Update on Muscle-Fiber Type and Meat-Sensory Quality

Muscle Fibers Within the Muscle Tissue

Muscle fibers are composed of myofibrils, which constitute the contractile elements of the muscle. Myofibrils are organized into repeating structural units known as sarcomeres. Sarcomeres contain 2 major proteins, actin and myosin, whose sliding interaction within the myofibrils enables muscle contraction and relaxation. Muscles require energy to sustain both contraction and maintenance functions. This energy is supplied in the form of adenosine triphosphate (ATP), which is generated initially from the breakdown of phosphocreatine and subsequently from glycogen stored within muscle cells (Geay et al., 2001).

Each muscle fiber, group of fibers, and fiber bundles are surrounded by different layers of connective tissue (endomysium, perimysium, epimysium). Collagen molecules within connective tissue are linked by chemical bonds known as cross-links. Proteoglycans bind collagen fibrils together, stabilize the extracellular matrix, and contribute to its functional properties, including hydration and resistance to compressive forces. It is well-established that a higher collagen content is generally associated with increased meat toughness (Roy and Bruce, 2024). As cross-linking increases, collagen becomes less soluble, and meat toughness increases (Purslow, 2005). Collagen content and characteristics largely determine the culinary use of individual muscles (muscles intended for rapid cooking generally contain less, whereas muscles destined for slow cooking are richer in collagen). Collagen content varies widely depending on multiple factors, including breed and animal type and not only between muscles but also within a given muscle. During postmortem ageing, proteoglycans are progressively degraded, the process of which exposes collagen fibrils to collagenolytic enzymes and may contribute to a reduction in meat toughness (Nishimura, 2010).

Intramuscular adipocytes are responsible for lipid storage and mobilization. Intramuscular lipids exert a moderate but significant positive effect on meat tenderness by increasing perceived juiciness and reducing the sensation of residual chewiness. In addition, at very high levels of intramuscular fat deposition, the collagen network may be physically disrupted, as observed in highly marbled meats such as Kobe beef (Nishimura, 2010). The visible component of intramuscular fat, commonly referred to as “marbling” in the meat industry, is widely used as an indicator of meat quality in carcass and meat-grading systems in countries such as Japan, the United States, and Australia.

While the roles of collagen and intramuscular lipids in determining meat quality are relatively well-established, the contribution of muscle-fiber type remains less clearly defined. The effects of muscle-fiber composition on meat quality may be either direct or indirect, highlighting the need for a comprehensive understanding of muscle-fiber typology and its interactions with other key muscle components, particularly connective tissue and intramuscular lipids.

Characteristics of the Different Muscle-Fiber Types

Muscle-fiber type composition is a key characteristic of skeletal muscle, influencing athletic performance in humans and meat quality in meat-producing farm animals (Sawano and Mizunoya, 2022). Muscle fibers can be classified into distinct types based on several criteria, primarily reflecting differences in 2 major functional compartments (Table 1): the myosin motor apparatus and energy metabolism (Schiaffino et al., 2025). Among these criteria, contractile and metabolic properties are the most widely used for practical classification (Picard and Gagaoua, 2020).

MyHC are a major determinant of contraction speed and allow muscle fibers to be broadly categorized into slow-twitch (Type I) and fast-twitch (Type II) fibers (Table 1). Historically, contraction speed has been most reliably assessed through MyHC isoforms expressed within each muscle fiber. This led to the classical distinction between slow-oxidative fibers (Type I), intermediate (Type IIa) and fastglycolytic fibers (Types IIx and IIb). Muscle fibers exhibit a high degree of plasticity and can undergo transitions along the continuum Type I → Type IIa → Type IIx → Type IIb or in the opposite direction, depending on physiologic and environmental factors (Schiaffino and Reggiani, 2011). While muscles of some species, such as rodents, express the full spectrum of fiber types, this is not always the case in large mammals, whose muscles typically contain mainly Type I, Type IIa, and Type IIx fibers. More recently, the characterization of muscle-fiber types has been substantially advanced by the use of highly specific antibodies and omics-based approaches (Schiaffino et al., 2025). To date, up to 11 sarcomeric genes for MyHC (MYH, the genomic nomenclature) have been identified (Table 2). In human skeletal muscle, the predominant MYH isoforms are MYH7 in slow (Type I) fibers, MYH2 in Type IIa fibers, and MYH1 in Type IIx fibers. In rodents, an additional isoform, MYH4, is expressed in Type IIb fibers. Developmental and regenerative stages are characterized by the expression of MYH3 (embryonic) and MYH8 (neonatal or perinatal). Importantly, fiber contractile performance is strictly determined by the specific myosin isoforms expressed (Schiaffino et al., 2025).

