(3), 417–423. https://doi.org/10.1016/j.meatsci.2004.08.012
13. Barbin, D. F., ElMasry, G., Sun, D.-W., Allen, P. (2012). Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Analytica Chimica Acta, 719, 30–42. https://doi.org/10.1016/j.aca.2012.01.004
14. Andersen, P. V., Veiseth-Kent, E., Wold, J. P. (2017). Analyzing pH-induced changes in a myofibril model system with vibrational and fluorescence spectroscopy. Meat Science, 125, 1–9. https://doi.org/10.1016/j.meatsci.2016.11.005
15. Samuel, D., Trabelsi, S., Karnuah, A. B., Anthony, N. B., Aggrey, S. E. (2012). The use of dielectric spectroscopy as a tool for predicting meat quality in poultry. International Journal of Poultry Science, 11(9), 551–555. https://doi.org/10.3923/ijps.2012.551.555
16. Font-i-Furnols, M., Fulladosa, E., Povše M. P., Čandek-Potokar, M. (2015). Future trends in non-invasive technologies suitable for quality determinations. Chapter in a book: A Handbook of Reference Methods for Meat Quality Assessment. European Cooperation in Science and Technology, Edinburgh, 2015.
17. Delgado-Pando, G., Allen, P., Troy, D. J., McDonnell, C. K. (2021). Objective carcass measurement technologies: Latest developments and future trends. Trends in Food Science and Technology, 111, 771–782. https://doi.org/10.1016/j.tifs.2020.12.016
18. Álvarez Alonso, R., Valera Córdoba, M., Alcalde Aldea, M. J. (2014). Normal vs. DFD beef: Evaluation by a consumer panel and comparison by pH and color. Informacion Tecnica Economica Agraria, 110(4), 368–373. https://doi.org/10.12706/itea.2014.023 (In Spanish)
19. Łyczyński, A., Pospiech, E., Rzosińska, E., Czyżak-Runowska, G., Mikołajczak, B., Iwańska, E., Ślósarz, P. (2005). Correlations between electrical conductivity and quality parameters of pork meat. Annals of Animal Science. Supplement, 2, 99–102.
20. Czyżak-Runowska, G., Łyczyński, A., Pospiech, E., Koćwin-Podsiadła M., Wojtczak J., Rzosińska E. et al. (2010). Electrical conductivity as an indicator of pork meat quality. Journal of Central European Agriculture, 11(1), 105–112.
21. Sieczkowska, H., Antosik, K., Krzęcio-Nieczyporuk, E., Zybert, A., Koćwin-Podsiadła, M. (2013). Usefulness of selected parameters determined 45 minutes post mortem in Longissimus lumborum muscle to evaluate pork quality. Food. Science. Technology. Quality, 2(87), 51–60. (In Polish)
22. Antosik, K., Krzęcio, E., Koćwin-Podsiadła, M., Zybert, A., Sieczkowska, H., Miszczuk, B. et al. (2003). The correlation between the electrical conductivity and some selected pork meat quality properties. Food. Science. Technology. Quality, 4(37 Supl), 11–21. (In Polish)
23. Oliver, M. À., Gobantes, I., Arnau, J., Elvira, J., Riu, P., Grèbol, N. et al. (2001). Evaluation of the electrical impedance spectroscopy (EIS) equipment for ham meat quality selection. Meat Science, 58(3), 305–312. https://doi.org/10.1016/s0309-1740(01)00033-x
