The Use of Chosen Physicochemical Indicators for Estimation of Pork Meat Quality
Abstract
:1. Introduction
2. Material and Methods
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
- Cummins, A.M.; Olynk Widmar, N.J.; Croney, C.C.; Fulton, J.R. Understanding consumer pork attribute preferences. Theor. Econ. Lett. 2016, 6, 166–177. [Google Scholar] [CrossRef]
- Liu, C.X.; Xiao, Y.P.; Hu, D.W.; Liu, J.X.; Chen, W.; Ren, D.X. The safety evaluation of chilled pork from online platform in China. Food Control 2019, 96, 244–250. [Google Scholar] [CrossRef]
- OECD/FAO. OECD-FAO Agricultural Outlook 2022–2031; OECD Publishing: Paris, France, 2022. [Google Scholar]
- Wrona, M.; Lours, J.; Salafranca, J.; Joly, C.; Nerín, C. Innovative Surface-Enhanced Raman Spectroscopy Method as a Fast Tool to Assess the Oxidation of Lipids in Ground Pork. Appl. Sci. 2023, 13, 5533. [Google Scholar] [CrossRef]
- Terlouw, C.; Picard, B.; Deis, V.; Berri, C.; Hocquette, J.F.; Lebret, B.; Lefèvre, F.; Hamill, R.; Gagaoua, M. Understanding the Determination of Meat Quality Using Biochemical Characteristics of the Muscle: Stress at Slaughter and Other Missing Keys. Foods 2021, 10, 84. [Google Scholar] [CrossRef] [PubMed]
- Grunert, K.G.; Bredahl, L.; Bruns, K. Consumer perception of meat quality and implications for product development in the meat sector—A review. Meat Sci. 2004, 66, 259–272. [Google Scholar] [CrossRef] [PubMed]
- Nychas, G.J.E.; Skandamis, P.N.; Tassou, C.C.; Koutsoumanis, K.P. Meat spoilage during distribution. Meat Sci. 2008, 78, 77–89. [Google Scholar] [CrossRef]
- Choe, J.H.; Choi, Y.M.; Lee, S.H.; Jeong, D.W.; Kim, B.C. Availability of Blood Glucose as the Indicator for Pork Quality. In Proceedings of the 55th International Congress of Meat Science and Technology (ICoMST), Copenhagen, Denmark, 16–21 August 2009; P. 1.34. pp. 148–151. [Google Scholar]
- Cannata, S.; Engle, T.E.; Moeller, S.J.; Zerby, H.N.; Radunz, A.E.; Green, M.D.; Bass, P.D.; Belk, K.E. Effect of visual marbling on sensory properties and quality traits of pork loin. Meat Sci. 2010, 85, 428–434. [Google Scholar] [CrossRef]
- Kim, J.M.; Kim, K.S.; Ko, K.B.; Ryu, Y.C. Estimation of pork quality in live pigs using biopsied muscle fibre number composition. Meat Sci. 2018, 137, 130–133. [Google Scholar] [CrossRef]
- Lebret, B.; Čandek—Potokar, M. Review: Pork quality attributes from farm to fork. Part I. Carcass and fresh meat. Animal 2022, 16, 100402. [Google Scholar] [CrossRef]
- Baker, M.T.; Lu, P.; Parrella, J.A.; Leggette, H.R. Consumer Acceptance toward Functional Foods: A Scoping Review. Int. J. Environ. Res. Public Health 2022, 19, 1217. [Google Scholar] [CrossRef]
- Mason, A.; Abdullah, B.; Muradov, M.; Korostynska, O.; Al-Shamma’a, A.; Bjarnadottir, S.G.; Lunde, K.; Alvseike, O. Theoretical Basis and Application for Measuring Pork Loin Drip Loss Using Microwave Spectroscopy. Sensors 2016, 16, 182. [Google Scholar] [CrossRef]
- Honikel, K.O.; Fischer, H. A rapid method for the detection of PSE and DFD porcine muscles. J. Food Sci. 1977, 42, 1633–1636. [Google Scholar] [CrossRef]
