Evaluation of the Tendertec beef grading instrument to predict the tenderness of steaks from beef carcasses

N,N'-Diphenylthiourea electrochemical sensor for the detection of l-glutamate and Aspartate in beef

This study was conducted to design an electrochemical sensor for detection of l-glutamate (L-Glu) and Aspartate (Asp), which contribute largely to the umami taste of beef. Using N, N'-diphenylthiourea (DPTU), polypyrrole (PPy), and polyvinyl chloride (PVC), a composite electrode (DPTU/PVC/PPy/Pt) was prepared for rapidly electrochemical detection of l-Glu and Asp. The surface morphology of the synthesized functionalized electrode was characterized by Field Emission Scanning Electron Microscopy (FESEM). Potentials of umami amino acids accounted for 97.8%, while six interferential amino acids existed. The linear correlation between the content of l-Glu and Asp in beef broth was studied under different treatment conditions. The sensor compared with an amino acid analyzer well detected the contents of l-Glu and Asp in beef broths, with a coefficient of 0.991 in Pearson correlation analysis and an accuracy of 88.9%. The proposed electrochemical sensor showed good concurrence with previously reported methods and was effectively employed for the quantification of l- Glu and Asp in beef. PRACTICAL APPLICATION: The sensor exhibits the good performance with high stability and high accuracy. And it is a potential sensing platform with good reproducibility, making the proposed method suitable and reliable for routine analysis of L-Glu and Asp in beef. This method was proved promising for quantitative detecting amino acids in beef.

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef.

Chianina beef tenderness investigated through integrated Omics

In the present study we performed an integrated proteomics, interactomics and metabolomics analysis of Longissimus dorsi tender and tough meat samples from Chianina beef cattle. Results were statistically handled as to obtain Pearson's correlation coefficients of the results from Omics investigation in relation to canonical tenderness-related parameters, including Warner Bratzler shear force, myofibrillar degradation (at 48 h and 10 days after slaughter), sarcomere length and total collagen content. As a result, we could observe that the tender meat group was characterized by higher levels of glycolytic enzymes, which were over-phosphorylated and produced accumulation of glycolytic intermediates. Oxidative stress promoted meat tenderness and elicited heat shock protein responses, which in turn triggered apoptosis-like cascades along with PARP fragmentation. Phosphorylation was found to be a key process in post mortem muscle conversion to meat, as it was shown not only to modulate glycolytic enzyme activities, but also mediate the stability of structural proteins at the Z-disk. On the other hand, phosphorylation of HSPs has been supposed to alter their functions through changing their affinity for target interactors. Analogies and breed-specific differences are highlighted throughout the text via a direct comparison of the present results against the ones obtained in a parallel study on Maremmana Longissimus dorsi. It emerges that, while the main cornerstones and the final outcome are maintained, post mortem metabolism in tender and tough meat yielding individuals is subtly modulated via specific higher levels of enzymes and amino acidic residue phosphorylation in a breed-specific fashion, and whether calcium homeostasis dysregulation was a key factor in Maremmana, higher early post mortem phosphocreatine levels in the Chianina tender group could favor a slower and prolonged glycolytic rate, prolonging the extent of the minimum hanging period necessary to obtain tender meat from this breed by a few days.

Meat quality assessment using biophysical methods related to meat structure

This paper overviews the biophysical methods developed to gain access to meat structure information. The meat industry needs reliable meat quality information throughout the production process in order to guarantee high-quality meat products for consumers. Fast and non-invasive sensors will shortly be deployed, based on the development of biophysical methods for assessing meat structure. Reliable meat quality information (tenderness, flavour, juiciness, colour) can be provided by a number of different meat structure assessment either by means of mechanical (i.e., Warner-Bratzler shear force), optical (colour measurements, fluorescence) electrical probing or using ultrasonic measurements, electromagnetic waves, NMR, NIR, and so on. These measurements are often used to construct meat structure images that are fusioned and then processed via multi-image analysis, which needs appropriate processing methods. Quality traits related to mechanical properties are often better assessed by methods that take into account the natural anisotropy of meat due to its relatively linear myofibrillar structure. Biophysical methods of assessment can either measure meat component properties directly, or calculate them indirectly by using obvious correlations between one or several biophysical measurements and meat component properties. Taking these calculations and modelling the main relevant biophysical properties involved can help to improve our understanding of meat properties and thus of eating quality.