24. Guerrero, L., Gobantes, I., Oliver, M. À., Arnau, J., Dolors Guàrdia, M., Elvira, J. et al. (2004). Green hams electrical impedance spectroscopy (EIS) measures and pastiness prediction of dry cured hams. Meat Science, 66(2), 289–294. https://doi.org/10.1016/s0309-1740(03)00101-3
25. Aleynikov, A.F., Chuguy, Yu.V., Glyanenko, V.S., Palchikova, I.G. (October 10–11, 2012). The use of the impedance spectrometry method in assessment of quality of meat raw materials. Proceedings of the 5 th International scientific-practical conference “Information technologies, systems and apparatus in AIC” Novosibirsk, Siberian Physico-Technical Institute of Agrarian Problems of Rossekhozacademia, 2012. (In Russian)
26. Feldhusen, F., Neumann-Fuhrmann, D., Wenzel, S. (1987). Die leitfähigkeit als parameter der fleischbeschaffenheit. Fleischwirtschaft, 67, 455–460. (In German)
27. Pliquett, U., Altmann, M., Pliquett, F., Schöberlein, L. (2003). P y – a parameter for meat quality. Meat Science, 65(4), 1429–1437. https://doi.org/10.1016/s0309-1740(03)00066-4
28. Egelandsdal, B., Abie, S. M., Bjarnadottir, S., Zhu, H., Kolstad, H., Bjerke, F. et al. (2019). Detectability of the degree of freeze damage in meat depends on analytic-tool selection. Meat Science, 152, 8–19. https://doi.org/10.1016/j.meatsci.2019.02.002
29. Suliga, P., Abie, S. M., Egelandsdal, B., Alvseike, O., Johny, A., Kathiresan, P. et al. (2022). Beyond standard PSE testing: An exploratory study of bioimpedance as a marker for ham defects. Meat Science, 194, Article 108980. https://doi.org/10.1016/j.meatsci.2022.108980
30. Kristensen, L., Purslow, P. P. (2001). The effect of ageing on the water-holding capacity of pork: Role of cytoskeletal proteins. Meat Science, 58(1), 17–23. https://doi.org/10.1016/s0309-1740(00)00125-x
31. Borzuta, K., Borys, A., Grześkowiak, E., Strzelecki, J., Lisiak, D., Rogalski, J. (August 8–13, 2004). Studies on changes of some selected pork meat quality traits in relation to the time after slaughter. Proceedings of 50th international congress of meat science and technology. Helsinki, Finland, 2004.
32. Czyżak-Runowska, G., Łyczyński, A., Pospiech, E., Koć winpodsiadła, M., Wojtczak, J., Rzosińska, E. et al. (2010). Electrical conductivity as an indicator of pork meat quality. Journal of Central European Agriculture, 11(1), 105–112. http://doi.org/10.5513/JCEA01/11.1.826
33. Łyczyński, A., Runowska, G., Pospiech, E., Koćwin-Podsiadła, M., Wojtczak, J., Rzosińska, E. et al. (2009). Estimation of selected porcine meat quality indicators on the basis of electrical conductivity measured 24 hours post-slaughter. Animal Science Papers and Reports, 27(1), 51–58.
34. Castro-Giráldez, M., Botella, P., Toldrá, F., Fito, P. (2010). Low-frequency dielectric spectrum to determine pork meat quality. Innovative Food Science and Emerging Technologies, 11(2), 376–386. https://doi.org/10.1016/j.ifset.2010.01.011
35. Jukna, V., Jukna, Č., Pečiulaitienė, N. (2012). Electrical conductivity of pig meat and its relation with quality. Veterinarija ir zootechnika, 57(79), 18–21.