- Swatland, H.J. Postmortem changes in electrical capacitance and resistivity of pork. J. Anim. Sci. 1980, 51, 1108–1112. [Google Scholar] [CrossRef]
- Swatland, H.J. On-line evaluation of meat quality: State of the art. In Proceedings of the 49th International Congress of Meat Science and Technology, São Paulo, Brazil, 31 August–5 September 2003; pp. 102–111. [Google Scholar]
- Joo, S.T. Pork Quality: Identification, Measurement and Explanation of Factors Associated with Color and Water—Holding Capacity of Porcine Muscle. Ph.D. Thesis, Korea University, Seoul, Republic of Korea, 1995. [Google Scholar]
- Monin, G. Recent methods for predicting quality of whole meat. In Proceedings of the 44th International Congress of Meat Science and Technology, Barcelona, Spain, 30 August–4 September 1998; L5. pp. 56–65. [Google Scholar]
- Przybylski, W. Wykorzystanie potencjału glikolitycznego mięśnia Longissimus dorsi w badaniach nad uwarunkowaniem wybranych cech jakości mięsa wieprzowego. In Rozprawa Habilitacyjna; SGGW: Warszawa, Poland, 2002. [Google Scholar]
- Koćwin-Podsiadła, M.; Krzęcio, E.; Pospiech, E.; Łyczyński, A.; Antosik, K.; Grześ, B.; Zybert, A.; Sieczkowska, H. Selection of pork meat quality traits. Estimation of culinary and technological quality on the basis of measurements after slaughter. Fleischwirtschaft 2005, 2, 21–23. [Google Scholar]
- Krzęcio, E. Zmienność, Uwarunkowania i Diagnostyka Wycieku Naturalnego z Mięsa Wieprzowego; Wydawnictwo Akademii Podlaskiej: Siedlce, Poland, 2009. [Google Scholar]
- Antosik, K. Uwarunkowania Genetyczne Zawartości Tłuszczu Śródmięśniowego Oraz Jego Przydatność w Diagnozowaniu Jakości Mięsa Wieprzowego; Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego: Siedlce, Poland, 2014. [Google Scholar]
- Aliani, M.; Farmer, L.J.; Kennedy, J.T.; Moss, B.W.; Gordon, A. Post-slaughter changes in ATP metabolites, reducing and phosphorylated sugars in chicken meat. Meat Sci. 2013, 94, 55–62. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.; Matarneh, S.K.; Gerrard, D.; Tan, J. Contributions of energy pathways to ATP production and pH variations in postmortem muscles. Meat Sci. 2022, 189, 108828. [Google Scholar] [CrossRef]
- Zell, M.; Lyng, J.G.; Cronin, D.A.; Morgan, D.J. Ohmic heating of meats: Electrical conductivities of whole meats and processed meat ingredients. Meat Sci. 2009, 83, 563–570. [Google Scholar] [CrossRef]
- Gaitan-Jurado, A.J.; Ortiz-Somovilla, V.; Espana-Espana, F.; Perez-Aparicio, J.; De Pedro-Sanz, E.J. Quantitative analysis of pork dry-cured sausages to quality control by NIR spectroscopy. Meat Sci. 2008, 78, 391–399. [Google Scholar] [CrossRef]
- Collell, C.; Gou, P.; Picouet, P.; Arnau, J.; Comaposada, J. Feasibility of near-infrared spectroscopy to predict aw and moisture and NaCl contents of fermented pork sausages. Meat Sci. 2010, 85, 325–330. [Google Scholar] [CrossRef]
- Barbin, D.F.; Masry, G.; Sun, D.; Allen, P. Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem. 2013, 138, 1162–1171. [Google Scholar] [CrossRef]
- Prieto, N.; Pawluczyk, O.; Dugan, M.E.R.; Aalhu, J.R. A Review of the Principles and Applications of Near-Infrared Spectroscopy to Characterize Meat, Fat, and Meat Products. Appl. Spectrosc. 2017, 71, 1403–1426. [Google Scholar] [CrossRef]