Predicting beef tenderness using near-infrared spectroscopy1

The objective of this multiple-phase study was to determine the accuracy of an on-line near-infrared (NIR) spectral reflectance system to predict 14-d-aged cooked beef tenderness. In phase I, 292 carcasses (140 US Select, 152 US Choice) were selected (d 2) from 2 commercial beef processing facilities. After carcass selection, longissimus lumborum (LL) muscle sections (ribs 9 to 12) were individually identified, vacuum-packaged, and transported to the Oklahoma State University Meats Laboratory, where a 2.54-cm-thick steak (n = 1) was fabricated and stored in refrigerated conditions (1 degrees C +/- 1). Following a 30-min oxygenation period, a NIR spectral scan was obtained on the 12th-rib LL steak. Steaks (d 3) were individually vacuum-packaged and aged at 4 degrees C for a total of 14 d before cooking slice shear force (SSF) analysis. In phases II and III, 476 carcasses (258 US Select, 218 US Choice) were immediately NIR scanned after carcass presentation to in-plant USDA grading personnel. In a similar fashion, all LL steaks were aged (1 degrees C +/- 1) for 14 d before cooking (70 degrees C) and conducting SSF. Of the phase I and II samples, 39 (6.77%) were categorized as being tough (i.e., >/= 25 kg of SSF after the 14-d postmortem aging period). Of these 39 tough samples, 20 (3.7% error rate) were correctly placed in the 90% certification level. Another 10 tough samples were placed in the 80% certification level (2.0% error rate). The overall NIR certified tender group was 1.67 kg more tender (P < 0.05) than LL samples from the noncertified samples. When the NIR predicted samples to be tough, 10% of the samples were eliminated from the phase I and II LL populations at 90% certification. The population SSF mean improved in excess of 6.5 kg. For phase III, SSF evaluation by an independent third party indicated the NIR system was able to successfully sort tough from tender LL samples to 70% certification levels. It was concluded that NIR scanning offers an in-plant opportunity to sort carcasses into tenderness outcome groups for guaranteed-tender branded beef programs.

Effects of aging prior to freezing on poultry meat tenderness

The objective of this study was to investigate the effects of aging prior to deboning and deep-freezing on the tenderness of cooked broiler breast fillets. Broilers (Ross-208) that were 37 and 38 d old were slaughtered in a licensed poultry meat plant. After spray chilling, carcasses with BW between 1,080 and 1,300 g were selected for the study and transported to a chilling room (2 to 4 degrees C). The whole carcasses were aged for up to 24 h. During the aging process, continuous measurements were made of pH values, electric conductivity, and firmness of the raw fillets. After the aging process, the carcasses were deboned, and the breast fillets were immediately frozen to an internal temperature of -20 degrees C. Tenderness was measured both mechanically with a Warner-Bratzler shear force device and by sensory testing. The ultimate pH values in the fillets were reached during the first 5 h of aging. Electric conductivity increased during the whole aging period. During the first hours of aging, firmness increased significantly, corresponding with the onset of rigor mortis. After between 8 and 9 h of aging, firmness decreased significantly. Shear force values of aged fillets also changed significantly; the highest values were found at the beginning of aging, which then decreased from the sixth hour. The results of the instrumental tenderness measurements were confirmed by the sensory evaluations. Implementation of in-plant measurements of firmness, thus, seemed to be useful in predicting the sensory quality of poultry meat.

Mechanical probes can predict tenderness of cooked beef longissimus using uncooked measurements1

Two experiments were conducted to determine the effectiveness of using mechanical probes and objective color measurement on beef LM to predict cooked tenderness. In Exp. 1, sharp needle (SN), sharp blade (SB), blunt needle (BN), blunt blade (BB), and plumb bob (PB) probes were used to measure uncooked LM (n = 29) at 2 d postmortem in both a perpendicular and parallel orientation to the long axis of the strip loin. Additionally, instrumental color measurements were measured on uncooked muscle at 2 d postmortem. Steaks for trained sensory panel (TSP) and Warner-Bratzler shear force (WBSF) measurements were aged 14 d postmortem before cooking. Probe measurements taken perpendicular to the long axis of the LM were not correlated (P = 0.22 to 0.82) to TSP tenderness. Probe measurements (BB, BN, SN, SB, and PB) taken parallel to the long axis were correlated to TSP tenderness (r = -0.57, -0.40, -0.77, -0.52, and -0.53, respectively). A regression equation using the SN probe to predict TSP tenderness had a R2 value of 0.74. The SB probe combined with L* accounted for 45% of the variation in TSP tenderness, whereas the PB probe combined with L* accounted for 56% of the variation in TSP tenderness. A second experiment (n = 24) was conducted using the SN, SB, and PB probes on uncooked sections at 2 d and on cooked steaks at 14 d postmortem. Probe measurements on cooked steaks were not correlated to TSP tenderness. New regression equations were calculated using the probe measurements on uncooked steaks from both experiments. Prediction equations formulated with L* values and either SN, SB, or PB probes accounted for 49, 50, and 47% of the variability in TSP tenderness scores, respectively. An equation using WBSF of cooked steaks to predict TSP tenderness had an R2 of 0.58. Of the steaks predicted to be tender (predicted tenderness > 5.0) by the equations using the SN, SB, and PB probes on uncooked steaks and WBSF on cooked steaks, 85, 88, 80, and 84%, respectively, were actually tender (TSP tenderness > 5.0). Mechanical probe measurements of uncooked steaks at 2 d postmortem can potentially classify strip loins into groups based on tenderness, as well as WBSF measurements, which are more costly and time consuming.