36. Zakharov, A.N., Sus, E.B. (2011). Electrical conductivity of meat. Vsyo o Myase, 5, 48–50. (In Russian)
37. Zakharov, A.N., Sus, E.B. (2011). Method for determining meat quality by its electrical conductivity. Proceedings of the 9th International conference of students and young scientists. Мoscow: VNIIMP, 2011. (In Russian)
38. Richter P. (1985). Erfassung postmortalen Veränderungen am Muskuls l. dorsi von Schwein. Fischwirtschaft, 39(12), 228–230. (In German)
39. Damez, J.-L., Clerjon, S. (2008). Meat quality assessment using biophysical methods related to meat structure. Meat Science, 80(1), 132–149. https://doi.org/10.1016/j.meatsci.2008.05.039
40. Nache, M., Hinrichs, J., Scheier, R., Schmidt, H., Hitzmann, B. (2016). Prediction of the pH as indicator of porcine meat quality using Raman spectroscopy and metaheuristics. Chemometrics and Intelligent Laboratory Systems, 154, 45–51. https://doi.org/10.1016/j.chemolab.2016.03.011
41. Anisimovich, P. V., Pochinok, T. B., Tokareva, E. V. (2017). Spectrophotometric determination of proteins in biological fluids. Journal of Analytical Chemistry, 72(12), 1212–1218. https://doi.org/10.1134/s106193481712002
42. Raman, C. V., Krishnan, K. S. (1928). A new type of secondary radiation. Nature, 121(3048), 501–502. https://doi.org/10.1038/121501c0
43. Landsberg, G.S., Mandelshtamm, L.I. (1928). A new phenomenon upon light scattering (preliminary report). Journal of Russian Physico-Chemical Society, 60, 335–338. (In Russian)
44. Herrero, A. M. (2008). Raman spectroscopy for monitoring protein structure in muscle food systems. Critical Reviews in Food Science and Nutrition, 48(6), 512–523. https://doi.org/10.1080/10408390701537385
45. Scheier, R., Köhler, J., Schmidt, H. (2014). Identification of the early postmortem metabolic state of porcine M. semimembranosus using Raman spectroscopy. Vibrational Spectroscopy, 70, 12–17. https://doi.org/10.1016/j.vibspec.2013.10.001
46. Bauer A., Petzet A., Schwägele F., Scheier R., Schmidt H. (August 18–23, 2013). Towards an online assessment of meat in pork. Proceedings of 59th international congress of meat science and technology. Izmir, Turkey, 2013.
47. Li-Chan, E., Nakai, S., Hirotsuka, M. (1994). Raman spectroscopy as a probe of protein structure in food systems. Chapter in a book: Protein structure. Function relationships in foods. Springer New York, NY, 1994. https://doi.org/10.1007/978-1-4615-2670-48
48. Beattie, R. J., Bell, S. J., Farmer, L. J., Moss, Bruce. W., Patterson, D. (2004). Preliminary investigation of the application of Raman spectroscopy to the prediction of the sensory quality of beef silverside. Meat Science, 66(4), 903–913. https://doi.org/10.1016/j.meatsci.2003.08.012
49. Beattie, J. R., Bell, S. E. J., Borggaard, C., Moss, B. W. (2008). Preliminary investigations on the effects of ageing and cooking on the Raman spectra of porcine longissimus dorsi. Meat Science, 80(4), 1205–1211. https://doi.org/10.1016/j.meatsci.2008.05.016
50. Schmidt, H., Scheier, R., Hopkins, D. L. (2013). Preliminary investigation on the relationship of Raman spectra of sheep meat with shear force and cooking loss. Meat Science, 93(1), 138–143. https://doi.org/10.1016/j.meatsci.2012.08.019
51. Beattie, J. R., Bell, S. E. J., Borgaard, C., Fearon, A., Moss, B. W. (2006). Prediction of adipose tissue composition using Raman spectroscopy: Average properties and individual fatty acids. Lipids, 41(3), 287–294. https://doi.org/10.1007/s11745-006-5099-1
52. Schmidt, H., Sowoidnich, K., Kronfeldt, H.-D. (2010). A prototype hand-held Raman sensor for the in situ characterization of meat quality. Applied Spectroscopy, 64(8), 888–894. https://doi.org/10.1366/000370210792081028
53. Scheier, R., Scheeder, M., Schmidt, H. (2015). Prediction of pork quality at the slaughter line using a portable Raman device. Meat Science, 103, 96–103. https://doi.org/10.1016/j.meatsci.2015.01.009
54. Andersen, P. V., Veiseth-Kent, E., Wold, J. P. (2017). Analyzing pH-induced changes in a myofibril model system with vibrational and fluorescence spectroscopy. Meat Science, 125, 1–9. https://doi.org/10.1016/j.meatsci.2016.11.005
55. Beganović, A., Hawthorne, L. M., Bach, K., Huck, C. W. (2019). Critical review on the utilization of handheld and portable Raman spectrometry in meat science. Foods, 8(2), Article 49. https://doi.org/10.3390/foods8020049
56. Osborne, B. G. (2000). Near‐Infrared Spectroscopy in Food Analysis. Encyclopedia of Analytical Chemistry. John Wiley and Sons, Ltd. 2000. https://doi.org/10.1002/9780470027318.a1018
57. Cozzolino, D., Murray, I. (2002). Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy. Journal of Near Infrared Spectroscopy, 10(1), 37–44. https://doi.org/10.1255/jnirs.319
58. Barlocco, N., Vadell, A., Ballesteros, F., Gallietta, G., Cozzolino, D. (August 31-September 21, 2003). Determination of quality characteristics in pork muscles by visible and near infrared reflectance spectroscopy. Proceedings of 49th international congress of meat science and technology. Brazil, 2003.