- Fortin, A.; Tong, A.K.W.; Robertson, W.M.; Zawadski, S.M.; Landry, S.J.; Robinson, D.J. A novel approach to grading pork carcasses: Computer vision and ultrasound. Meat Sci. 2003, 63, 451–462. [Google Scholar] [CrossRef]
- O’Sullivan, M.G.; Byrne, D.V.; Martens, H.; Gidskehaug, L.H.; Andersen, H.J.; Martens, M. Evaluation of pork colour: Prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. Meat Sci. 2003, 65, 909–918. [Google Scholar] [CrossRef] [PubMed]
- Dasiewicz, K.; Mierzwińska, I. The use of a computer digital analysis for evaluating the quality of pork trimmings. Acta Sci. Pol. 2006, 5, 85–91. [Google Scholar]
- Sun, X.; Young, J.; Liu, J.H.; Newman, D. Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Sci. 2018, 140, 72–77. [Google Scholar] [CrossRef]
- Santos, C.C.; Zhao, J.; Dong, X.; Lonergan, S.M.; Huff-Lonergan, E.; Outhouse, A.; Carlson, K.B.; Prusa, K.J.; Fedler, C.A.; Yu, C.; et al. Predicting aged pork quality using a portable Raman device. Meat Sci. 2018, 145, 79–85. [Google Scholar] [CrossRef]
- Koćwin-Podsiadła, M.; Krzęcio, E.; Antosik, K.; Zybert, A.; Włodowiec, P. Ocena przydatności aparatów Ultra-FOM 100, Ultra-FOM 300 i CGM do szacowania mięsności tusz wieprzowych na podstawie uzyskanej mięsności wg metodyki SKURTCH. Trzoda Chlewna 2000, 38, 56–62. [Google Scholar]
- Grau, R.; Hamm, R. Eine einfache Methode zur Bestimmung der Wasserbindung in Fleisch. Fleischwirtschaft 1952, 4, 295–297. [Google Scholar]
- Pohja, N.S.; Ninivaara, F.P. Die Bestimmung der Wasserbindung des Fleisches mittels der Konsandrückmethods. Fleischwirtschaft 1957, 9, 193–195. [Google Scholar]
- Prange, H.; Jugert, L.; Schamer, E. Untersuchungen zur Muskel fleischqualität beim Schwein. Arch. Exp. Vet. Med. Leipzig 1997, 31, 235–248. [Google Scholar]
- Naveau, J.; Pommeret, P.; Lechaux, P. Proposition d’une methode de mesure du rendement technologique: La “methode Napole”. Techni-Porc 1985, 8, 7–13. [Google Scholar]
- Koćwin-Podsiadła, M.; Antosik, K.; Krzęcio, E.; Zybert, A.; Sieczkowska, H.; Grześ, B.; Łyczyński, A.; Pospiech, E. Effect of carcass muscling on culinary and technological pork properties in fatteners of three genetic groups. Anim. Sci. Pap. Rep. 2004, 22, 451–458. [Google Scholar]
- AOAC. Official Methods of Analysis; Association of Official Analytical Chemists: Washington, DC, USA, 2000. [Google Scholar]
- Dalrymple, R.H.; Hamm, R.A. Method for extracting of glycogen and metabolites from a single muscle sample. J. Anim. Sci. 1973, 8, 439–444. [Google Scholar]
- Bergmeyer, H.U. Methods of Enzyme Tic Analysis; Academic Press: New York, NY, USA, 1974. [Google Scholar]
- Monin, G.; Sellier, P. Pork flow technological quality with a normal rate of muscle pH fall in the immediate post mortem period: The case of the Hampshire breed. Meat Sci. 1985, 13, 49–63. [Google Scholar] [CrossRef]
- Barbosa, L.; Lopes, P.S.; Regazzi, A.J.; Guimarães, S.E.F.; Torres, R.A. Estudo da associação entre caracteristicas de desempenho e de carcaça de suinos por meio de correlação canônica. Rev. Bras. Zootec. 2005, 34, 2218–2224. [Google Scholar] [CrossRef]
