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Quality refers to the characteristics of products that meet the demands and expectations of the end users. Beef quality is a convergence between product characteristics on one hand and consumers’ experiences and demands on the other. This paper reviews the formation of consumer beef quality perception, the main factors determining beef sensory quality, and how to measure and predict beef eating quality at scientific and industrial levels. Beef quality is of paramount importance to consumers since consumer perception of quality determines the decision to purchase and repeat the purchase. Consumer perception of beef quality undergoes a multi-step process at the time of purchase and consumption in order to achieve an overall value assessment. Beef quality perception is determined by a set of quality attributes, including intrinsic (appearance, safety, technological, sensory and nutritional characteristics, convenience) and extrinsic (price, image, livestock farming systems, commercial strategy, etc.) quality traits. The beef eating qualities that are the most valued by consumers are highly variable and depend mainly on the composition and characteristics of the original muscle and the post-mortem processes involved in the conversion of muscle into meat, the mechanisms of which are summarized in this review. Furthermore, in order to guarantee good quality beef for consumers in advance, the prediction of beef quality by combining different traits in scenarios where the animal, carcass, and muscle cuts can be evaluated is also discussed in the current review.

This research study was conducted to evaluate treatments with UVC light and a combination of UVC and ozone that have recently received attention from the beef processing industry as antimicrobial interventions that leave no chemical residues on products. The effectiveness of UVC and UVC plus gaseous ozone treatments was evaluated for inactivation of pathogenic bacteria on fresh beef and for any impact on fresh beef quality. Fresh beef tissues were inoculated with cocktails of Shiga toxin-producing Escherichia coli (STEC) strains (serotypes O26, O45, O103, O111, O121, O145, and O157:H7), Salmonella, and Listeria monocytogenes. Inoculated fresh beef tissues were subjected to UVC or UVC-ozone treatments at 106 to 590 mJ/cm2. UVC treatment alone or in combination with ozone reduced populations of STEC, Salmonella, L. monocytogenes, and aerobic bacteria from 0.86 to 1.49, 0.76 to 1.33, 0.5 to 1.14, and 0.64 to 1.23 log CFU, respectively. Gaseous ozone alone reduced populations of E. coli O157:H7, Salmonella, and L. monocytogenes by 0.65, 0.70, and 0.33 log CFU, respectively. Decimal reduction times (D-values) for STEC serotypes, Salmonella, and L. monocytogenes on surfaces of fresh beef indicated that the UVC-ozone treatment was more effective (P ≤ 0.05) than UVC light alone for reducing pathogens on the surface of fresh beef. Exposure to UVC or UVC plus gaseous ozone did not have a deleterious effect on fresh meat color and did not accelerate the formation of oxidative rancidity. These findings suggest that UVC and UVC in combination with gaseous ozone can be useful for enhancing the microbial safety of fresh beef without impairing fresh beef quality.

This paper reviews recent research into predicting the eating qualities of beef. A range of instrumental and grading approaches have been discussed, highlighting implications for the European beef industry. Studies incorporating a number of instrumental and spectroscopic techniques illustrate the potential for online systems to non-destructively measure muscle pH, colour, fat and moisture content of beef with R 2 (coefficient of determination) values >0.90. Direct predictions of eating quality (tenderness, flavour, juiciness) and fatty acid content using these methods are also discussed though success is greatly variable. R 2 values for instrumental measures of tenderness have been quoted as high as 0.85 though R 2 values for sensory tenderness values can be as low as 0.01. Discriminant analysis models can improve prediction of variables such as pH and shear force, correctly classifying beef samples into categorical groups with >90% accuracy. Prediction of beef flavour continues to challenge researchers and the industry alike, with R 2 values rarely quoted above 0.50, regardless of instrumental or statistical analysis used. Beef grading systems such as EUROP and United States Department of Agriculture systems provide carcase classification and some indication of yield. Other systems attempt to classify the whole carcase according to expected eating quality. These are being supplemented by schemes such as Meat Standards Australia (MSA), based on consumer satisfaction for individual cuts. In Australia, MSA has grown steadily since its inception generating a 10% premium for the beef industry in 2015-16 of $187 million. There is evidence that European consumers would respond to an eating quality guarantee provided it is simple and independently controlled. A European beef quality assurance system might encompass environmental and nutritional measures as well as eating quality and would need to be profitable, simple, effective and sufficiently flexible to allow companies to develop their own brands.

