Prediction of beef color using time‐domain nuclear magnetic resonance (TD‐NMR) relaxometry data and multivariate analyses. (19th May 2016) - Moreira, Luiz Felipe Pompeu Prado; Ferrari, Adriana Cristina; Moraes, Tiago Bueno; Reis, Ricardo Andrade; Colnago, Luiz Alberto; Pereira, Fabíola Manhas Verbi
Abstract : Time‐domain nuclear magnetic resonance and chemometrics were used to predict color parameters, such as lightness (L*), redness (a*), and yellowness (b*) of beef ( Longissimus dorsi muscle) samples. Analyzing the relaxation decays with multivariate models performed with partial least‐squares regression, color quality parameters were predicted. The partial least‐squares models showed low errors independent of the sample size, indicating the potentiality of the method. Minced procedure and weighing were not necessary to improve the predictive performance of the models. The reduction of transverse relaxation time ( T 2 ) measured by Carr–Purcell–Meiboom–Gill pulse sequence in darker beef in comparison with lighter ones can be explained by the lower relaxivity Fe 2+ present in deoxymyoglobin and oxymyoglobin (red beef) to the higher relaxivity of Fe 3+ present in metmyoglobin (brown beef). These results point that time‐domain nuclear magnetic resonance spectroscopy can become a useful tool for quality assessment of beef cattle on bulk of the sample and through‐packages, because this technique is also widely applied to measure sensorial parameters, such as flavor, juiciness and tenderness, and physicochemical parameters, cooking loss, fat and moisture content, and instrumental tenderness using Warner Bratzler shear force. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : Time‐domain NMR spectroscopy has been used to predict several technological parameters to assess meat quality. These analyses are based on the decay of NMR signal Carr‐Purcell‐Meiboom‐Gill pulse sequence that depends on transverse relaxation time (T2). By means of multivariate models is also feasible to measure color in beef samples with robustness and speed, independent of the sample size.