- Ventura, H.T.; Lopes, P.S.; Peloso, J.V.; Guimarães, S.E.F.; Carneiro, A.P.S.; Carneiro, P.L.S. A canonical correlation analysis of the association between carcass and ham traits in pigs used to produce dry–cured ham. Genet. Mol. Biol. 2011, 34, 451–455. [Google Scholar] [CrossRef]
- Huff-Lonergan, E.; Lonergan, S.M. Mechanisms of water-holding capacity of meat: The role of postmortem biochemical and structural changes. Meat Sci. 2005, 71, 194–204. [Google Scholar] [CrossRef]
- Jo, K.; Lee, S.; Jeong, H.G.; Lee, D.H.; Yoon, S.; Chung, Y.; Jung, S. Utilization of Electrical Conductivity to Improve Prediction Accuracy of Cooking Loss of Pork Loin. Food Sci. Anim. Resour. 2023, 43, 113–123. [Google Scholar] [CrossRef]
- Larzul, C.; Le Roy, P.; Monin, G.; Sellier, P. Variabilité génétique du potentiel glycolytique du muscle chez le porc. INRA Prod. Anim. 1998, 11, 183–197. [Google Scholar] [CrossRef]
- Larzul, C.; Le Roy, P.; Sellier, P.; Jacquet, B.; Gogue, J.; Talamant, A.; Vernin, P.; Monin, G. Le potentiel glycolytique du muscle mesuré sur le porc vivant: Un nouvea critére de sélection pour la qualité de la viande? J. Rech. Porc. France 1998, 30, 81–85. [Google Scholar]
- Davoli, R.; Vegni, J.; Cesarani, A.; Dimauro, C.; Zappaterra, M.; Zambonelli, P. Identification of differentially expressed genes in early-postmortem Semimembranosus muscle of Italian Large White heavy pigs divergent for glycolytic potential. Meat Sci. 2022, 187, 108754. [Google Scholar] [CrossRef]
- Wu, W.; Zhang, Z.; Chao, Z.; Li, B.; Li, R.; Jiang, A.; Kim, K.H.; Liu, H. Transcriptome analysis reveals the genetic basis of skeletal muscle glycolytic potential based on a pig model. Gene 2021, 766, 145157. [Google Scholar] [CrossRef] [PubMed]
- Sulaiman, K.M.; Scheffler, T.L.; Gerrard, D.E. Chapter 5—The conversion of muscle to meat. In Lawrie’s Meat Science, 9th ed.; Woodhead Publishing Series in Food Science, Technology and Nutrition; Toldrá, F., Ed.; Woodhead Publishing: Sawston, UK, 2023; pp. 159–194. ISBN 9780323854085. [Google Scholar]
- Fernandez, X.; Gueblez, R. Relationship between lactate and glycogen contents and pH values in post mortem longissimus muscle of the pig. In Proceedings of the 38th International Congress of Meat Science and Technology, Clermont-Ferrend, France, 23–28 August 1992; pp. 355–358. [Google Scholar]
- Lundström, K.; Andersson, A.; Hansson, I. Effect of RN gene on technological and sensory meat quality in crossbred pigs with Hampshire as terminal sire. Meat Sci. 1996, 42, 145–153. [Google Scholar] [CrossRef]
- Zybert, A.; Sieczkowska, H.; Antosik, K.; Krzęcio-Nieczyporuk, E.; Adamczyk, G.; Koćwin -Podsiadła, M. Relationship between glycolytic potential and meat quality of Duroc pigs with consideration of carcass chilling system. Ann. Animal Sci. 2013, 13, 645–654. [Google Scholar] [CrossRef]
- Milan, D.; Jeon, J.T.; Looft, C.; Amarger, V.; Robic, A.; Thelander, M.; Rogel-Gaillard, C.; Paul, S.; Iannuccelli, N.; Rask, L.; et al. A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science 2000, 288, 1248–1251. [Google Scholar] [CrossRef]
