Evaluating the Ability of Rapid Evaporative Ionization Mass Spectrometry to Predict the Palatability of Long Aged Beef Cuts

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Chapter II: Meat aging is a classic method for increasing the tenderness and value of traditional beef products. However, the effects of extended aging times on other palatability traits as well as on typically tender muscles such as the Psoas major have not been fully explored. In this study paired striploin and tenderloin samples were collected from upper 2/3 USDA Choice and USDA Select beef carcasses (n=42). Subprimals were fabricated into chunks and assigned to one of 6 postmortem aging treatments (3, 14, 28, 42, 56, 70 d). Following the completion of the aging interval, samples were then subjected to Warner Bratzler (WBSF) and slice shear force (SSF) as well as trained sensory panels analysis. Using this data, aging curves were created to analyze the rate of tenderization of both muscles over time. Overall, aging time displayed an effect on SSF and WBSF (P<0.01) for both muscles. However, extended aging times beyond certain thresholds (day 14: tenderloins, day 28: striploins) resulted in decreased rates of tenderization and no differences between treatments (P > 0.05). Trained sensory panelist responses, in conjunction with hierarchal clustering, determined a flavor shift which occurred in both striploins and tenderloins around days 42 and 56. This shift in flavor profiles resulted in exponential increases in off-note intensities (liver-like, sour, oxidized flavors), and decreased intensities for beef ID, browned, and roasted flavors. Chapter III: Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technology for chemometric analysis in the food industry. The technology has shown potential in identifying food fraud, detecting adulteration, and predicting tenderness in meat samples. In this study, REIMS was used to generate metabolomic fingerprints for 84 beef carcasses at time of grading. Striploins and tenderloins were collected from the carcasses, fabricated, and then aged to six aging treatments (3, 14, 28, 42, 56, 70d). Following aging, palatability traits were collected from all samples using trained sensory panels and shear force analysis (SSF and WBSF). The REIMS data and 45 machine learning techniques were then used to build prediction models for quality grade prediction as well as the mentioned palatability traits at each of the aging intervals. A total of 2,475 models were created in this study and their efficacies were compared. Overall, REIMS proved effective at classifying animals based on quality grade, tenderness, juiciness, and flavor with top model accuracies ranging from 71.0-90.3% validation accuracy. The results of this study indicate REIMS has the potential to be used as a supplement to traditional quality grading for identification of highly palatable beef products. Chapter IV: Metabolomic profiling using rapid evaporative ionization mass spectrometry (REIMS) has been shown to be an effective tool in the meat industry for detection of food fraud and identification of abnormal flavor traits such as boar taint. Thanks to these successes, REIMS has been suggested as a potential tool to track and identify the changes in the metabolome between samples of varying palatability levels, ages, and differing muscles. In this study, REIMS ability to identify these changes as well as determine the animal of origin was tested. 84 beef animals of two quality levels were selected and both striploins and tenderloins were collected from each carcass. Each of these cuts were then split into 6 chunks and randomly assigned to an aging treatment (3,14,28,42,56,70). Following aging, samples were analyzed using trained sensory panels, shear force, and REIMS analysis. Finally multivariate statistical techniques were used to identify the metabolites of interest within the metabolome and predict the observed traits. Overall, REIMS proved to be an extremely capable tool for prediction of sensory experience (85.8% accuracy), muscle identification (99.5%), and determination of animal of origin (99.2%). REIMS also was able to identify the age of a sample within a 2 week window with 94.0% accuracy.

Meat aging, REIMS, Beef flavor, machine learning and prediction, Beef proteolysis, Mass spectrometry, Chemometrics