Fast Determination Of Beef Quality Parameters With Time-domain Nuclear Magnetic Resonance Spectroscopy And Chemometrics

The noteworthy of this study is to predict seven quality parameters for beef samples using time-domain nuclear magnetic resonance (TD-NMR) relaxometry data and multivariate models. Samples from 61 Bonsmara heifers were separated into five groups based on genetic (breeding composition) and feed system (grain and grass feed). Seven sample parameters were analyzed by reference methods; among them, three sensorial parameters, flavor, juiciness and tenderness and four physicochemical parameters, cooking loss, fat and moisture content and instrumental tenderness using Warner Bratzler shear force (WBSF). The raw beef samples of the same animals were analyzed by TD-NMR relaxometry using Carr-Purcell-Meiboom-Gill (CPMG) and Continuous Wave-Free Precession (CWFP) sequences. Regression models computed by partial least squares (PLS) chemometric technique using CPMG and CWFP data and the results of the classical analysis were constructed. The results allowed for the prediction of aforementioned seven properties. The predictive ability of the method was evaluated using the root mean square error (RMSE) for the calibration (RMSEC) and validation (RMSEP) data sets. The reference and predicted values showed no significant differences at a 95% confidence level. © 2013 Elsevier B.V.1088891Cardello, A.V., (1995) Food Qual. Prefer., 6, pp. 163-170Piqueras-Fiszman, B., Alcaide, J., Roura, E., Spence, C., (2012) Food Qual. Prefer., 24, pp. 205-208Monin, G., (1998) Meat Sci., 49 (S1), pp. 231-S243Sudre, J., Pineau, N., Loret, C., Martin, N., (2012) Food Qual. Prefer., 24, pp. 179-189Raudenbush, B., Van Der Klaauw, N.J., Frank, R.A., (1995) Appetite, 25, pp. 1-15Drewnowski, A., (1993) Acta Psychol., 84, pp. 103-110Bertram, H.C., Straadt, I.K., Jensen, J.A., Aaslyng, M.D., (2007) Meat Sci., 77, pp. 190-195Van Duynhoven, J., Voda, A., Witek, M., Van As, H., (2010) Annu. Rep. NMR Spectro., 69, pp. 145-197Todt, H., Guthausen, G., Burk, W., Schmalbein, D., Kamlowski, A., (2006) Food Chem., 96, pp. 436-440Ferguson, D.M., Warner, R.D., Walker, P.J., Knee, B., (2007) Aust. J. Exp. Agric., 47, pp. 774-781Bressan, M.C., Rodrigues, E.C., Rossato, L.V., Ramos, E.M., Gama, L.T., (2011) R. Bras. Zootec., 40, pp. 1250-1259Corrêa, C.A., Forato, L.A., Colnago, L.A., (2009) Anal. Bioanal. Chem., 393, pp. 1357-1360Van Eenennaam, A.L., Weber, K.L., Cooprider, K., Drake, D.J., (2010) Calif. Agr., 64, pp. 94-100Colnago, L.A., Azeredo, R.B.V., Marchi Netto, A., Andrade, F.D., Venâncio, T., (2011) Magn. Reson Chem., 49, pp. 113-S120Meilgaard, M., Civille, G.V., Carr, B.T., (1999) Sensory Evaluation Techniques, , 3rd ed. CRC Press Inc. Boca Raton(1995) American Meat Science Association, Research Guidelines for Cookery, Sensory Evaluation and Tenderness Measurements of Fresh Meat, , AMSA Chicago: National Live Stock and Meat Board, IL(1980) Official Method of Analysis of the Association of Analytical Chemists, , AOAC, Association of Official Analytical Chemists 17th rev. ed. Association of the Analytical Chemists, Washington, DCBligh, E.G., Dyer, W.J., (1959) Can. J. Biochem. Physiol., 37, pp. 911-917Iverson, S.J., Lang, S.L., Cooper, M.H., (2001) Lipids, 36, pp. 1283-1287Belton, P.S., Gil, A.M., Webb, G.A., Rutledge, D., Cornejo, F., Chinachoti, P., NMR in foods: Opportunity and challenges (2003) Magnetic Resonance in Food Science: Latest Developments, pp. 25-37. , P.S Belton, A.M. Gil, G.A Webb, D. Rutledge, The Royal Society of Chemistry CambridgeGeladi, P., (2003) Spectrochim. Acta B, 58, pp. 767-782Hoskuldsson, A., (1998) J. Chemometr., 2, pp. 211-228Pereira, F.M.V., Colnago, L.A., (2012) Food. Anal. Meth., 5 (6), pp. 1349-135