Muscle-fiber types also differ substantially in their energy metabolism. In practice, several enzyme activities are commonly used as metabolic markers, including lactate dehydrogenase (LDH) and phosphofructokinase (PFK) for glycolytic metabolism, and citrate synthase (CS), isocitrate dehydrogenase (ICDH), succinate dehydrogenase (SDH), or cytochrome c oxidase (COX) for mitochondrial oxidative metabolism. These indicators can be used to estimate the relative proportions of oxidative and glycolytic fibers within a muscle (Hocquette et al., 1998). However, among these enzymes, LDH, COX, and ICDH appear to be the most discriminant for fiber-type identification, based on the strength of their correlations with MyHC isoform expression (Gagaoua et al., 2016).

Extensive research has also focused on identifying genes, transcription factors, hormones, and signaling pathways involved in muscle-fiber type specification during both fetal and postnatal development. (Mo et al., 2023; Schiaffino et al., 2025; Wang et al., 2024). These genes directly or indirectly regulate muscle-fiber types through various signaling pathways, influencing genes related to the MyHC family (MyHC-IIB, MyHC-I, MyHC-IIA, and MyHC-IIX), mitochondrial biogenesis, and oxidative metabolism (MSTN, PRKAG1, PRKAG3, and PRKAR2B) (Wang et al., 2024). Among the major molecular pathways involved in the control of proliferation, differentiation and maturation of myotubes are the well-known myogenic regulatory factors family, the Pax family, the Homeobox Transcription Factor Sine oculis homeobox (Six) family, the Mstn gene, but also the signaling pathways such as FoxO signaling pathway, the peroxisome proliferator-activated receptor γ coactivator 1-α pathway, Wingless/Integrated (Wnt), and sonic hedgehog (Shh) signaling pathways. For instance, Mstn (a well-known negative regulator of muscle growth) and Six1 (a transcription factor controlling muscle development) are major regulators of the proportion of slow-twitch to fast-twitch fibers, Mst favors slow-twitch fibers, while Six1 acting in interaction with other factors favors the expression of the fast-twitch phenotype. The other underlying mechanisms have been extensively described by Mo et al. (2023). An extensive list of microRNA, long noncoding RNA, and circular RNA regulating the transformation of muscle fibers in both directions (fast to slow or slow to fast fibers) has been published from previous research in pigs, poultry, and bovines. MicroRNA plays a major role by targeting the downstream genes involved in slow or fast phenotypes of muscle fibers, while long noncoding RNA and circular RNA regulate muscle-fiber types through endogenous RNA network (Wang et al., 2024). All these molecules can be considered biomarkers of meat quality, provided they make a significant contribution and appropriate methods are used to determine them routinely.

Muscle-Fiber Type Classification

Early classifications of muscle-fiber types were based on muscle color, with fibers described as red, intermediate, or white according to their mitochondrial content. This was followed by histochemical approaches, relying on the activity of metabolic enzymes (Table 3). Subsequently, muscle-fiber typing was largely based on myosin adenosine triphosphatase (ATPase) histochemistry, which progressively became a reference method. This technique relies on the selective inactivation of myosin ATPase activity through preincubation of muscle sections at different pH values. The inorganic phosphate released during ATP hydrolysis is then visualized by a dark color. After alkaline preincubation (pH 10.3), myosin ATPase activity in Type I fibers is abolished, whereas Type IIa and Type IIb fibers remain stained. Conversely, after acidic preincubation (pH 4.3), only Type I fibers retain ATPase activity. Type IIa and Type IIb fibers can be further distinguished by gradually increasing the pH from 4.3 to 4.6, at which point Type IIb fibers become stained. Although robust, this method is time consuming, as it requires the examination of multiple serial muscle sections. To overcome these limitations, additional histochemical methods were developed, including staining for nicotinamide adenine dinucleotide–tetrazolium reductase (NADH-TR) and SDH. Both the NADH-TR and SDH staining methods provide similar results because both primarily visualize mitochondrial enzymatic activity. Indeed, the staining principle relies on the transfer of electrons within mitochondria. However, classifications based on metabolic properties do not always coincide with those based on myosin ATPase activity (Sawano and Mizunoya, 2022). Furthermore, these staining methods have certain limitations, primarily due to the overlapping characteristics of different fiber types, their sensitivity to tissue preparation conditions, and the plasticity of muscle fibers. Combining them with other techniques can allow for a more comprehensive and precise classification of muscle-fiber types.