- Scheffler, T.L.; Scheffler, J.M.; Kasten, S.C.; Sosnicki, A.A.; Gerrard, D.E. High glycolytic potential does not predict low ultimate pH in pork. Meat Sci. 2013, 95, 85–91. [Google Scholar] [CrossRef]
- Tarczyński, K.; Sieczkowska, H.; Zybert, A.; Krzęcio-Nieczyporuk, E.; Antosik, K. pH measured 24 hours post mortem should not be regarded as ultimate pH in pork meat quality evaluation. S. Afr. J. Anim. Sci. 2018, 48, 1009–1016. [Google Scholar] [CrossRef]
- Lana, A.; Zolla, L. Proteolysis in meat tenderization from the point of view of each single protein: A proteomic perspective. J. Proteomics 2016, 147, 85–97. [Google Scholar] [CrossRef] [PubMed]
- Le Roy, P.; Przybylski, W.; Burlot, T.; Bazin, C.; Lagant, H.; Monin, G. Etude des relations entre le potentiel glycolytique du muscle et les caractéres de production dans les lignees Laconie et Penshire. J. Rech. Porc. France 1994, 26, 311–314. [Google Scholar]
- Bai, X.; Hou, J.; Wang, L.; Wang, M.; Wang, X.; Wu, C.; Yu, L.; Yang, J.; Leng, Y. Electrical impedance analysis of pork tissues during storage. Food Meas. 2018, 12, 164–172. [Google Scholar] [CrossRef]
- Koćwin–Podsiadła, M.; Krzęcio, E.; Przybylski, W. Pork quality and the methods of its evaluation. Pol. J. Food Nutr. Sci. 2006, 56, 241–248. [Google Scholar]
Parameter | Value |
---|---|
Number of animals (n) | 495 |
gilts | 235 |
castrates | 260 |
Hot carcass weight (kg) | 84.98 ± 7.45 |
Carcass meatiness by FOM (%) | 56.22 ± 4.04 |
Abbreviation | Explanation |
---|---|
ATP | adenosine triphosphate |
CR | canonical correlation |
DL | drip loss |
EC | electrical conductivity |
GP | glycolytic potential |
IMF | intramuscular fat content |
IMP | inosine-5′-monophosphate |
L* | lightness of meat color |
MT | meat tenderness |
R1 | IMP/ATP ratio |
RC2 | composed determination coefficients |
WHC | water holding capacity |
TY | technological yield |
(y) | (x) | ||||||||
---|---|---|---|---|---|---|---|---|---|
pH1 | pH24 | R1 | L* | EC2 (mS cm−1) | GP (μmol g−1) | Glycogen (μmol g−1) | Lactate (μmol g−1) | ||
pH1 | rxy bxy | - | −0.03 | −0.46 ** −0.09 | −0.12 ** −0.01 | −0.30 ** −0.05 | 0.08 | 0.31 ** 0.04 | −0.48 ** −0.01 |
pH24 | rxy bxy | −0.03 | - | −0.01 | −0.22 ** −0.02 | −0.09 | −0.26 ** −0.01 | −0.28 ** −0.02 | 0.08 |
R1 | rxy bxy | −0.46 ** −0.15 | −0.01 | - | 0.19 ** 0.01 | 0.34 ** 0.02 | 0.04 | −0.24 ** −0.01 | 0.61 ** 0.03 |
L* | rxy bxy | −0.12 ** −1.98 | −0.22 ** −6.12 | 0.19 ** 9.67 | - | 0.00 | 0.27 ** 0.03 | 0.16 ** 0.04 | 0.23 ** 0.06 |
EC2 (mS cm−1) | rxy bxy | −0.30 ** −1.78 | −0.09 | 0.34 ** 6.19 | 0.00 | - | −0.05 | −0.13 ** −0.01 | 0.18 ** 0.02 |
GP (μmol g−1) | rxy bxy | 0.08 | −0.26 ** −9.18 | 0.04 | 0.27 ** 2.25 | −0.05 | - | 0.88 ** 1.71 | 0.19 ** 0.39 |
glycogen (μmol g−1) | rxy bxy | 0.31 ** 2.85 | −0.28 ** −3.97 | −0.24 ** −3.81 | 0.16 ** 0.68 | −0.13 ** −1.67 | 0.88 ** 0.45 | - | −0.29 ** −0.31 |
lactate (μmol g−1) | rxy bxy | −0.48 ** −3.29 | 0.08 | 0.61 ** 2.56 | 0.23 ** 0.91 | 0.18 ** 2.05 | 0.19 ** 0.09 | −0.29 ** −0.27 | - |
protein content (%) | rxy bxy | −0.15 ** −0.47 | −0.01 | 0.16 ** 1.74 | 0.08 | −0.05 | 0.13 * 0.05 | 0.04 | 0.18 ** 0.09 |
