Quality of Beef

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1		Jingjing LIU	--	5920	2022-06-28 20:01:09	\|
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This entry is adapted from the peer-reviewed paper 10.3390/foods11121732

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.

beef eating quality consumer perception pre‐ and post‐mortem determinisms beef grading scheme

1. Main Factors Affecting Beef Eating Quality

1.1. Antemortem Factors Affecting Beef Eating Quality

1.1.1. Breed

Bos taurus and Bos indicus are the two main cattle breed groups in the world. They are genetically adapted to survive with high productivity in adverse conditions, including heat, drought, and poor-quality pastures. It is well known that the meat produced by Bos indicus cattle tends to be of lower quality ^[1]. Indeed, some beef cuts from Bos indicus cattle can be tough due to the genetical effect on the calpain-calpastatin system, muscle fiber size, and metabolic properties, which result in inhibited protein degradation and ultimately decreased sensory tenderness ^[2]. This has led some labeling systems to exclude Bos indicus meat from their certified brands, thus hindering the presence of Bos indicus meat in major markets ^[3]. In fact, it has been observed that other differences such as IMF deposition and FA profile of meat produced by Bos taurus and Bos indicus cattle depend mainly on feeding system ^[4].

Breed-related differences in beef eating quality have long been discussed with respect to grow path and age at physiological maturity, which are mainly reflected in muscle structure, the content and solubility of connective tissue and the amount, and the composition and distribution of adipose tissue, especially IMF in beef ^[5]. The beef produced by the Wagyu breed, notably characterized by its intense marbling ^[6], has a more intense flavor and juiciness than that of the Angus breed ^[7]. Intramuscular adipose tissue matures late and accumulates as the animal grows and matures, with IMF being deposited after intermuscular fat, which is itself deposited after subcutaneous fat ^[8]. Therefore, at similar levels of maturity, early maturing breeds (e.g., traditional British beef breeds such as Angus and Hereford) have a tendency to deposit more IMF and can be slaughtered at lower weights with a higher fat content, compared to late-maturing beef breeds (e.g., continental European breeds such as French Limousin, Charolais, Blonde d’Aquitaine and Belgian Blue), which have relatively less IMF. With a different level of marbling, the beef eating quality is therefore different for these two types of breeds.

Beef quality is multi-determined and must be analyzed based on many factors to avoid a biased comparison. In general, the eating quality of meat from beef breeds is considered better than dairy breeds. However, untrained consumers reported hardly any differences in eating quality between meat from dairy and beef breeds, except for a few muscles ^[9]. Although breed has an important effect on beef sensory quality, beyond differences in carcass characteristics, breed might explain only a small part of the variability in beef quality or sometimes may not explain it at all. For instance, Conanec et al. (2021) observed very few differences in beef sensory quality between beef aged under the same conditions and produced by young bulls from 15 European breeds reared under relatively similar conditions ^[10].

1.1.2. Sex

There are several differences between sex categories related to hormone level and muscle composition and in interaction with genotype ^[11]. Heifers are identified as more tender than bulls and steers with less intramuscular connective tissue content and smaller muscle fiber diameter ^[12]. In contrast, bulls grow more rapidly and produce carcasses with less fat and more red-oxidative muscle than steers ^[13]. Steers are rated less tough and more palatable with more IMF compared to bulls ^[14]. Moreover, it was found that even after adjusting for different carcass traits, meat from bulls had lower eating quality scores than meat from females and steers ^[9]. Sex also influences meat color especially in combination with age; female animals tend to deposit more pigment with age than males ^[15]. However, in general, due to higher physical activity and myoglobin concentrations, the meat from intact males is darker than that of females and castrated males ^[16]. Nonetheless, in practice, the meat from females usually comes from dairy cows or cull cows slaughtered at a later age, which usually results in a darker color ^[17].

1.1.3. Animal Age and Maturity

In general, increasing age and maturity is correlated with a decrease in eating quality. With increasing age and maturity of the animal, the collagen content increases and the heat stability of the collagen declines, the shear force and toughness of the cooked beef proportionally increase ^[18]. Meat color and fat color are generally influenced by animal age, with L* and a* values higher for older animals than younger ones ^[19]. Meanwhile, older animals tend to contain more fat, and the percentage of IMF increases with a concomitant increase in the percentage of monounsaturated fatty acids (MUFAs) and a decrease in that of PUFAs, and this is associated with better flavor intensity ^[20], but lower healthiness due to higher proportions of saturated fat. In addition, it was found that the decrease in tenderness appears to be less pronounced with beef from animals over 18 months of age compared to animals under 18 months of age, although this is animal- and muscle-dependent ^[21]. Moreover, the flavor intensity of beef tends to increase up to the age of 18 months and thereafter reaches a plateau ^[22]. Kopuzlu et al. (2018) also found for Eastern Anatolian Red bulls that beef tenderness, juiciness, flavor, and overall acceptability increased until the animals reached 19 months of age ^[19].

1.1.4. Feeding System, Fat Content and Marbling

The feeding system has effects on beef quality since the nutrient composition and energy intake of the diet can affect the animal’s growth rate, degree of maturity, and carcass composition, particularly the amount of IMF and the FA profile ^[23]. Diet composition and finishing management have different effects on beef quality traits, especially for different animal categories. Specific analyzes are therefore necessary to determine the impact of feeding on beef sensory quality in specific circumstances, such as when animals are inconsistently characterized. In the beef industry, different finishing systems are used, resulting in beef product variations. In general, beef produced in extensive production systems is considered to have a healthier FA composition, and pasture-based feeding strategies are developed for this purpose as consumers prefer grass-fed beef as it is perceived to be healthier and “greener” ^[24]. However, scientists found that rearing systems (indoor rearing vs. outdoor grazing) had no major impact on Warner Bratzler shear force (WBSF), texture profile, WHC, and most of the sensory attributes of m. longissimus dorsi lumborum from Podolian beef ^[25]. However, in general, due to higher IMF accumulation, grain-finished beef from feedlot systems is perceived as superior to that of grazing systems and/or forage/pasture-finished cattle, which tend to produce leaner beef ^[26]. In addition, from the perspective of eating quality, some consumers prefer grain-fed/finished beef because pasture/forage-fed/finished beef contains specific pastoral flavors such as “grassy”, “wild” and “barny” and lacks the normal beef flavor ^[27]. In contrast, forage/pasture-finished beef generally has an increased conjugated linoleic acid (CLA) and PUFA to SFA (saturated fatty acid) ratio ^[28], which is better for human health, especially in reducing the incidence of many diseases such as heart and cardiovascular diseases. Additionally, pasture quality is an important element in differentiating beef quality. Therefore, meat from pasture-fed cattle may not only be of comparable quality to meat from grain-fed animals ^[29] but may even be more tender ^[30].

Fat is not defined as a basic sensory trait but provides meat with specific mouthfeel and lubrication between muscle fibers that could increase the perception of tenderness and juiciness, and in particular provides meat with a flavor profile and aromas ^[31]. Numerous studies have investigated the relationship between IMF/marbling and beef sensory quality. It has been reported that 10–15% of the variance in tenderness evaluation could be explained by marbling ^[32], and that 2% to 56% of the variation in flavor could be explained by IMF content ^[33]. Although no evidence shows that excess fat leads to a progressive increase in flavor and palatability ^[34], higher IMF content could lead to diminishing returns on beef sensory traits. Undoubtedly, a range of acceptability for IMF and marbling could improve beef eating quality ^[35]. However, significant but varied associations with sensory quality attributes are often observed as this relationship is highly dependent on confounding factors, including animal breed, age, and sex. Nevertheless, several studies agree that there is a curvilinear relationship between IMF content and beef flavor; flavor intensity increases with IMF content, then reaches a plateau at higher levels of IMF ^[36]^[37].