Subsequently, immunohistochemical approaches targeting specific MyHC isoforms were introduced. These methods rely on the use of antibodies directed against individual MyHC isoforms and allow precise identification of fiber types. A major limitation, however, is that antibody specificity is not always conserved across species (Sawano and Mizunoya, 2022). Nevertheless, immunostaining enables the identification of both pure fibers expressing a single MyHC isoform, and hybrid fibers expressing multiple isoforms. For example, in beef muscle, IIC fibers coexpressing MyHC-I and IIa, as well as IIAX fibers expressing both IIa and IIx isoforms, have been described. Identifying such hybrid fibers is particularly valuable for studying muscle-fiber plasticity. Indeed, hybrid fibers (fibers that express more than one MyHC isoform or a mix of oxidative and glycolytic traits) are a direct result of this plasticity (i.e., the conversion of muscle fibers from one type to another). By studying hybrid fibers, researchers can better understand how different factors (i.e., training, diseases, injuries, regeneration, aging, etc.) lead to changes in fiber composition. All techniques described in this section can identify overlapping characteristics of slow- and fast-twitch fibers, confirming the existence of hybrid fibers (Picard and Gagaoua, 2020).

In addition to staining-based methods, muscle-fiber type composition can be assessed using whole-muscle homogenates. In this case, metabolic-enzyme activities or MyHC isoform content are measured in whole tissue samples. MyHC isoforms can be separated according to their molecular weight using electrophoretic techniques such as sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) (Table 3). After separation, the number and position of electrophoretic bands give an indication of the types of myosin isoforms. However, this approach is time consuming and may suffer from limited reproducibility (Picard and Gagaoua, 2020), which explains why assessments based on metabolic-enzyme activities are often preferred (Hocquette et al., 1998). Immunologic techniques, including the enzyme-linked immunosorbent assay (ELISA), dot blot, and reverse-phase protein array (RPPA), have also been proposed to evaluate muscle contractile properties (Picard and Gagaoua, 2020). These methods use antibodies to quantify the contents of specific proteins in a sample (i.e., MyHC isoforms in muscles). The output is a quantitative measurement of protein concentration with some specificity and sensitivity. Compared to staining methods and SDS-PAGE, these methods are more quantitative, more sensitive, simpler, and may better represent overall muscle composition, provided that sufficiently large and representative samples are used. However, they are limited to known targets (only proteins for which antibodies are available can be quantified) and may not resolve all isoform variants or hybrid fiber types if they are not specifically targeted by the antibody. They also require standardization to avoid false positives or false negatives and to ensure accurate quantification. RPPA is a high-throughput method generating a protein profile (i.e., showing the relative abundances of various muscle proteins, including myosin isoforms, metabolic enzymes, and other markers specific to muscle-fiber types). It is, therefore, an expensive technique that requires specialized equipment and technical expertise. Finally, as for previous methods, discrepancies between classifications based on metabolic vs. contractile properties have been reported, notably in pork (Park et al., 2024) and beef (Gagaoua et al., 2016).

More recently, transcriptomic and proteomic analyses performed on single myofibers have enabled direct comparison of gene- and protein-expression profiles between pure slow-twitch (Type I) and fast-twitch (Type II) fibers. This approach overcomes the masking effects inherent to whole-muscle analyses, which average signals across heterogeneous fiber populations. Based on the expression of hundreds or thousands of genes and proteins—including MyHC isoforms and key metabolic enzymes—muscle-fiber types can be robustly clustered (Schiaffino et al., 2025). These high-resolution approaches can also be applied to larger muscle samples to compare their overall fiber-type composition.

Despite their high informative value, most of the techniques described above are time consuming, labor intensive, costly, and/or subject to technical bias. For most of the previous techniques, semi or fully automated software options for the image analysis of muscle cross-sections, electrophoretic patterns of immunologic outputs have been developed. Indeed, current research efforts are focused on the development of automated, rapid, and resource-efficient methods for potential practical applications (Table 3). For example, Rehman et al. (2025) recently developed a high-throughput approach for muscle-fiber analysis based on automated fluorescence microscopy combined with high-content image analysis. These authors used antibodies against the various MyHC. They showed on a large scale that the bovine longissimus dorsi muscle lacks Type IIB myofibers and express Type IIA and IIX myofibers, confirming previous studies with a smaller number of samples. Type IIB muscle fibers, therefore, generally appear restricted to small mammals such as rodents. Furthermore, this large-scale approach significantly reduces the time and effort required for image analysis to simultaneously determine total fiber numbers, fiber-type proportions, and myofiber cross-sectional areas, while maintaining accuracy comparable to manual analysis, allowing its application in large-scale studies of muscle samples. Depending on the antibodies used, this approach has also the potential to detect on a large-scale hybrid myofibers (i.e., fibers that are double positive for 2 different primary antibodies). In brief, this is a rapid, high-throughput analysis of muscle-fiber type that is accurate, efficient, and scalable. We are, therefore, approaching fiber classification methods that could potentially be used routinely.