IMF (%) | rxy bxy | −0.02 | 0.11 * 0.61 | −0.03 | 0.07 | −0.12 * −0.22 | 0.00 | 0.01 | −0.02 |
WHC (cm2) | rxy bxy | −0.01 | −0.02 | 0.15 ** 3.47 | 0.21 ** 0.47 | 0.13 ** 0.18 | 0.14 ** 2.62 | 0.05 | 0.18 ** 1.58 |
TY (%) | rxy bxy | −0.08 | 0.30 ** 12.75 | −0.11 * −8.69 | −0.14 ** −0.21 | −0.13 ** −0.56 | −0.14 ** −0.76 | −0.14 ** −0.38 | −0.00 |
DL (%) | rxy bxy | −0.14 ** −1.96 | −0.35 ** −8.02 | 0.11 * 4.65 | 0.26 ** 0.32 | 0.21 ** 0.51 | 0.22 ** 2.30 | 0.13 ** 0.67 | 0.19 ** 0.92 |
MT (N cm−2) | rxy bxy | −0.33 ** −16.26 | −0.15 | 0.52 ** 28.24 | 0.17 * 0.53 | 0.43 ** 2.99 | 0.16 * 0.38 | −0.06 | 0.41 ** 0.32 |
Correlated Sets | Independent (Determining) Variables’ Sets | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
GP (X1) | Glycogen (X2) | Lactate (X3) | GP, Glycogen (X4) | GP, Lactate (X5) | GP, Glycogen Lactate (X6) | pH1, R1 (X7) | pH1, pH24, (X8) | pH1, pH24, L* (X9) | EC2, pH24 (X10) | ||
Dependent variables sets: | |||||||||||
(Y1)
| CR RC2 | 0.49 ** 0.24 | 0.51 ** 0.26 | 0.67 ** 0.45 | 0.77 ** 0.59 | 0.77 ** 0.59 | 0.76 ** 0.58 | - | - | 0.72 ** 0.51 | |
(Y2) set (Y1) and:
| CR RC2 | 0.57 ** 0.32 | 0.59 ** 0.35 | 0.68 ** 0.46 | 0.79 ** 0.62 | 0.79 ** 0.62 | 0.79 ** 0.62 | - | - | - | |
(Y3)
| CR RC2 | 0.26 NS | 0.10 NS | 0.41 ** 0.17 | 0.44 ** 0.19 | 0.44 ** 0.19 | 0.44 ** 0.19 | 0.46 ** 0.21 | 0.53 ** 0.28 | 0.54 ** 0.29 | 0.58 ** 0.34 |
(Y4)
| CR RC2 | 0.38 * 0.14 | 0.19 NS | 0.46 ** 0.21 | 0.52 ** 0.27 | 0.52 * 0.27 | 0.52 ** 0.27 | 0.48 ** 0.23 | 0.59 ** 0.35 | 0.60 ** 0.36 | 0.61 ** 0.37 |
(Y5)
| CR RC2 | 0.14 NS | 0.05 NS | 0.16 NS | 0.19 NS | 0.19 NS | 0.25 NS | 0.29 * 0.08 | 0.51 ** 0.26 | 0.51 ** 0.26 | 0.52 ** 0.27 |
(Y6)
| CR RC2 | 0.41 ** 0.17 | 0.25 NS | 0.30 * 0.09 | 0.46 ** 0.21 | 0.46 ** 0.21 | 0.47 ** 0.22 | 0.31 ** 0.10 | 0.55 ** 0.30 | - | 0.57 ** 0.32 |
(Y7)
| CR RC2 | 0.14 * 0.02 | 0.31 ** 0.10 | 0.64 ** 0.41 | 0.58 ** 0.34 | 0.59 ** 0.35 | 0.59 ** 0.35 | - | - | - | 0.67 ** 0.45 |
(Y8)
| CR RC2 | 0.34 ** 0.11 | 0.52 ** 0.27 | 0.58 ** 0.34 | 0.50 ** 0.25 | 0.49 ** 0.24 | 0.50 ** 0.25 | - | - | - | - |
(Y9)
| CR RC2 | 0.39 ** 0.15 | 0.53 ** 0.28 | 0.64 ** 0.41 | 0.50 ** 0.25 | 0.50 ** 0.25 | 0.50 ** 0.25 | - | - | - | - |
(Y10)
| CR RC2 | 0.33 ** 0.11 | 0.48 ** 0.23 | 0.39 ** 0.15 | 0.35 ** 0.12 | 0.35 ** 0.12 | 0.35 ** 0.12 | 0.67 ** 0.45 | - | - | - |
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Antosik, K.; Krzęcio-Nieczyporuk, E.; Sieczkowska, H.; Zybert, A.; Tarczyński, K. The Use of Chosen Physicochemical Indicators for Estimation of Pork Meat Quality. Agriculture 2023, 13, 1670. https://doi.org/10.3390/agriculture13091670
Antosik K, Krzęcio-Nieczyporuk E, Sieczkowska H, Zybert A, Tarczyński K. The Use of Chosen Physicochemical Indicators for Estimation of Pork Meat Quality. Agriculture. 2023; 13(9):1670. https://doi.org/10.3390/agriculture13091670
Chicago/Turabian StyleAntosik, Katarzyna, Elżbieta Krzęcio-Nieczyporuk, Halina Sieczkowska, Andrzej Zybert, and Krystian Tarczyński. 2023. "The Use of Chosen Physicochemical Indicators for Estimation of Pork Meat Quality" Agriculture 13, no. 9: 1670. https://doi.org/10.3390/agriculture13091670