1.1.5. Pre-Slaughter Stress

Prior to slaughter, animals are exposed to certain situations that can trigger a stress response that can reduce the eating quality of the meat. Improper handling during transport and at the abattoir can lead to muscle glycogen depletion and inadequate acidification and ultimately high pH, resulting in dark cut beef and reduced sensory tenderness ^[38], juiciness, and flavor ^[39]. However, higher pH is not always the only reason for the reduced quality of stressed cattle. This has been confirmed by several studies: pre-slaughter stress was found to have a negative impact on consumer-assessed eating quality, even if the ultimate pH of the carcass was compliant (pH ≤ 5.7) ^[40] and, with a compliant pH, WBSF was higher in stressed cattle ^[38].

There is no doubt that pre-slaughter stress is associated with lower beef eating quality and it has been demonstrated that mixing and transporting animals prior to slaughter was associated with lower eating quality for some cuts and that a two-week rest period in the slaughterhouse prior to slaughter is beneficial in improving consumer perception of beef sensory scores ^[41]. The beef industry and some quality grading systems, such as MSA, have developed pathways to minimize the adverse effects of physical activity and emotional stress prior to slaughter. For instance, different lairage periods are recommended according to the transport journey to enable animals to rest, rehydrate, and replenish their glycogen stores ^[42]. In the MSA system, some pre-slaughter pathways that may maximize stress could be penalized such that cattle sold in the saleyard prior to slaughter are deducted 5 points from the final MSA meat quality score ^[43].

1.2. Post-Mortem Factors Affecting Beef Eating Quality

1.2.1. Post-Harvest Aging and pH/Temperature Decline

Post-harvest aging is a value-adding process which involves storing the carcass at cold temperatures for varying periods of time, profoundly affecting the biophysical and biochemical modification conditions of the carcass through regulating post-mortem energy metabolism, proteolysis, and apoptosis ^[44]. These processes lead to a progressive increase in tenderness and flavor with the disintegration of muscle structure and the release and accumulation of peptides and free amino acids. Based on theoretical knowledge, several practical adjustments could be implemented to improve beef palatability with some treatments such as aging, with some breeds showing optimum tenderness at short aging periods and other breeds requiring longer aging to achieve similar consumer acceptance ^[45]. Several beef grading systems use aging time as a parameter to guarantee/predict beef quality. According to MSA, five days of aging is required as a minimum aging period; for the French Label Rouge, ten days is generally considered ^[27]. Longer aging times up to a certain level are generally good for better palatability; for m. longissimus dorsi, it takes 4.3 and 10 days to reach 50 percent and 80 percent of total aging, respectively. The aging process affects muscles differently: slow-twitch muscles are thought to age more slowly than fast-twitch muscles ^[46]. The tenderness of m. psoas major and m. semitendinosus needs 7 and 14 days to improve, whilst m. longissimus lumborum can achieve the most tender score at 21 days ^[47].

1.2.2. Electrical Stimulation (ES)

Two mechanisms could explain the effect of ES on tenderization. The primary effect is to reduce cold shortening by accelerating glycolysis and rapid pH drop to avoid the temperature drop at which cold toughness occurs ^[48]. The secondary effect is to accelerate proteolysis by stimulating the release of Ca ions at a higher temperature ^[49] and to increase disruption of muscle structure ^[27]. Based on these two effective tenderization mechanisms, ES has therefore been applied in the worldwide meat industry for decades to achieve optimal tenderization, especially in combination with pH/temperature controls. It has been reported that when carcasses were electrically stimulated and held at 35 °C for 3 h, a fast drop in pH to 6 and significant increases in μ-calpain activity and ultimately in tenderness were observed ^[50]. In addition to the beneficial effect of ES on tenderness, some improvements are observed regarding juiciness and flavor and overall satisfaction ^[51], as the perception on those sensory traits in electrically stimulated meat is more impacted by the fat content ^[52].

The voltage of ES has long been investigated, with the use of high-voltage ES (3600 V) being first investigated ^[53], followed by low-voltage ES (32 V) ^[54], which was more used in the industry due to safety concerns. In fact, high and low voltage ES with different durations can achieve the same tenderization effect ^[55]. Recent research has focused on combining chilling methods (Tenderstretch, super chilled storage, and long aging time) with new technologies such as the so-called new generation medium voltage ES. The tenderness of meat subjected to medium voltage ES has been improved due to various reasons such as physical disruption of the muscle structure ^[56] and myofibrillar degradation ^[57].

1.2.3. Carcass Suspension

Several hanging methods have been used to improve meat tenderness during post-mortem aging. Achilles tendon is the most traditional and widely used carcass suspension method, although it cannot prevent the majority of muscle shortening, but, with the appropriate aging process, Achilles tendon still can achieve the tenderization potential of beef cuts ^[27]. In comparison with Achilles tendon, Tenderstretch increases tension and results in more tender meat, but this varies between muscles, with improved eating quality in most hindquarter muscles ^[58]. In general, different muscles could respond differently to post-mortem aging and, therefore, muscle-specific aging strategies could improve tenderness and overall eating quality ^[47]. In fact, Tenderstretch could effectively shorten aging time and improve beef tenderness by up to 40% ^[59], and indeed performs better on improving beef sensory quality (flavor, juiciness, and overall liking) than that of Achilles tendon ^[60].

2. Main Methods for Measuring and Predicting Beef Quality

2.1. Mechanical Measurement of Beef Quality

2.1.1. Physical Texture Measurement

Evaluation of beef quality is complicated, especially with respect to sensory quality, which in reality can only be measured by consumers or sensory panels ^[61]. However, since consumer evaluation is time-consuming and costly, it cannot be widely used for all quality measurements. A widely-used method of evaluating meat quality is to measure the physical texture of meat products. The physical texture of beef is mainly related to mechanical attributes, which are generally characterized by hardness, cohesiveness, viscosity, springiness, and adhesiveness ^[62]. Mechanical measurements of the strength required to break down the meat are mainly categorized as shearing, biting, compressing a standardized piece of meat. The most commonly used measurement for meat toughness/tenderness is the WBSF. The Slice shear force (SSF) is a faster alternative to WBSF but is less used ^[63]. For overall physical texture, there is the texture profile analysis (TPA), and some devices are used such as the MIRINZ tenderometer with a biting action for measuring overall tenderness of meat ^[61]. The WBSF was found to be more effective in classifying beef as tender (68% accuracy) than the SSF (47%), compared to consumer perceived sensory tenderness (80%) ^[64]. Many studies have tried to relate the meat physical texture measurement to consumer-rated tenderness/mouth-feel-taste, with physical measurements being able to explain a variable variation in tenderness assessed by human panels but no more than 60% ^[61]. Platter et al. (2003) found that WBSF can only explain 23% of the total variance of consumer-scored tenderness ^[65]. Various correlations between WBSF and consumer evaluated tenderness have been observed, ranging from low (e.g., r = −0.19, −0.26) ^[66] to high values (e.g., r = −0.72, −0.82) ^[67]. Different factors such as aging process, cooking temperature ^[68], and muscle cuts ^[69] might contribute to these inconsistencies. Except for the above, the lack of strong correlations between physical shear force and consumer-perceived tenderness indicates that they seem to be two non-equivalent issues, the latter being not only related to mechanical force but also associated with sensations generated by moisture and fat within the meat.

2.1.2. Juiciness Measurement

According to a National Beef Tenderness Survey conducted in the United States at the food service and retail level, over 94% of rib and loin beef were rated tender or very tender. Such a large proportion of tender beef has magnified the importance of juiciness and flavor to the consumer eating experience ^[70]. This is the reason why the importance of beef sensory traits has renewed attention from meat scientists in recent years. For many years, tenderness was considered as the dominant factor in determining eating quality and with the clarification of a higher contribution of flavor liking to overall consumer satisfaction, the importance of juiciness should not be neglected ^[71].