A survey was conducted to quantify incidence of Beef Quality Assurance (BQA)-related defects in market beef and dairy cows and bulls selling at auction during 2 seasons in 2008. Twenty-three BQA-related traits were evaluated by 9 trained personnel during sales at 10 livestock auction markets in Idaho (n = 5; beef and dairy), California, (n = 4; dairy only), and Utah (n = 1; beef and dairy). Overall, 18,949 unique lots (8,213 beef cows, 1,036 beef bulls, 9,177 dairy cows, and 523 dairy bulls,) consisting of 23,479 animals (9,299 beef cows, 1,091 beef bulls, 12,429 dairy cows, and 660 dairy bulls) were evaluated during 125 sales (64 spring, 61 fall) for dairy and 79 sales (40 spring, 39 fall) for beef. The majority of market beef cows and bulls (60.9 and 71.3%, respectively) were predominantly black-hided, and the Holstein hide pattern was observed in 95.4 and 93.6% of market dairy cows and bulls, respectively. Market cattle weighed 548 ± 103.6 kg (beef cows), 751 ± 176.1 kg (beef bulls), 658 ± 129.7 kg (dairy cows), and 731 ± 150.8 kg (dairy bulls). Most beef cows (79.6%) weighed 455 to 726 kg, and most beef bulls (73.8%) weighed 545 to 954 kg, respectively. Among market beef cattle, 16.0% of cows and 14.5% of bulls weighed less than 455 and 545 kg, respectively, and 63.7% of dairy cows and 81.5% of dairy bulls weighed 545 to 817 kg or 545 to 954 kg, respectively. However, 19.5% of dairy cows and 13.1% of dairy bulls weighed less than 545 kg. Mean BCS for beef cattle (9-point scale) was 4.7 ± 1.2 (cows) and 5.3 ± 0.9 (bulls), and for dairy cattle (5-point scale) was 2.6 ± 0.8 (cows) and 2.9 ± 0.6 (bulls). Some 16.5% of beef cows and 4.1% of beef bulls had a BCS of 1 to 3, whereas 34.8% of dairy cows and 10.4% of dairy bulls had a BCS of 2 or less. Emaciation (beef BCS = 1, dairy BCS = 1.0) or near-emaciation (beef BCS = 2, dairy BCS = 1.5) was observed in 13.3% of dairy cows and 3.9% of beef cows. Among beef cattle, 15.1% of cows and 15.4% of bulls were considered lame. In contrast, 44.7% of dairy cows and 26.1% of dairy bulls were lame. Ocular neoplasia (cancer eye) was observed in only 0.6% of beef cows, 0.3% of beef bulls, 0.3% of dairy cows, and 0.0% of dairy bulls. However, among animals with ocular neoplasia, it was cancerous in 34.4% of beef bulls, 48.0% of dairy cows, and 73.3% of beef cows. In conclusion, numerous quality defects are present in market beef and dairy cattle selling at auction in the Western United States, which could influence their value at auction.

To characterize carcass and meat attributes, such as beef eating quality in specific farming conditions, 31 young grass-fed crossbred Angus x Salers cattle in two farming systems (a mono-cattle system versus a mixed system with beef cattle and sheep) were used in this study. Three muscle cuts (striploin—m. longissimus dorsi et thoracis; bolar blade—m. triceps brachii caput longum; internal flank plate—m. obliquus internus abdominis) were used for consumer eating quality testing and striploin was used for panelist eating quality assessment, and objective measurements [Warner–Bratzler shear force (WBSF) and fatty acid (FA) and antioxidant contents]. Results indicated that the farming system had no impact on carcass characteristics or meat quality, but it tended to affect FA content, which is likely explained by between-system differences in animal maturity (assessed by ossification score). Animal gender had significant effects on three eating quality traits evaluated by untrained consumers, with higher flavor liking, overall liking, and overall meat eating quality (MQ4) scores in females than in males. Additionally, FA contents were correlated with sensory quality traits to varying extents: consumer-scored tenderness, flavor, and overall liking were mainly positively correlated with ω-3 and ω-6 polyunsaturated fatty acid (PUFA) contents, and panelist-evaluated tenderness and abnormal flavor were more positively correlated with total lipids, saturated fatty acid (SFA), and monounsaturated fatty acid (MUFA) contents. Overall, this study showed that specific grass-fed crossbred Angus x Salers cattle can produce lean meat rich in ω-3 PUFAs with a low ω-6/ω-3 ratio and with “better than average” beef eating quality.