Effectiveness of the SmartMV prototype BeefCam System to sort beef carcasses into expected palatability groups

This study was conducted to determine the effectiveness of the SmartMV prototype BeefCam Video Imaging System (prototype BeefCam) for classifying beef carcasses into palatability ("certified" or "not certified" as palatable) groups. Carcasses (n = 769) were selected from four beef-packing plants to represent three USDA quality grade groups (Top Choice, TC; Low Choice, LC; and Select, SE). Following chilling, a prototype BeefCam image of the longissimus muscle was obtained for each carcass. Strip loins were collected from the left side of each carcass and aged for 10 d; Warner-Bratzler shear force (WBSF; n = 769) values and consumer panel ratings (hedonic, end-anchored, 9-point ratings for overall like/dislike; n = 500 carcasses) were obtained for cooked steaks. Using information from the images, two regression models were developed to predict the first principal component of WBSF and consumer panel ratings for sorting carcasses based on expected eating quality. Model I used only prototype BeefCam output, whereas Model II used prototype BeefCam output and a coded value for quality grade group. For both models, carcasses with a predicted value of less than 0.0 were certified as producing palatable beef Additional carcasses (n = 292) were evaluated at a fifth and separate packing plant by prototype BeefCam to validate Models I and II. A strip loin was collected from each carcass and WBSF was measured after 14 d of aging. The percentages of validation carcasses that generated tough (WBSF > or = 4.5 kg) steaks were 6.5,5.8,10.7, and 7.9% for, TC, LC, SE, and all carcasses, respectively. Use of Model I certified 51.9, 47.6, 43.8, and 47.3% of TC, LC, SE, and all carcasses, respectively. Of the carcasses certified by use of Model I, 0.0,0.0, 4.1, and 1.4% of TC, LC, SE, and all carcasses, respectively, generated tough steaks. Use of Model II certified 59.7, 47.6, 25.0, and 42.1% of TC, LC, SE, and all carcasses, respectively. Of the carcasses certified by use of Model II, 2.2, 0.0, 3.6, and 1.6% of TC, LC, SE, and all carcasses, respectively, generated tough steaks. For both models, the frequency of carcasses that produced tough steaks in the certified group was lower (P < 0.05) for all validation carcasses sampled compared with that of the original carcass population. Based on the decrease in the frequency of carcasses that produced tough steaks, further development of a commercial BeefCam system is warranted.

The efficacy of three objective systems for identifying beef cuts that can be guaranteed tender

The objective of this study was to determine the accuracy of three objective systems (prototype BeefCam, colorimeter, and slice shear force) for identifying guaranteed tender beef. In Phase I, 308 carcasses (105 Top Choice, 101 Low Choice, and 102 Select) from two commercial plants were tested. In Phase II, 400 carcasses (200 rolled USDA Select and 200 rolled USDA Choice) from one commercial plant were tested. The three systems were evaluated based on progressive certification of the longissimus as "tender" in 10% increments (the best 10, 20, 30%, etc., certified as "tender" by each technology; 100% certification would mean no sorting for tenderness). In Phase I, the error (percentage of carcasses certified as tender that had Warner-Bratzler shear force of > or = 5 kg at 14 d postmortem) for 100% certification using all carcasses was 14.1%. All certification levels up to 80% (slice shear force) and up to 70% (colorimeter) had less error (P < 0.05) than 100% certification. Errors in all levels of certification by prototype BeefCam (13.8 to 9.7%) were not different (P > 0.05) from 100% certification. In Phase I, the error for 100% certification for USDA Select carcasses was 30.7%. For Select carcasses, all slice shear force certification levels up to 60% (0 to 14.8%) had less error (P < 0.05) than 100% certification. For Select carcasses, errors in all levels of certification by colorimeter (20.0 to 29.6%) and by BeefCam (27.5 to 31.4%) were not different (P > 0.05) from 100% certification. In Phase II, the error for 100% certification for all carcasses was 9.3%. For all levels of slice shear force certification less than 90% (for all carcasses) or less than 80% (Select carcasses), errors in tenderness certification were less than (P < 0.05) for 100% certification. In Phase II, for all carcasses or Select carcasses, colorimeter and prototype BeefCam certifications did not significantly reduce errors (P > 0.05) compared to 100% certification. Thus, the direct measure of tenderness provided by slice shear force results in more accurate identification of "tender" beef carcasses than either of the indirect technologies, prototype BeefCam, or colorimeter, particularly for USDA Select carcasses. As tested in this study, slice shear force, but not the prototype BeefCam or colorimeter systems, accurately identified "tender" beef.