The measurement of juiciness has previously focused on total water content, WHC, and water fractions of meat, although the consistency between sensory juiciness and these parameters varies ^[72]. One of the reasons could be that the meat evaluated by consumer or panels has been cooked, which means that with physical/chemical alterations and intra- and extra-myofibrillar water movements, the perception of juiciness may be altered. Cooking loss, drip loss, and compression-based methods have been usually used to quantify expressible moisture in meat. Cooking loss has been reported to be able to explain 60–80% of the juiciness variance ^[73], but it has also been reported that cooking loss cannot explain the juiciness of cooked meat due to heat-induced changes ^[72]^[74]. Compression-based methods have evolved from filter paper press methods from the Carver hydraulic press apparatus, the Instron-based press method to the pressed juice percentage (PJP) method with various capabilities to predict juiciness scored by a sensory panel ^[75]. PJP was observed to be strongly correlated with sensory juiciness scored by trained and untrained consumers (r = 0.69, 0.45), respectively. IMF content can also be a good indicator of juiciness. Thompson (2004) found that consumers were satisfied and dissatisfied with beef juiciness when IMF was above 20% or below 2%, respectively ^[37]. However, it is difficult to define a threshold of juiciness for consumer perception based on IMF content due to the different distribution of IMF ^[76].

2.1.3. Flavor Measurement

Flavor is perceived by consumers through two pathways, namely odor detected by the nose and taste perceived by the mouth and tongue. There are receptors on the olfactory bulb in the nose and mouth that detect volatile compounds; when they come into contact with the olfactory bulb and are recognized by these receptors, flavor is thus perceived. In addition to these volatile compounds, there are volatile aromatic compounds generated in the mouth during chewing or swallowing of meat. However, the amount or types of receptors and the amount or concentration of volatile compounds needed for perception vary between individuals ^[22]. The perception of flavor is therefore complicated to define due to the individual diversity of the taster. This is the reason why meat flavor is further described and assessed by highly trained descriptive attributes with panelists, which are the most accurate methods for measuring meat flavor.

Mechanical measurement of flavor on the basis of consumer perception is challenging due to the complexity of the meat matrix and consumer perception. In recent decades, significant progress has been made in identifying and quantifying meat flavor compounds ^[77]. Thousands of volatile compounds have been identified as constituting the aromas of meat odor/flavor using mechanical and/or chemical measurements such as olfactometry, flame ionization detection (FID), and thiobarbituric acid reactive substances (TBARS). TBARS have been shown to have a predictive ability for the consumer’s flavor liking threshold, but this is highly dependent on the method used for TBARS determination. FA profile can contribute to consumer flavor liking, as CLA, SFAs, and MUFAs have been associated with flavor liking, although some effects are muscle-dependent ^[78]. Additionally, the electronic nose (e-nose) and electronic tongue (e-tongue) are also useful tools for evaluating meat flavor attributes ^[79].

2.2. Non-Destructive Instrumental Methods for Beef Quality Prediction

There has been a demand to predict beef quality by non-destructive instrumental methods, which are considered as having many clear-cut advantages, such as ease of use, non-destructiveness, speed, cost-effectiveness, reproducibility, and a high potential accuracy ^[80]. Ongoing work with various emerging technologies has been conducted with the aim of predicting beef quality directly or indirectly, i.e., predicting consumer sensory attributes directly related to quality, such as tenderness or flavor, or predicting indirect quality-related parameters that have been shown to have an impact on meat quality such as meat color, pH, IMF content, or marbling ^[81].

The use of Near-InfraRed Spectroscopy (NIRS) to predict the chemical composition, technological parameters, and sensory feature, of meat quality attributes, such as WBSF values and trained panel or untrained consumer sensory scores, is a topic with important applications in meat plants, as both WBSF and sensory measurements are time-consuming and destructive; however, due to the complexity of predicting these attributes, the determinant coefficients proposed in the literature are variable. NIRS can correctly detect 80–95% dark cut beef depending on the instrument used ^[81]. Several studies have suggested that the sensory quality of meat can be accurately predicted by NIRS but with relatively low accuracy (R² = 0.10–0.58) ^[82]^[83], although Ripoll et al. reported that beef tenderness could be predicted by NIRS with high accuracy (R² = 0.98) ^[84]. Computer vision techniques have been utilized to visually assess meat quality in the processing line as they are non-invasive and consistent to assessing color, IMF and, most importantly eating quality ^[85]. It has been reported that computer vision has the ability to assess marbling and predict quality attributes with R² values for tenderness (0.72), WBSF (0.83), juiciness (0.60), flavor (0.78), and overall consumer acceptability (0.82), respectively.

2.3. Omics Approaches

2.3.1. Genomics

The criteria for defining consumer beef eating quality are based on several traits (e.g., tenderness, juiciness, and flavor and so on), which are quantitative traits determined by sets of components regulated by the joint action of numerous genes and environmental regulations (growth, rearing and processing factors) ^[86]. Each individual component contributing to the palatability phenotype is consequently difficult to control and costly to measure. All beef eating quality traits are difficult to improve based merely upon phenotypic selection, but there may be effective candidate genes for genomic selection if genetic markers that account for a significant variance for those quality traits are identified ^[87].

Within the meat sector, numerous genes have been identified as being involved in valuable estimates of genetic parameters. They provide key insights into the regions that underpin variation in physical meat characteristics, including muscle fibers, connective tissue, IMF, meat color, fat color, shear force, and sensory meat quality traits such as tenderness, juiciness, flavor, chewiness and so on ^[88]. So far, some sensory-related traits including tenderness and color have been confirmed with notable representations of related biomarkers on chromosomes ^[89]. Despite its relevant potential to predict meat quality variation, some limitations have still been noted, the most common being that, thanks to numerous association studies, predictive information can be obtained but not deep scientific knowledge of the underlying mechanisms, at least in the earliest stages of omics development. Moreover, predictive reliability appears to be less consistent, in particular with human-evaluated meat eating quality. For instance, recent heritability estimates for tenderness, juiciness and flavor scores range from 0.1 to 0.2 ^[90]. This indicates that the proportion of variability in beef eating quality explained by genetic factors is moderate to weak.

2.3.2. Proteomics

An emerging body of literature has examined the proteomic pathways involved in meat eating quality variations ^[91]. All these works also contribute to the elucidation of the biological mechanisms involved in muscle to meat conversion and in meat qualities ^[92]. Despite the many factors regulating beef eating quality, and therefore the large number of biomarkers involved in the regulation of quality by these factors, with more and more results from proteomic studies, robust candidate biomarkers can still be identified due to their consistent associations with meat qualities. Gagaoua et al. (2019) found some biomarkers that related to muscle structure (MyHC-I, MyHC-IIa, MyHC-IIx), oxidative stress (DJ-1, PRDX6), and proteolysis (CAPN1) that were consistently associated with tenderization of longissimus thoracis muscle. Despite various results depending on animal breeds (Aberdeen Angus, Limousin, and Blond d’Aquitaine), end-point cooking temperature of beef (55 or 74 °C), and consumer origin (France and UK), some of these biomarkers performed as robust predictors for tenderness ^[93]. Protein network research has revealed the functional annotation of 124 proteins in the longissimus dorsi muscle, which are crucial in the production of high-quality beef ^[89]. More and more integrated proteomics studies have been carried out to create a repertoire of biomarkers, especially for beef quality defects (i.e., dark, firm, and dry beef). The ultimate goal of these biomarkers is to guarantee the eating quality for consumers by proposing a list of validated biomarkers for the development of routine bioanalytical tools to be used by breeders and producers to improve the potential merits of breeds and to detect potential quality during the pre- and post-mortem periods ^[89].