59. Sahar, A., Dufour, É. (2015). Classification and characterization of beef muscles using front-face fluorescence spectroscopy. Meat Science, 100, 69–72. https://doi.org/10.1016/j.meatsci.2014.09.142
60. García-Rey, R. M., García-Olmo, J., De Pedro, E., Quiles-Zafra, R., Luque de Castro, M. D. (2005). Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy using a fiber optic probe. Meat Science, 70(2), 357–363. https://doi.org/10.1016/j.meatsci.2005.02.001
61. Geesink, G. H., Schreutelkamp, F. H., Frankhuizen, R., Vedder, H. W., Faber, N. M., Kranen, R. W. et al. (2003). Prediction of pork quality attributes from near infrared reflectance spectra. Meat Science, 65(1), 661–668. https://doi.org/10.1016/s0309-1740(02)00269-3
62. Barlocco, N., Vadell, A., Ballesteros, F., Galietta, G., Cozzolino, D. (2006). Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Animal Science, 82(1), 111–116. https://doi.org/10.1079/asc20055
63. Kapper, C., Klont, R. E., Verdonk, J. M. A. J., Williams, P. C., Urlings, H. A. P. (2012). Prediction of pork quality with near infrared spectroscopy (NIRS) 2. Feasibility and robustness of NIRS measurements under production plant conditions. Meat Science, 91(3), 300–305. https://doi.org/10.1016/j.meatsci.2012.02.006
64. Neyrinck, E., Lescouhier, S., Vermeulen, L., Telleir, D., Fraeye, D., Paelinck, H. et al. (18–23rd August 2013). Use of near infrared spectroscopy as selection tool for PSE pork. Proceedings of 59th international congress of meat science and technology. Izmir, Turkey, 2013.
65. Kennedy, C., Moss, B., Farmer, L., Fearon, A., Beattie, A., Birnie, J. et al. (18–23rd August 2013). The ability of near infrared spectroscopy (NIR) to discriminate seasonal and influences on lamb meat quality. Proceedings of 59th international congress of meat science and technology. Izmir, Turkey, 2013.