Meat-Quality Traits

Effective management of meat-sensory quality first requires a clear and shared definition of sensory-quality criteria. Sensory quality encompasses meat color, which plays a key role at the point of purchase, and eating quality, which includes juiciness, flavor liking, and tenderness.

Meat color primarily depends on the concentrations of myoglobin and hemoglobin within muscle tissue, and therefore on muscle-fiber types. It can be assessed directly using a wide range of methods, including visual evaluation by carcass graders in the chiller using standardized grids, instrumental colorimetric measurements with spectrophotometers, sensory evaluation by consumers, computer vision systems, and multispectral imaging applied to muscle samples (Mo et al., 2023).

As for color, multiple approaches are available for assessing eating quality (Table 3), either directly through sensory analysis of flavor liking, tenderness and juiciness by trained panels or untrained consumers, or indirectly through instrumental measurements, such as shear force for tenderness. These methods will not be described exhaustively here but will be referenced when relevant to quality-management practices or meat-grading systems.

Flavor is the combination of taste and aroma. Volatile compounds of low molecular weight stimulate olfactory receptors in the nasal epithelium, whereas the tongue and oral mucosa detect water-soluble compounds responsible for taste, as well as irritant stimuli. Flavor-related metabolites are released or formed during postmortem ageing, depending on factors such as ageing method (wet or dry), duration, and temperature but also microbial activity (Legako, 2025). Heat induces 2 major types of reactions responsible for aroma formation: the Maillard reaction between amino acids and sugars, and lipid degradation (Kerth and Miller, 2015).

Meat tenderness depends partly on intramuscular fat content but primarily on the properties of connective tissue (which determine the basal toughness) and on the characteristics of muscle fibers (which explain the improvement in tenderness during ageing). However, beef tenderness is often disappointing, which poses a major challenge for the beef industry and results in an unfavorable price-to-quality ratio. Current meat-grading systems are unable to solve this problem (Pogorzelski et al., 2022).

Influence of Muscle-Fiber Type Composition on Meat-Sensory Quality Traits

The proportions of muscle-fiber types vary primarily between species (e.g., ruminants, poultry, pigs) and between muscles within the same animal. Less pronounced, but still significant, differences in fiber-type composition have also been reported between breeds within a given species (Pogorzelski et al., 2022). Muscle-fiber composition further changes with animal age, sex, and hormonal status, as well as with production systems, including nutritional practices (Figure 1). In addition, for a given breed and muscle, smaller yet consistent variations can be observed along the proximal-distal axis of large muscles. Although it is often difficult to disentangle the effects of fiber type from those of fiber size, as slow-oxidative fibers are generally smaller in diameter than fast-glycolytic ones (Picard and Gagaoua, 2020), the above sources of variability contribute to the influence of muscle-fiber composition on meat-sensory quality, namely color, flavor, and juiciness and tenderness (Table 1).

Figure 1.

Overview of the direct and indirect effects of muscle-fiber types on meat-sensory quality as influenced by multiple factors. Muscle-fiber types influence sensory quality directly or through indirect criteria in any case in interaction with other biochemical characteristics (related to connective tissue and intramuscular fat) as well as in interaction with ageing and cooking. Animal factors directly influence muscle-fiber types and all biochemical characteristics. They also influence quality criteria, whether directly or indirectly. Abbreviation: pHu, ultimate pH.

Regarding color, Type I and Type IIa muscle fibers contain higher concentrations of myoglobin, resulting in a darker red appearance, whereas Type IIb fibers exhibit lower myoglobin content and produce paler meat (Table 1). Consequently, meat redness is often positively correlated with the proportion of Type I and Type IIa fibers within a muscle (Pogorzelski et al., 2022).