2.3.3. Metabolomics

Skeletal muscle is characterized by a set of functionally cooperative genes designed to address the spatiotemporal requirements of each muscle. Gene expression is then regulated, including protein modification, during muscle development, growth, and maturation. In the later stages, muscle metabolites determine the muscle characteristics, which are the major phenotypic components of meat eating quality. During the development and physiological specialization of muscle, many well-known factors all impact on the genome, transcriptome, and proteome profiles of muscle, making it very difficult to understand the precise mechanisms behind meat quality variations through these molecular markers ^[94]. Nonetheless, changes in muscle metabolome profiles (small hydrophilic molecules/metabolites such as polyphenols, organic acids (carnitine, creatine, and carnosine), amino acids, vitamins and minerals and so on) can be quantified by metabolomics as potential indicators reflecting the metabolic process and screened to predict sensory quality ^[95]. For example, Ma et al. (2017) reported that an increase in the amount of free amino acids was associated with the degree of proteolysis, which suggests more tender meat, but also with more precursors of aromatic compounds that play a role in the sensory aspects of cooked meat ^[96]. Furthermore, Antonelo et al. (2020) found a positive correlation between carnitine and consumer acceptance of beef steaks, while strong negative correlations were observed between carnitine and creatine and consumer sensory scores for tenderness, juiciness, and overall liking ^[97].

2.4. Grading Schemes for Beef Eating Quality

With the advancement of international trade of beef carcasses, carcass classification standards and beef quality grading schemes are required to provide a description of carcasses and muscle cuts with the definition of quality to purchasers and destination markets ^[98]. Based on this objective, two categories of grading schemes, based on carcass and muscle cut, have been used to classify carcasses and predict beef quality.

2.4.1. Carcass-Based Grading Schemes of Beef Quality

A small number of countries have carcass grading schemes to directly predict beef eating quality. Most of them focus more on a generic scenario of beef quality in relation to carcass characteristics. The current carcass-based grading systems in these regions (mainly Europe, USA, and Japan) primarily encompass two categories of carcass classification, namely yield and quality grading.

Yield is determined by various criteria depending on the system but basically can be defined as lean or saleable meat yield and can be determined by carcass weight and composition. In the USDA (United States Department of Agriculture) system, the yield grading is an indication of yield of boneless, trimmed retail cuts. The JMGA (Japanese Meat Grading Association) yield grade refers to the proportion of meat produced by the animal that can be eaten and is determined by eye muscle area, rib thickness, cold left side carcass weight, and subcutaneous fat thickness through a regression calculation ^[99]. In contrast with the USDA and JMGA systems, which have a parallel quality evaluation criterion related to beef palatability, the European classification system places emphasis only on the description of production yield rather than beef eating quality. The EUROP grid is established to classify carcasses according to the assessment of carcass weight, muscle shape, and fat level, described by conformation score and fat score, respectively ^[100]. Since the EUROP grid is widely applied and regarded as traditionally important for the European beef industry, carcasses are assigned and traded to differentially priced sales markets according to the European classification scores ^[101]. However, meat experts have gradually become aware of the weakness of the EUROP grid nowadays within Europe, as European classification scores have little relation to eating quality at consumer level and cannot reflect carcass composition ^[102] and consumer satisfaction ^[103].

Carcass maturity and IMF level (marbling) are two major attributes that are used for quality segments. For example, according to the combinations of maturity and marbling level, carcasses can be graded into one of eight categories as in the USDA system. Maturity indicates the physiological age of the animal (ossification, dentition) rather than the chronological age. The amount and distribution of marbling on the m. longissimus dorsi are critical assessments in most carcass grading systems due to the strong association between marbling score and beef palatability. In the USDA, graders evaluate marbling between the 12th and 13th ribs, but in the JMGA, carcass grading is performed at the rib site, between the 5th and 6th ribs ^[104]. Recently, carcass grading with marbling assessment was conducted in a French private meat plant. It was found no significant difference in marbling score between the 5th and 10th ribs, such that marbling score could be measured at the quarter carcass level ^[105]. This could provide a theoretical basis for the introduction of marbling score in the European carcass grading system.

2.4.2. Cut-Based Grading Scheme—The MSA Grading System

Unlike the aforementioned carcass grading schemes, the MSA grading system is a beef eating quality grading system, aimed at delivering an eating quality guarantee to consumers. There are two ways in which the MSA system differs from other grading schemes: (1) the grading of beef quality is based on each of the MSA muscle cuts rather than the whole carcass; (2) the definition of eating quality depends on the responses of untrained consumers ^[98], and actual consumer performance has been shown to be consistent with a high degree of accuracy when tasting samples with a wide range of quality variance ^[106].

In the MSA prediction model, different Critical Control Points (CCPs) have been used from the breeding, production, pre-slaughter, processing, and value-adding aspects of the supply chain that have an impact on eating quality. In addition, consumer preference is evaluated through large-scale consumer testing ^[98]. The parameters used in the prediction model are based on inputs including: animal type and production (Bos indicus content, hormone growth promotants, milk fed veal classification and sex, sale yard, and selling method), carcass characteristics (carcass weight, ossification score, hump height, USDA marbling score, rib fat depth, and ultimate pH), post-slaughter factors (hanging method and aging time), the prediction of beef eating quality being provided for different muscle cuts, and various cooking methods.

Another crucial part of the MSA system is the extensive use of sensory testing of beef by untrained consumers to develop a combined eating quality score (MQ4, 0–100) based on tenderness, flavor liking, juiciness, and overall liking. To link the carcass characteristics with consumer valued palatability, carcass production and grading inputs (that are statistically related to this combined eating quality score (MQ4)) are combined to form eating quality prediction algorithms for specific muscle cuts (39 cuts in total) in combination with a defined aging period and one of eight different cooking methods ^[43]. Meanwhile, beef samples are graded by consumers as Unsatisfactory (2 star), Good Everyday (3 star), Better Than Everyday (4 star), and Premium quality (5 star). These categories should correspond to the MQ4 score, and this connection enables the muscle cuts to be allocated to these four quality grades ^[43]. Consequently, beef can be classified into grades that correspond to consumer expectations. The consumer’s willingness to pay for these grades has been estimated from consumers’ answers: if a 3-star beef is set at a unit monetary value of 1, then 2-star, 3-star, 4-star, and 5-star quality graded products can be subsequently valued at 0.5, 1, 1.5 and 2 respectively ^[107].

The MQ4 score can be used to reflect the overall consumer eating experience of a muscle cut ^[43]. The eating quality value of a whole carcass, termed MSA Index, can also be predicted in MSA grading scheme. The MSA index is the sum of the weighted MQ4 scores of all MSA cuts (39 muscle cuts), where the weighting of each cut was calculated as the percentage of the total weight of the MSA cuts in the carcass. The MSA Index is used to value the potential eating quality of beef carcasses and enables producers to monitor the impact of genetic and breeding practices and management on the eating quality of each carcass ^[108].

2.4.3. The Future Grading Scheme for Beef Palatability in Europe and Other Countries

There are various grading schemes to evaluate beef quality with different standards. In addition, different rearing and feeding systems, environmental conditions, animal type, breed, and processing practices add to the variability of quality evaluation between countries. However, for scientific research on meat quality evaluation for further industrial applications, there is a need to develop and/or share a set of generic principles and/or establish an international database containing a significant number of assessments on beef quality traits from different countries ^[109]. The MSA protocol has always been considered as a good standard with critical steps including rigorous beef carcass assessment and untrained consumer evaluation of beef palatability. Over the past two decades, independent and/or collaborative studies have concluded that this consumer-focused and cooking- and cut-based quality grading scheme is applicable for many countries such as Ireland, the United States, South Korea, Northern Ireland, Japan, France, Poland, South Africa, and China. It would therefore be very useful to have a platform for comprehensive data pooling and analysis to maximize research efficiency for the benefit of the global beef industry ^[109].

To this end, a collaborative, non-profit, and independent foundation, the International Meat Research 3G Foundation (3G Foundation), has been established to improve consumer satisfaction of beef quality by promoting worldwide collaborative meat research throughout the bovine supply chain. The platform is designed to coordinate and support global scientific research on beef quality evaluation and prediction by collecting a large amount of data based on a standard methodology (MSA) for further data sharing and modeling and ultimate integrative investigations on beef quality prediction.