66. Pankratova, K.G., Schelokov, V.I., Plechanova, L.D., Potasheva, E.V. (1998). Express determination of meat and meat products quality by method of IR-spectroscopy. Meat Industry, 3, 38–40. (In Russian)
67. Monroy, M., Prasher, S., Ngadi, M. O., Wang, N., Karimi, Y. (2010). Pork meat quality classification using Visible/Near-Infrared spectroscopic data. Biosystems Engineering, 107(3), 271–276. https://doi.org/10.1016/j.biosystemseng.2010.09.006
68. Prieto, N., Roehe, R., Lavín, P., Batten, G., Andrés, S. (2009). Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Science, 83(2), 175–186. https://doi.org/10.1016/j.meatsci.2009.04.016
69. Cafferky, J., Sweeney, T., Allen, P., Sahar, A., Downey, G., Cromie, A. R. et al. (2020). Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M. longissimus thoracis et lumborum. Meat Science, 159, Article 107915. https://doi.org/10.1016/j.meatsci.2019.107915
70. Gariepy, C., Amiot, J., Nadai, S. (1989). Ante-mortem detection of PSE and DFD by infrared thermography of pigs before stunning. Meat Science, 25(1), 37–41. https://doi.org/10.1016/0309-1740(89)90064-8
71. Savenije, B., Geesink, G. H., van der Palen, J. G. P., Hemke, G. (2006). Prediction of pork quality using visible/near-infrared reflectance spectroscopy. Meat Science, 73(1), 181–184. https://doi.org/10.1016/j.meatsci.2005.11.006
72. De Marchi, M., Berzaghi, P., Boukha, A., Mirisola, M., Galol, L. (2007). Use of near infrared spectroscopy for assessment of beef quality traits. Italian Journal of Animal Science, 6(sup1), 421–423. https://doi.org/10.4081/ijas.2007.1s.421
73. Prieto, N., Andrés, S., Giráldez, F. J., Mantecón, A. R., Lavín, P. (2008). Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. Meat Science, 79(4), 692–699. https://doi.org/10.1016/j.meatsci.2007.10.035
74. Prevolnik, M., Čandek-Potokar, M., Novič, M., Škorjanc, D. (2009). An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks. Meat Science, 83(3), 405–411. https://doi.org/10.1016/j.meatsci.2009.06.015
75. Cecchinato, A., De Marchi, M., Penasa, M., Albera, A., Bittante, G. (2011). Near-infrared reflectance spectroscopy predictions as indicator traits in breeding programs for enhanced beef quality. Journal of Animal Science, 89(9), 2687–2695. https://doi.org/10.2527/jas.2010-3740
76. Ripoll, G., Albertí, P., Panea, B., Olleta, J. L., Sañudo, C. (2008). Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. Meat Science, 80(3), 697–702. https://doi.org/10.1016/j.meatsci.2008.03.009
77. Leroy, B., Lambotte, S., Dotreppe, O., Lecocq, H., Istasse, L., Clinquart, A. (2004). Prediction of technological and organoleptic properties of beef Longissimus thoracis from nearinfrared reflectance and transmission spectra. Meat Science, 66(1), 45–54. https://doi.org/10.1016/s0309-1740(03)00002-0
78. Čandek-Potokar, M., Prevolnik, M., Škrlep, M. (2006). Ability of near infrared spectroscopy to predict pork technological traits. Journal of Near Infrared Spectroscopy, 14(4), 269–277. https://doi.org/10.1255/jnirs.644
79. Brøndum, J., Byrne, D. V., Bak, L. S., Bertelsen, G., Engelsen, S. B. (2000). Warmed-over flavour in porcine meat — a combined spectroscopic, sensory and chemometric study. Meat Science, 54(1), 83–95. https://doi.org/10.1016/s0309-1740(99)00085-6
80. Yam, K. L., Papadakis, S. E. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering, 61(1), 137–142. https://doi.org/10.1016/s0260-8774(03)00195-x
81. McDarrah, G.S., McDarrah, F.W., Timothy, S. McDarrah, T.S. (1999). The Photography Encyclopedia. Publisher Schirmer Books, 1999.