Regarding flavor and juiciness (Table 1), a meta-analysis conducted in beef showed that the proportion of Type I fibers is positively associated with these 2 traits (Listrat et al., 2020). Indeed, these traits are generally enhanced in more oxidative muscles, which also contain higher levels of intramuscular fat. Conversely, low intramuscular fat content is associated with reduced flavor intensity. Because oxidative muscles typically contain more intramuscular triglycerides, muscles rich in oxidative fibers tend to produce meat with more pronounced flavor (Hocquette et al., 2010). In addition, a reduction in phospholipid content in Type IIb fibers has been shown to negatively affect meat flavor (Matarneh et al., 2021).

With respect to tenderness, glycolytic fast muscles generally exhibit higher glycogen reserves and a faster postmortem pH decline than oxidative slow muscles (Maltin et al., 2003). These characteristics, in combination with postmortem proteolytic activity, may explain why muscles rich in Type IIx fibers often exhibit more rapid ageing than those dominated by Type I fibers. Consequently, a higher proportion of Type IIx fibers may improve tenderness in certain muscles by accelerating the ageing process (Table 1). Nevertheless, the relationship between muscle-fiber type and pH is more complex than initially anticipated (Picard and Gagaoua, 2020). For example, comparisons across beef muscles have revealed positive correlations between tenderness and the proportions of Type I and Type IIb fibers and negative correlations with the proportion of Type IIa fibers (Chriki et al., 2012). A more recent meta-analysis confirmed the positive association between tenderness and Type IIb fibers and the negative association with Type IIa fibers, while the relationship between tenderness and Type I fibers was less consistent and depended on muscle type and experimental conditions (Listrat et al., 2020). In particular, results differ depending on whether analyses account for muscle type or pool data across muscles (Picard and Gagaoua, 2020).

Relationships Between Markers of Fiber Types and Meat-Sensory Quality

As discussed above, relationships between muscle characteristics, particularly muscle-fiber traits, and meat quality are highly variable (Pogorzelski et al., 2022). This variability arises from numerous interacting factors, including the methods used to assess meat-quality traits, techniques for muscle-fiber typing, muscle and animal types, and interactions with postmortem processes such as ageing. For instance, a recent study indicated that biochemical predictors of eating quality in older cull cows are probably more specific because they are likely to differ at least in part from those for growing animals (Cui et al., 2026a). Because muscle-fiber type and muscle biology more broadly are regulated by the coordinated expression of many genes, substantial research efforts have focused on identifying biomarkers of muscle biology and meat quality.

Early approaches of molecular biology relied on the analysis of candidate genes and proteins involved in key physiologic pathways, selected based on prior knowledge of muscle biology. Therefore, only already known molecular mechanisms could be explored and investigated in greater depth. Later on, high-throughput genomic and proteomic approaches were developed. The principle of these high-throughput techniques is to analyze all expressed genes, or all expressed proteins, including those related to muscle-fiber types without prior assumptions or without any previous selection (Cassar-Malek et al., 2008). Transcriptomic approaches enable the quantification of all messenger RNA in any biological sample, thus determining the expression level of all genes. In proteomics and transcriptomics, the methodologies developed make it possible to separate and identify all the proteins or metabolites present in biological samples. Their costs have decreased significantly in recent decades and their technical feasibility has improved considerably, making these techniques more accessible despite the need for sometimes expensive equipment and a high level of expertise. These approaches raised expectations that novel and more robust genes and proteins associated with unknown mechanisms could be identified to improve muscle-fiber typing and muscle phenotyping or to better explain variability in meat-eating quality.

This hypothesis has been partly validated, as numerous biomarkers related to muscle-fiber characteristics and other biochemical pathways have been identified. Nevertheless, relationships between these biomarkers and sensory quality remain highly variable, particularly in beef. For example, in beef, associations depend on breed, muscle type (Picard et al., 2014), and methods used to assess sensory quality, including cooking temperature and panelist nationality (Gagaoua et al., 2019). Despite these limitations, a comprehensive meta-analysis reviewed the status of protein biomarker discovery related to beef tenderness. From 28 independent proteomics studies, 124 putative protein biomarkers were identified, among which 33 robust candidates were retained for further validation. These proteins were mainly associated with muscle contraction (n = 12), energy metabolism (n = 9), heat-shock proteins (n = 8), and oxidative stress (n = 3), highlighting the major biological pathways underlying beef tenderness (Gagaoua et al., 2021). More broadly, biomarker discovery techniques are evolving rapidly and becoming increasingly accessible. Initially dominated by transcriptomics and proteomics, these approaches are now expanding toward metabolomics (Muroya, 2023). For instance, rapid evaporative ionization mass spectrometry is gaining attention as a tool for discriminating beef samples based on their fiber-type composition and sensory-quality traits (Liu et al., 2024; Cui et al., 2026b). Integrated proteomic, transcriptomic, and metabolomic analyses as shown, for instance, in pork (Feng et al., 2025) and beef (Tan et al., 2025) are nowadays a science front. For instance, up to 616 differentially expressed genes and 272 differentially abundant proteins were identified between the fast-twitch longissimus dorsi and the slow-twitch semitendinosus muscles of Bama miniature pig, a specific Chinese breed (Feng et al., 2025). Similarly, a total of 1717 differentially expressed genes, 297 differentially abundant proteins, and 193 differentially abundant metabolites were identified between the fast-type longissimus dorsi muscle and slow-type psoas major muscle from cattle. Not surprisingly, many of them are involved in glycolysis, mitochondria activity, and fatty acid metabolism. Integrated multiomics analysis showed a high correlation (>0.62) between results of the transcriptome and of the proteome (Tan et al., 2025).