2.4.4. Advanced Technology in Consumer Perception of Beef Quality

Human sensory evaluations are applied as useful tools for generating data for the description, discrimination and prediction of meat eating quality. However, time and cost constraints, as well as the lack of flexibility required for successful commercialization, make human sensory testing unsuitable for today’s rapidly changing industrial environment ^[89]. Moreover, the data generated by human evaluation shows great variability between individuals and the information required for quality perception is becoming increasingly complex as consumer purchasing decisions become more sensitive to both intrinsic and extrinsic factors, including nutritional quality and safety, animal welfare, and environmental and agroecological sustainability. Hence, human sensory evaluations are to some extent heterogeneous methods for generating meat eating quality data based on different emotions, attitudes and responses that are influenced by different intrinsic and extrinsic cues. To better reflect real-world consumer assessments, with the expansion of beef industry into emerging markets, there is a trend to develop and adopt novel and rapid sensory techniques (i.e., Check All That Apply, Napping, Flash Profile, Temporal Dominance of Sensations) to produce data from conventional methods (Quantitative Descriptive Analysis) ^[110] in order to better understand complex consumer perceptions. Virtual reality is also being used as a tool to improve the analysis of consumer perception on food quality with more realistic parameters through the measurement of consumer psychological and physiological responses ^[110]. Overall, in order to reduce the variability due to human involvement in meat quality definition and to increase the efficiency of meat quality prediction, novel advanced techniques and methods have been explored and implemented in a more holistic way, taking into account various aspects of consumer quality perception. More information on consumer perception and prediction of meat quality would help to establish a greater degree of accuracy in this area.