82. Klettner, P.G., Stiebing, A. (1980). Beitrag zur Bestimmung der Farbe bei Fleisch und Fleischerzeugnissen. I. Einführung in die Grundlagen der Farbmessung. Fleischwirtschaft. 60(11), 1970–1976. (In German)
83. Chizzolini, R., Novelli, E., Badiani, A., Rosa, P., Delbono, G. (1993). Objective measurements of pork quality: Evaluation of various techniques. Meat Science, 34(1), 49–77. https://doi.org/10.1016/0309-1740(93)90018-d
84. Kudryashov, L.S., Gurinovich, G.V. (1998). Changes in color characteristics of meat during its technological processing. Review information. Moscow, AgroNIITEIMMP, 1998. (In Russian)
85. Swatland, H. J. (1988). Selection of wavelengths at which to measure paleness in pork by fiber-optic spectrophotometry. Canadian Institute of Food Science and Technology Journal, 21(5), 494–500. https://doi.org/10.1016/s0315-5463(88)71029-9
86. Kudryashov, L.S., Gurinovich, G.V., Potipaeva, N.N., Katselashvili, D.V. (1998). Objective assessment of meat and meat product quality by photometric methods. The 25 th anniversary of Kemerovo Technological Institute of Food Industry: achievements, problems, prospects. Proceedings. Part 1. Kemerovo, KemTIPP, 1998. (In Russian)
87. Kudryashov, L.S., Gurinovich, G.V. (1998). Colorimetric quality control of meat and meat products. Meat Industry, 5, 17–19. (In Russian)
88. McKee, S.R., Sams, A. R. (1998). Rigor mortis development at elevated temperatures induces pale exudative turkey meat characteristics. Poultry Science, 77(1), 169–174. https://doi.org/10.1093/ps/77.1.169
89. Owens, C. M., Matthews, N. S., Sam, A. R. (2000). The use of halothane gas to identify turkeys prone to developing pale, exudative meat when transported before slaughter. Poultry Science, 79(5), 789–795. https://doi.org/10.1093/ps/79.5.789
90. Kudryashov. L.S., Gurinovich, G.V., Potipaeva, N. N. Method for meat quality control. Patent RF, no. 2092836, 1997. (In Russian)
91. Tomasevic, I. B. (2018). Computer vision system for color measurements of meat and meat products: A review. Theory and Practice of Meat Processing, 3(4), 4–15. https://doi.org/10.21323/2414-438x-2018-3-4-4-15
92. Sun, X., Young, J., Liu, J., Newman, D. (2018). Prediction of pork loin quality using online computer vision system and artificial intelligence model. Journal of Animal Science, 96(suppl 3), 274–275. https://doi.org/10.1093/jas/sky404.602
93. Girolami, A., Napolitano, F., Faraone, D., Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science, 93(1), 111–118. https://doi.org/10.1016/j.meatsci.2012.08.010
94. Chmiel, M., Słowiński, M., Dasiewicz, K., Florowski, T. (2012). Application of a computer vision system to classify beef as normal or dark, firm, and dry. Journal of Animal Science, 90(11), 4126–4130. https://doi.org/10.2527/jas.2011-5022
95. Chmiel, M., Słowiński, M., Dasiewicz, K., Florowski, T. (2016). Use of computer vision system (CVS) for detection of PSE pork meat obtained from m. semimembranosus. LWT, 65, 532–536. https://doi.org/10.1016/j.lwt.2015.08.021
96. Sun, X., Young, J., Liu, J.H., Bachmeier, L., Somers, R.M., Chen, K.J. et al. (2016). Prediction of pork color attributes using computer vision system. Meat Science, 113, 62–64. https://doi.org/10.1016/j.meatsci.2015.11.009
97. Chmiel, M., Słowinski, M. (2016). The use of computer vision system to detect pork defects. LWT, 73, 473–480. https://doi.org/10.1016/j.lwt.2016.06.054
98. Modzelewska-Kapituła, M., Jun, S. (2022). The application of computer vision systems in meat science and industry — A review. Meat Science, 192, Article 108904. https://doi.org/10.1016/j.meatsci.2022.108904
99. Kay, L. E. (2005). NMR studies of protein structure and dynamics. Journal of Magnetic Resonance, 173(2), 193–207. https://doi.org/10.1016/j.jmr.2004.11.021