Delivering beef of consistent quality to the consumer is vital for consumer satisfaction and will help to ensure demand and therefore profitability within the beef industry. In Australia, this is being tackled with Meat Standards Australia (MSA), which uses carcass traits and processing factors to deliver an individual eating quality guarantee to the consumer for 135 different ‘cut by cooking methods’ from each carcass. The carcass traits used in the MSA model, such as ossification score, carcass weight and marbling explain the majority of the differences between breeds and sexes. Therefore, it was expected that the model would predict with eating quality of bulls and dairy breeds with good accuracy. In total, 8128 muscle samples from 482 carcasses from France, Poland, Ireland and Northern Ireland were MSA graded at slaughter then evaluated for tenderness, juiciness, flavour liking and overall liking by untrained consumers, according to MSA protocols. The scores were weighted (0.3, 0.1, 0.3, 0.3) and combined to form a global eating quality (meat quality (MQ4)) score. The carcasses were grouped into one of the three breed categories: beef breeds, dairy breeds and crosses. The difference between the actual and the MSA-predicted MQ4 scores were analysed using a linear mixed effects model including fixed effects for carcass hang method, cook type, muscle type, sex, country, breed category and postmortem ageing period, and random terms for animal identification, consumer country and kill group. Bulls had lower MQ4 scores than steers and females and were predicted less accurately by the MSA model. Beef breeds had lower eating quality scores than dairy breeds and crosses for five out of the 16 muscles tested. Beef breeds were also over predicted in comparison with the cross and dairy breeds for six out of the 16 muscles tested. Therefore, even after accounting for differences in carcass traits, bulls still differ in eating quality when compared with females and steers. Breed also influenced eating quality beyond differences in carcass traits. However, in this case, it was only for certain muscles. This should be taken into account when estimating the eating quality of meat. In addition, the coefficients used by the Australian MSA model for some muscles, marbling score and ultimate pH do not exactly reflect the influence of these factors on eating quality in this data set, and if this system was to be applied to Europe then the coefficients for these muscles and covariates would need further investigation.

Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

To identify the effects of the expression levels of DNMT3a and DNMT3b , coding the de novo methyltransferases DNMT3a and DNMT3b, on 16 beef carcass and quality traits, 50 beef cattle liver and ribeye muscle tissue samples were collected. Quantitative real-time RT-PCR was employed to quantify the expression level of these two genes, and a basic model included fixed effects of gender, age, and expression level of these two genes was used to analyze live weight; and slaughtering batches and aging days were added when beef carcass traits and beef quality traits were analyzed, respectively. Results showed that transcripts of DNMT3a and DNMT3b were present at significantly higher levels in liver tissue than in muscle tissue, and the expression level of DNMT3a was significantly higher than that of DNMT3b in both tissues. Regression analysis found that the expression levels of DNMT3a and DNMT3b were associated with several beef quality traits, which are important in beef breeding. Findings of the present study suggested that these two genes could significantly contribute to the improvement of beef quality genetically.

Phosphate has been widely used in beef to improve processing characteristics such as tenderness and water-holding capacity. However, the effects of phosphates on the quality and especially the flavor of beef are not well understood. This study investigated the influence of eight different phosphate marinade solutions on the quality and flavor of prepared beef. The results revealed that the thawing loss in the control group was 11.47%, and NaCl with sodium hexametaphosphate (SYCP) had the lowest thawing loss, with a value of 2.13%, which was reduced by 81.43% as compared to the control group. The shear force of the control group was 3.85 kg, and the shear work was 10.03 kg. The best tenderness was recorded in the NaCl with sodium hexametaphosphate (SYST) group, which had a shear force of 1.14 kg and shear work of 3.34 kg. The incorporation of phosphates suppressed fat oxidation and increased the total free amino acid content. Additionally, the levels of certain key volatile flavor compounds, particularly those associated with fat oxidation, such as hexanal, heptanal, octanal, and nonanal, were reduced. In terms of sensory evaluation, juiciness, flavor, tenderness, and overall acceptability in the treatment group were significantly increased (p < 0.05). Overall, the results indicate that adding phosphates can enhance the quality of processed beef, inhibit lipid oxidation, and improve sensory evaluation.