References

EBLEX Quality Standard Mark Scheme for Beef and Lamb. Available online: https://issuu.com/ahdb1/docs/scheme-operating-guide (accessed on 23 January 2022).
Wright, S.A.; Ramos, P.M.; Johnson, D.D.; Scheffler, J.; Elzo, M.A.; Mateescu, R.G.; Bass, A.L.; Carr, C.C.; Scheffler, T.L. Brahman genetics influence muscle fiber properties, protein degradation, and tenderness in an Angus-Brahman multibreed herd. Meat Sci. 2018, 135, 84–93.
Bressan, M.C.; Rodrigues, E.C.; Rossato, L.V.; Neto-Fonseca, I.; Alves, S.; Bessa, R.J.; Gama, L.T. Discrimination of Meat Produced by Bos taurus and Bos indicus Finished under an Intensive or Extensive System. Animals 2020, 10, 1737.
Bressan, M.C.; Rossato, L.V.; Rodrigues, E.C.; Alves, S.P.; Bessa, R.J.B.; Ramos, E.M.; Gama, L.T. Genotype x environment interactions for fatty acid profiles in Bos indicus and Bos taurus finished on pasture or grain. J. Anim. Sci. 2010, 89, 221–232.
Bonny, S.; Polkinghorne, R.; Strydom, P.; Matthews, K.; López-Campos, Ó.; Nishimura, T.; Hocquette, J.F. Quality Assurance Schemes in Major Beef Producing Countries. In New Aspects of Meat Quality; Woodhead Publishing: Cambridge, UK, 2017; pp. 223–255.
Motoyama, M.; Sasaki, K.; Watanabe, A. Wagyu and the factors contributing to its beef quality: A Japanese industry overview. Meat Sci. 2016, 120, 10–18.
Frank, D.; Ball, A.; Hughes, J.; Krishnamurthy, R.; Piyasiri, U.; Stark, J.; Watkins, P.; Warner, R. Sensory and Flavor Chemistry Characteristics of Australian Beef: Influence of Intramuscular Fat, Feed, and Breed. J. Agric. Food Chem. 2016, 64, 4299–4311.
Pethick, D.W.; Harper, G.S.; Hocquette, J.F.; Wang, Y. Marbling Biology—What Do We Know about Getting Fat into Muscle? In Proceedings of the Australian Beef–the Leader Conference, Armidale, Australia, 7–8 March 2006; pp. 103–110.
Bonny, S.P.F.; Hocquette, J.-F.; Pethick, D.W.; Farmer, L.J.; Legrand, I.; Wierzbicki, J.; Allen, P.; Polkinghorne, R.J.; Gardner, G. The variation in the eating quality of beef from different sexes and breed classes cannot be completely explained by carcass measurements. Animal 2016, 10, 987–995.
Conanec, A.; Campo, M.; Richardson, I.; Ertbjerg, P.; Failla, S.; Panea, B.; Chavent, M.; Saracco, J.; Williams, J.; Ellies-Oury, M.-P.; et al. Has breed any effect on beef sensory quality? Livest. Sci. 2021, 250, 104548.
Bonfatti, V.; Albera, A.; Carnier, P. Genetic associations between daily BW gain and live fleshiness of station-tested young bulls and carcass and meat quality traits of commercial intact males in Piemontese cattle. J. Anim. Sci. 2013, 91, 2057–2066.
Venkata Reddy, B.; Sivakumar, A.S.; Jeong, D.W.; Woo, Y.-B.; Park, S.-J.; Lee, S.-Y.; Byun, J.-Y.; Kim, C.-H.; Cho, S.-H.; Hwang, I. Beef quality traits of heifer in comparison with steer, bull and cow at various feeding environments. Anim. Sci. J. 2014, 86, 1–16.
Seideman, S.C.; Cross, H.R.; Oltjen, R.R.; Schanbacher, B.D. Utilization of the Intact Male for Red Meat Production: A Review. J. Anim. Sci. 1982, 55, 826–840.
Nogalski, Z.; Pogorzelska-Przybyłek, P.; Sobczuk-Szul, M.; Nogalska, A.; Modzelewska-Kapituła, M.; Purwin, C. Italian Journal of Animal Science Carcass Characteristics and Meat Quality of Bulls and Steers Slaughtered at Two Different Ages Carcass Characteristics and Meat Quality of Bulls and Steers Slaughtered at Two Different Ages. Ital. J. Anim. Sci. 2018, 17, 279–288.
Monin, G. Facteurs biologiques des qualités de la viande bovine. INRAE Prod. Anim. 1991, 4, 151–160.
Neethling, N.E.; Suman, S.P.; Sigge, G.O.; Hoffman, L.C.; Hunt, M.C. Exogenous and Endogenous Factors Influencing Color of Fresh Meat from Ungulates. Meat Muscle Biol. 2017, 1, 253–275.
Clinquart, A.; Ellies-Oury, M.; Hocquette, J.; Guillier, L.; Santé-Lhoutellier, V.; Prache, S. Review: On-farm and processing factors affecting bovine carcass and meat quality. Animal 2022, 16, 100426.
Hopkins, D.L.; Stanley, D.F.; Martin, L.C.; Toohey, E.S.; Gilmour, A.R. Genotype and age effects on sheep meat production. 3. Meat quality. Aust. J. Exp. Agric. 2007, 47, 1155–1164.
Kopuzlu, S.; Esenbuga, N.; Onenc, A.; Macit, M.; Yanar, M.; Yuksel, S.; Ozluturk, A.; Unlu, N. Effects of slaughter age and muscle type on meat quality characteristics of Eastern Anatolian Red bulls. Arch. Anim. Breed. 2018, 61, 497–504.
Watkins, P.J.; Frank, D.; Singh, T.K.; Young, O.A.; Warner, R.D. Sheepmeat Flavor and the Effect of Different Feeding Systems: A Review Project Investigating Interactions between Pater Natalis and Rangifer Tarandus View Project Vitamins in Legumes View Project. Artic. J. Agric. Food Chem. 2013, 61, 3561–3579.
Hopkins, D.L. The Eating Quality of Meat: II-Tenderness; Elsevier Ltd.: Amsterdam, The Netherlands, 2017; ISBN 9780081006979.
Flores, M. The Eating Quality of Meat: III—Flavor. In Lawrie’s Meat Science, 8th ed.; Woodhead Publishing: Cambridge, UK, 2017; pp. 383–417. ISBN 9780081006979.
Janssen, J.; Cammack, K.; Legako, J.; Cox, R.; Grubbs, J.; Underwood, K.; Hansen, J.; Kruse, C.; Blair, A. Influence of Grain- and Grass-Finishing Systems on Carcass Characteristics, Meat Quality, Nutritional Composition, and Consumer Sensory Attributes of Bison. Foods 2021, 10, 1060.
Stampa, E.; Schipmann-Schwarze, C.; Hamm, U. Consumer perceptions, preferences, and behavior regarding pasture-raised livestock products: A review. Food Qual. Prefer. 2020, 82, 103872.
Marino, R.; Albenzio, M.; Caroprese, M.; Napolitano, F.; Santillo, A.; Braghieri, A. Effect of grazing and dietary protein on eating quality of Podolian beef. J. Anim. Sci. 2011, 89, 3752–3758.
Duckett, S.K.; Neel, J.P.S.; Fontenot, J.P.; Clapham, W.M. Effects of winter stocker growth rate and finishing system on: III. Tissue proximate, fatty acid, vitamin, and cholesterol content1. J. Anim. Sci. 2009, 87, 2961–2970.
Pogorzelski, G.; Pogorzelska-Nowicka, E.; Pogorzelski, P.; Półtorak, A.; Hocquette, J.-F.; Wierzbicka, A. Towards an integration of pre- and post-slaughter factors affecting the eating quality of beef. Livest. Sci. 2022, 255, 104795.
Elmore, J.; Warren, H.; Mottram, D.; Scollan, N.; Enser, M.; Richardson, R.; Wood, J. A comparison of the aroma volatiles and fatty acid compositions of grilled beef muscle from Aberdeen Angus and Holstein-Friesian steers fed diets based on silage or concentrates. Meat Sci. 2004, 68, 27–33.
Latimori, N.; Kloster, A.; García, P.; Carduza, F.; Grigioni, G.; Pensel, N. Diet and genotype effects on the quality index of beef produced in the Argentine Pampeana region. Meat Sci. 2008, 79, 463–469.
Realini, C.; Duckett, S.; Brito, G.; Dalla-Rizza, M.; De Mattos, D. Effect of pasture vs. concentrate feeding with or without antioxidants on carcass characteristics, fatty acid composition, and quality of Uruguayan beef. Meat Sci. 2004, 66, 567–577.
Hopkins, D.L.; Geesink, G.H. Protein Degradation Post Mortem and Tenderization. In Applied Muscle Biology and Meat Science; CRC Press: Boca Raton, FL, USA, 2009; pp. 149–173.
Dikeman, M.E. The Relationship of Animal Leanness to Meat Tenderness. In Proceedings of the Reciprocal Meat Conference, Provo, UT, USA, 11 June 1996; pp. 87–101.
Hocquette, J.F.; Meurice, P.; Brun, J.P.; Jurie, C.; Denoyelle, C.; Bauchart, D.; Picard, B. BIF-Beef: A Data Warehouse for Mus-cle Biology to Predict Beef Quality. Application to the Relationship between Intramuscular Fat Level and Flavour. Anim. Prod. Sci. 2011, 51, 975–981.
Toldrá, F. The Storage and Preservation of Meat: III-Meat Processing. In Lawrie’s Meat Science, 8th ed.; Woodhead Publishing: Cambridge, UK, 2017; ISBN 9780081006979.
Bonny, S.P.F.; Gardner, G.; Pethick, D.W.; Allen, P.; Legrand, I.; Wierzbicki, J.; Farmer, L.J.; Polkinghorne, R.J.; Hocquette, J.-F. Untrained consumer assessment of the eating quality of European beef: 2. Demographic factors have only minor effects on consumer scores and willingness to pay. Animal 2017, 11, 1399–1411.