100. Söbeli, C., Kayaardı, S. (2014). New techniques for predicting meat quality. GIDA, 39(4), 251–258.
101. Parlak, Y., Güzeler, N. (2016). Nuclear magnetic resonance spectroscopy applications in foods. Current Research in Nutrition and Food Science, 4(SI), 161–168. https://doi.org/10.12944/CRNFSJ.4
102. Renou, J. P., Monin, G., Sellier, P. (1985). Nuclear magnetic resonance measurements on pork of various qualities. Meat Science, 15(4), 225–233. https://doi.org/10.1016/0309-1740(85)90078-6
103. Decanniere, C., Van Hecke, P., Vanstapel, F., Ville, H., Geers, R. (1993). Metabolic response to halothane in piglets susceptible to malignant hyperthermia: An in vivo 31P-NMR study. Journal of Applied Physiology, 75(2), 955–962. https://doi.org/10.1152/jappl.1993.75.2.955
104. Borowiak, P. (August 24–29,1986). The identification of normal and watery pork by pulsed nuclear magnetic resonance measurements. Proceedings 32nd European Meeting of Meat Research Workers. Ghent, Belgium, 1986.
105. Miri, A., Talmant, A., Renou, J. P., Monin, G. (1992). 31P NMR study of post mortem changes in pig muscle. Meat Science, 31(2), 165–173. https://doi.org/10.1016/0309-1740(92)90036-4
106. Bogner , P., Berényi, E., Miseta, A., Horn, P., Kellermayer, M, Wheatley, D.N., Jolesz, F.A. (1996). Nuclear magnetic resonance relaxation parameters of muscle in malignant hyperthermia-susceptible swine. Academic Radiology, 3(1), 26–30. https://doi.org/10.1016/s1076-6332(96)80328-x
107. Li, C., Liu, D., Zhou, G., Xu, X., Qi, J., Shi, P. et al. (2012). Meat quality and cooking attributes of thawed pork with different low field NMR T21. Meat Science, 92(2), 79–83. https://doi.org/10.1016/j.meatsci.2011.11.015
108. Jensen, S.A.K., Munck, L., Sigsgaard, P., Huss, H.H. (1986). Patent United States US4631413A. Method for quality control of products from fish, cattle, swine and poultry.
109. Mansoor, S. E., DeWitt, M. A., Farrens, D. L. (2010). Distance mapping in proteins using fluorescence spectroscopy: The tryptophan-induced quenching (TrIQ) method. Biochemistry, 49(45), 9722–9731. https://doi.org/10.1021/bi100907m
110. Selci, S. (2019). The future of hyperspectral imaging. Journal of Imaging, 5(11), Article 84. https://doi.org/10.3390/jimaging5110084
111. Chang, C.-I. (2003). Hyperspectral Imaging. Spectral detection and classification methods. Springer Scientific and Business Media, US, 2003.
112. Huang, Q., Chen, Q., Li, H., Huang, G., Ouyang, Q., Zhao, J. (2015). Non-destructively sensing pork’s quality indicators using near infrared multispectral imaging technique. Journal of Food Engineering, 154, 69–75. https://doi.org/10.1016/j.jfoodeng.2015.01.006
113. Barbin, D., Elmasry, G., Sun, D.-W., Allen, P. (2012). Nearinfrared hyperspectral imaging for grading and classification of pork. Meat Science, 90(1), 259–268. https://doi.org/10.1016/j.meatsci.2011.07.011
114. Qiao, J., Wang, N., Ngadi, M. O., Gunenc, A., Monroy, M., Gariépy, C. et al. (2007). Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. Meat Science, 76(1), 1–8. https://doi.org/10.1016/j.meatsci.2006.06.031
115. El Karam, S.A., Berge, P., Culioli, J. (1997). Application of ultrasonic data to classify bovine muscles. Ultrasonics Symposium Proceedings. An International Symposium. Toronto, Cánada, 1997. https://doi.org/10.1109/ULTSYM.1997.661793
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Kudryashov L.S., Kudryashova O.A. Post-mortem identification of meat with abnormal autolysis by non-invasive methods. Theory and practice of meat processing. 2025;10(3):226-236. https://doi.org/10.21323/2414-438X-2025-10-3-226-236
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