Hocquette, J.-F.; Meurice, P.; Brun, J.P.; Jurie, C.; Denoyelle, C.; Bauchart, D.; Renand, G.; Nute, G.R.; Picard, B. The challenge and limitations of combining data: A case study examining the relationship between intramuscular fat content and flavour intensity based on the BIF-BEEF database. Anim. Prod. Sci. 2011, 51, 975–981.
Thompson, J.M. The effects of marbling on flavour and juiciness scores of cooked beef, after adjusting to a constant tenderness. Aust. J. Exp. Agric. 2004, 44, 645–652.
Gruber, S.L.; Tatum, J.D.; Engle, T.E.; Chapman, P.L.; Belk, K.E.; Smith, G.C. Relationships of behavioral and physiological symptoms of preslaughter stress to beef longissimus muscle tenderness. J. Anim. Sci. 2010, 88, 1148–1159.
Hemsworth, P.H.; Rice, M.; Karlen, M.G.; Calleja, L.; Barnett, J.L.; Nash, J.; Coleman, G.J. Human–animal interactions at abattoirs: Relationships between handling and animal stress in sheep and cattle. Appl. Anim. Behav. Sci. 2011, 135, 24–33.
Ferguson, D.; Warner, R. Have we underestimated the impact of pre-slaughter stress on meat quality in ruminants? Meat Sci. 2008, 80, 12–19.
Loudon, K.M.; Tarr, G.; Lean, I.J.; Polkinghorne, R.; McGilchrist, P.; Dunshea, F.R.; Gardner, G.E.; Pethick, D.W. The Impact of Pre-Slaughter Stress on Beef Eating Quality. Animals 2019, 9, 612.
Del Campo, M.; Brito, G.; de Lima, J.S.; Hernández, P.; Montossi, F. Finishing diet, temperament and lairage time effects on carcass and meat quality traits in steers. Meat Sci. 2010, 86, 908–914.
Watson, R.; Gee, A.; Polkinghorne, R.; Porter, M. Consumer assessment of eating quality-development of protocols for Meat Standards Australia (MSA) testing. Aust. J. Exp. Agric. 2008, 48, 1360–1367.
Kim, Y.H.B.; Ma, D.; Setyabrata, D.; Farouk, M.M.; Lonergan, S.M.; Huff-Lonergan, E.; Hunt, M.C. Understanding postmortem biochemical processes and post-harvest aging factors to develop novel smart-aging strategies. Meat Sci. 2018, 144, 74–90.
Monsón, F.; Sañudo, C.; Sierra, I. Influence of Breed and Ageing Time on the Sensory Meat Quality and Consumer Accepta-bility in Intensively Reared Beef. Meat Sci. 2005, 71, 471–479.
Dransfield, E. Optimisation of tenderisation, ageing and tenderness. Meat Sci. 1994, 36, 105–121.
Nair, M.N.; Canto, A.C.; Rentfrow, G.; Suman, S.P. Muscle-specific effect of aging on beef tenderness. LWT—Food Sci. Technol. 2019, 100, 250–252.
Dikeman, M.; Devine, C.E. Sensory and Meat Quality, Optimization of. In Encyclopedia of Meat Sciences; Elsevier Ltd.: Amsterdam, The Netherlands, 2014; pp. 267–271. ISBN 9780123847317.
Hwang, I.; Devine, C.; Hopkins, D. The biochemical and physical effects of electrical stimulation on beef and sheep meat tenderness. Meat Sci. 2003, 65, 677–691.
Rhee, M.; Kim, B. Effect of low voltage electrical stimulation and temperature conditioning on postmortem changes in glycolysis and calpains activities of Korean native cattle (Hanwoo). Meat Sci. 2001, 58, 231–237.
Arroyo, C.; Lascorz, D.; O’Dowd, L.; Noci, F.; Arimi, J.; Lyng, J.G. Effect of Pulsed Electric Field treatments at various stages during conditioning on quality attributes of beef longissimus thoracis et lumborum muscle. Meat Sci. 2015, 99, 52–59.
Juárez, M.; Aldai, N.; López-Campos, Ó.; Dugan, M.E.R.; Uttaro, B.; Aalhus, J.L. Beef Texture and Juiciness; CRC Press: Boca Raton, FL, USA, 2012.
Chrystall, B.B.; Hagyard, C. Electrical stimulation and lamb tenderness. N. Z. J. Agric. Res. 1976, 19, 7–11.
Taylor, D.G.; Marshall, A.R. Low Voltage Electrical Stimulation of Beef Carcasses. J. Food Sci. 1980, 45, 144–145.
Gursansky, B.; O’Halloran, J.; Egan, A.; Devine, C. Tenderness enhancement of beef from Bos indicus and Bos taurus cattle following electrical stimulation. Meat Sci. 2010, 86, 635–641.
Zhang, Y.; Ji, X.; Mao, Y.; Luo, X.; Zhu, L.; Hopkins, D.L. Effect of new generation medium voltage electrical stimulation on the meat quality of beef slaughtered in a Chinese abattoir. Meat Sci. 2019, 149, 47–54.
Ji, X.; Luo, X.; Zhu, L.; Mao, Y.; Lu, X.; Chen, X.; Hopkins, D.L.; Zhang, Y. Effect of medium voltage electrical stimulation and prior ageing on beef shear force during superchilled storage. Meat Sci. 2021, 172, 108320.
Hopkins, D.L. Tenderizing Mechanisms: Mechanical. In Encyclopedia of Meat Sciences; Devine, C., Dikeman, M., Eds.; Elsevier: Oxford, UK, 2014; Volume 3, pp. 443–451. ISBN 9780123847317.
Nian, Y.; Allen, P.; Harrison, S.M.; Kerry, J.P. Effect of castration and carcass suspension method on the quality and fatty acid profile of beef from male dairy cattle. J. Sci. Food Agric. 2018, 98, 4339–4350.
Moran, L.; Barron, L.J.R.; Wilson, S.S.; O’Sullivan, M.G.; Kerry, J.P.; Prendiville, R.; Moloney, A.P. Effect of pelvic suspension and post-mortem ageing on the quality of three muscles from Holstein Friesian bulls and steers. J. Sci. Food Agric. 2021, 101, 1892–1900.
Purchas, R.W. Tenderness Measurement. In Encyclopedia of Meat Sciences; Elsevier: Oxford, UK, 2014; pp. 452–459. ISBN 9780123847317.
Aaslyng, M.; Meinert, L.; Bejerholm, C. Sensory Assessment of Meat. In Encyclopedia of Meat Sciences; Elsevier Ltd.: Oxford, UK, 2014; pp. 272–279. ISBN 9780123847317.
Derington, A.; Brooks, J.; Garmyn, A.; Thompson, L.; Wester, D.; Miller, M. Relationships of slice shear force and Warner-Bratzler shear force of beef strip loin steaks as related to the tenderness gradient of the strip loin. Meat Sci. 2011, 88, 203–208.
Battaglia, C.; Vilella, G.F.; Bernardo, A.P.S.; Gomes, C.L.; Biase, A.G.; Albertini, T.Z.; Pflanzer, S.B. Comparison of methods for measuring shear force and sarcomere length and their relationship with sensorial tenderness of longissimus muscle in beef. J. Texture Stud. 2020, 51, 252–262.
Platter, W.J.; Tatum, J.D.; Belk, K.E.; Chapman, P.L.; Scanga, J.A.; Smith, G.C. Relationships of consumer sensory ratings, marbling score, and shear force value to consumer acceptance of beef strip loin steaks. J. Anim. Sci. 2003, 81, 2741–2750.
Rodas-González, A.; Huerta-Leidenz, N.; Jerez-Timaure, N.J.; Miller, M.F. Establishing tenderness thresholds of Venezuelan beef steaks using consumer and trained sensory panels. Meat Sci. 2009, 83, 218–223.
Liang, R.; Zhu, H.; Mao, Y.; Zhang, Y.; Zhu, L.; Cornforth, D.; Wang, R.; Meng, X.; Luo, X. Tenderness and sensory attributes of the longissimus lumborum muscles with different quality grades from Chinese fattened yellow crossbred steers. Meat Sci. 2016, 112, 52–57.
Sasaki, K.; Motoyama, M.; Narita, T.; Hagi, T.; Ojima, K.; Oe, M.; Nakajima, I.; Kitsunai, K.; Saito, Y.; Hatori, H.; et al. Characterization and classification of Japanese consumer perceptions for beef tenderness using descriptive texture characteristics assessed by a trained sensory panel. Meat Sci. 2013, 96, 994–1002.
Shackelford, S.D.; Wheeler, T.L.; Koohmaraie, M. Relationship between shear force and trained sensory panel tenderness ratings of 10 major muscles from Bos indicus and Bos taurus cattle1. J. Anim. Sci. 1995, 73, 3333–3340.
McKillip, K.V.; Wilfong, A.K.; Gonzalez, J.M.; Houser, T.A.; Unruh, J.A.; Boyle, E.A.E.; O’Quinn, T.G. Repeatability and Accuracy of the Pressed Juice Percentage Method at Sorting Steaks into Juiciness Categories. Meat Muscle Biol. 2017, 1, 242–252.
Liu, J.; Ellies-Oury, M.-P.; Chriki, S.; Legrand, I.; Pogorzelski, G.; Wierzbicki, J.; Farmer, L.; Troy, D.; Polkinghorne, R.; Hocquette, J.-F. Contributions of tenderness, juiciness and flavor liking to overall liking of beef in Europe. Meat Sci. 2020, 168, 108190.
Pearce, K.L.; Rosenvold, K.; Andersen, H.J.; Hopkins, D. Water distribution and mobility in meat during the conversion of muscle to meat and ageing and the impacts on fresh meat quality attributes—A review. Meat Sci. 2011, 89, 111–124.
Bouton, P.E.; Ford, A.L.; Harris, P.V.; Ratcliff, D. Objective assessment of meat juiciness. J. Food Sci. 1975, 40, 884–885.
Young, O.A.; Hopkins, D.; Pethick, D.W. Critical control points for meat quality in the Australian sheep meat supply chain. Aust. J. Exp. Agric. 2005, 45, 593–601.
Lucherk, L.W.; O’Quinn, T.G.; Legako, J.F.; Rathmann, R.J.; Brooks, J.C.; Miller, M.F. Assessment of objective measures of beef steak juiciness and their relationships to sensory panel juiciness ratings. J. Anim. Sci. 2017, 95, 2421–2437.
Holman, B.W.B.; Collins, D.; Hopkins, D.L. The Relationship between Aged Beef Intramuscular Fat Content and Australian Consumer Rankings for Juiciness. In Proceedings of the Physical Education and Sport for Children and Youth with Special Needs Researches—Best Practices—Situation, the 33rd Conference of the Australian Association of Animal Sciences, Fremantle, Australia, 31 January–2 February 2021; CSIRO: Cairns, Australia, 2021.
Resconi, V.C.; Campo, M.D.M.; Montossi, F.; Ferreira, V.; Sañudo, C.; Escudero, A. Gas Chromatographic-Olfactometric Aroma Profile and Quantitative Analysis of Volatile Carbonyls of Grilled Beef from Different Finishing Feed Systems. J. Food Sci. 2012, 77, S240–S246.
Listrat, A.; Gagaoua, M.; Andueza, D.; Gruffat, D.; Normand, J.; Mairesse, G.; Picard, B.; Hocquette, J.-F. What are the drivers of beef sensory quality using metadata of intramuscular connective tissue, fatty acids and muscle fiber characteristics? Livest. Sci. 2020, 240, 104209.
Tan, J.; Xu, J. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artif. Intell. Agric. 2020, 4, 104–115.
Hocquette, J.-F.; Botreau, R.; Picard, B.; Jacquet, A.; Pethick, D.W.; Scollan, N.D. Opportunities for predicting and manipulating beef quality. Meat Sci. 2012, 92, 197–209.
Farmer, L.J.; Farrell, D.T. Review: Beef-eating quality: A European journey. Animal 2018, 12, 2424–2433.
Liu, Y.; Lyon, B.G.; Windham, W.R.; Realini, C.E.; Pringle, T.D.; Duckett, S. Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study. Meat Sci. 2003, 65, 1107–1115.
Prieto, N.; Roehe, R.; Lavín, P.; Batten, G.; Andrés, S. Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Sci. 2009, 83, 175–186.
Ripoll, G.; Albertí, P.; Panea, B.; Olleta, J.; Sañudo, C. Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. Meat Sci. 2008, 80, 697–702.
Taheri-Garavand, A.; Fatahi, S.; Omid, M.; Makino, Y. Meat quality evaluation based on computer vision technique: A review. Meat Sci. 2019, 156, 183–195.
Picard, B.; Gagaoua, M.; Hollung, K. Gene and Protein Expression as a Tool to Explain/Predict Meat (and Fish) Quality. In New Aspects of Meat Quality; Woodhead Publishing: Cambridge, UK, 2017; pp. 321–354. ISBN 9780081005934.
Leal-Gutiérrez, J.D.; Mateescu, R.G. Genetic basis of improving the palatability of beef cattle: Current insights. Food Biotechnol. 2019, 33, 193–216.
Leal-Gutiérrez, J.D.; Rezende, F.M.; Reecy, J.M.; Kramer, L.M.; Peñagaricano, F.; Mateescu, R.G. Whole Genome Sequence Data Provides Novel Insights Into the Genetic Architecture of Meat Quality Traits in Beef. Front. Genet. 2020, 11, 538640.
Gagaoua, M.; Duffy, G.; Alvarez, C.; Burgess, C.M.; Hamill, R.; Crofton, E.; Botinestean, C.; Ferragina, A.; Cafferky, J.; Mul-len, A.M.; et al. Current Research and Emerging Tools to Improve Fresh Red Meat Quality; Compuscript: Shannon, Ireland, 2022; pp. 1–23.
Berry, D.; Conroy, S.; Hegarty, P.; Evans, R.; Pabiou, T.; Judge, M. Inter-animal genetic variability exist in organoleptic properties of prime beef meat. Meat Sci. 2021, 173, 108401.
Gagaoua, M.; Warner, R.D.; Purslow, P.; Ramanathan, R.; Mullen, A.M.; López-Pedrouso, M.; Franco, D.; Lorenzo, J.M.; Tomasevic, I.; Picard, B.; et al. Dark-cutting beef: A brief review and an integromics meta-analysis at the proteome level to decipher the underlying pathways. Meat Sci. 2021, 181, 108611.
Yang, B.; Liu, X. Application of proteomics to understand the molecular mechanisms determining meat quality of beef muscles during postmortem aging. PLoS ONE 2021, 16, e0246955.
Gagaoua, M.; Terlouw, C.; Richardson, I.; Hocquette, J.-F.; Picard, B. The associations between proteomic biomarkers and beef tenderness depend on the end-point cooking temperature, the country origin of the panelists and breed. Meat Sci. 2019, 157, 107871.
Muroya, S.; Ueda, S.; Komatsu, T.; Miyakawa, T.; Ertbjerg, P. MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals. Metabolites 2020, 10, 188.
Jeong, J.Y.; Kim, M.; Ji, S.-Y.; Baek, Y.-C.; Lee, S.; Oh, Y.K.; Reddy, K.E.; Seo, H.-W.; Cho, S.; Lee, H.-J. Metabolomics Analysis of the Beef Samples with Different Meat Qualities and Tastes. Korean J. Food Sci. Anim. Resour. 2020, 40, 924–937.
Ma, D.; Kim, Y.H.B.; Cooper, B.; Oh, J.-H.; Chun, H.; Choe, J.-H.; Schoonmaker, J.P.; Ajuwon, K.; Min, B. Metabolomics Profiling to Determine the Effect of Postmortem Aging on Color and Lipid Oxidative Stabilities of Different Bovine Muscles. J. Agric. Food Chem. 2017, 65, 6708–6716.
Antonelo, D.S.; Cônsolo, N.R.; Gómez, J.F.; Beline, M.; Goulart, R.S.; Corte, R.; Colnago, L.A.; Schilling, M.W.; Gerrard, D.E.; Silva, S.L. Metabolite profile and consumer sensory acceptability of meat from lean Nellore and Angus × Nellore crossbreed cattle fed soybean oil. Food Res. Int. 2020, 132, 109056.
Bonny, S.P.; O’Reilly, R.A.; Pethick, D.W.; Gardner, G.E.; Hocquette, J.-F.; Pannier, L. Update of Meat Standards Australia and the cuts based grading scheme for beef and sheepmeat. J. Integr. Agric. 2018, 17, 1641–1654.
Polkinghorne, R.; Thompson, J. Meat standards and grading: A world view. Meat Sci. 2010, 86, 227–235.
Fisher, A. Beef Carcass Classification in the EU: An Historical Perspective. In Book of Abstracts of the 58th Annual Meeting of the European Federation of Animal; Wageningen Academic Publishers: Wageningen, The Netherlands, 2007; p. 19.
Tarrés, J.; Fina, M.; Varona, L.; Piedrafita, J. Carcass conformation and fat cover scores in beef cattle: A comparison of threshold linear models vs grouped data models. Genet. Sel. Evol. 2011, 43, 16.
Pabiou, T.; Fikse, W.; Cromie, A.; Keane, M.; Näsholm, A.; Berry, D. Use of digital images to predict carcass cut yields in cattle. Livest. Sci. 2011, 137, 130–140.
Bonny, S.P.F.; Pethick, D.W.; Legrand, I.; Wierzbicki, J.; Allen, P.; Farmer, L.J.; Polkinghorne, R.J.; Hocquette, J.-F.; Gardner, G. European conformation and fat scores have no relationship with eating quality. Animal 2016, 10, 996–1006.
Strong, J. Differences in carcass grading schemes used in the USA, Japan and Australia. Aust. J. Exp. Agric. 2004, 44, 675–680.
Liu, J.; Pogorzelski, G.; Neveu, A.; Legrand, I.; Pethick, D.; Ellies-Oury, M.-P.; Hocquette, J.-F. Are Marbling and the Prediction of Beef Eating Quality Affected by Different Grading Sites? Front. Veter.-Sci. 2021, 8, 611153.
Hwanga, I.H.; Polkinghorne, R.; Lee, J.M.; Thompson, J.M. Demographic and design effects on beef sensory scores given by Korean and Australian consumers. Aust. J. Exp. Agric. 2008, 48, 1387–1395.
Pethick, D.; Hocquette, J.-F.; Scollan, N.; Dunshea, F. Review: Improving the nutritional, sensory and market value of meat products from sheep and cattle. Animal 2021, 15, 100356.
McGilchrist, P.; Polkinghorne, R.; Ball, A.; Thompson, J. The Meat Standards Australia Index indicates beef carcass quality. Animal 2019, 13, 1750–1757.
Hocquette, J.-F.; Ellies-Oury, M.-P.; Legrand, I.; Pethick, D.; Gardner, G.; Wierzbicki, J.; Polkinghorne, R.J. Research in Beef Tenderness and Palatability in the Era of Big Data. Meat Muscle Biol. 2020, 4, 1–13.
Ruiz-Capillas, C.; Herrero, A.; Pintado, T.; Delgado-Pando, G. Sensory Analysis and Consumer Research in New Meat Products Development. Foods 2021, 10, 429.

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Subjects: Food Science & Technology

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Jingjing Liu

Marie-Pierre Ellies-Oury

Todor Stoyanchev

Jean-François Hocquette

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Update Date: 29 Jun 2022

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