Muscle-Fiber Type and Meat-Sensory Quality: Take-Home Messages

Globally, the above relationships between muscle-fiber composition and sensory traits depend on the indicators used to characterize fiber types. For example, PFK, a marker of glycolytic metabolism, and CS, a marker of oxidative metabolism, were both negatively correlated with tenderness and positively correlated with beef flavor, whereas other metabolic enzymes, such as LDH and COX, showed no significant association with sensory scores (Gagaoua et al., 2016). As indicated above, the same variability in results have been observed depending on markers of fiber types or depending on the methods used to assess sensory quality (Picard et al., 2014; Gagaoua et al., 2019).

However, despite these limitations, muscles with a higher proportion of oxidative Type I fibers are generally characterized by a deeper red color, lower shear force, smaller fiber diameter, higher fiber density, improved tenderness, higher phospholipid content, and enhanced flavor, as demonstrated in pork (Lee et al., 2016). In contrast, muscles dominated by Type IIx fibers tend to exhibit a paler and coarser appearance (Song et al., 2021). A negative relationship between muscle-fiber diameter and tenderness has been reported, indicating that smaller fiber diameter and higher fiber density, as observed in oxidative muscles, contribute to a finer meat texture, as shown in ducks (Huo et al., 2021). Type IIx fibers exhibit the highest glycolytic capacity and contraction speed. Accordingly, the rate of postmortem pH decline is positively correlated with muscle glycolytic potential and the proportion of Type II fibers, particularly Type IIx and Type IIb fibers (Matarneh et al., 2021). Consistent with these findings, muscles rich in Type IIa fibers are generally the least tender, as reported in beef (Chriki et al., 2012; Listrat et al., 2020).

In addition, new methods recently developed for muscle-fiber typing open new methodological perspectives for the future routine analysis of muscle fibers to better predict meat quality. Indeed, high-throughput, low-cost, noninvasive methods that simultaneously provide a wealth of information are suitable for modeling meat quality from different indicators or biomarkers.

Main Preslaughter Factors Affecting Muscle-Fiber Type

Species, breed, sex, age, and rearing practices are among the major factors influencing muscle-tissue characteristics, including muscle-fiber properties (Figure 1), as well as collagen and intramuscular fat content. Only selected examples related to breed, sex, age, and diet are discussed here. This type of knowledge is a prerequisite for predicting the sensory qualities of meat.

Breeds belonging to the Bos indicus subspecies, also known as zebu cattle, such as Brahman or Nellore, generally produce less tender meat due to specific muscle-fiber characteristics (Wright et al., 2018). Their muscles may also contain higher collagen levels. In contrast, the Belgian Blue breed, which carries a mutation in the myostatin gene (double-muscling), produces more tender meat, mainly due to a high proportion of fast-glycolytic fibers and a low relative collagen content. However, this meat is often less flavorful because of reduced intramuscular fat deposition, which is associated with a predominance of glycolytic fibers and similar biochemical pathways observed in both mice and cattle (Chelh et al., 2011).

Significant differences in tenderness, juiciness, and flavor have been reported among certain breeds. Early maturing breeds such as Aberdeen Angus tend to have a higher proportion of oxidative muscle fibers and deposit more collagen and intramuscular fat than late-maturing breeds such as Limousin or Blonde d’Aquitaine (Gagaoua et al., 2016). British breeds generally exhibit more oxidative muscles and produce beef with a stronger flavor profile. In contrast, French breeds are characterized by rapid growth rates, while Italian breeds display marked muscular development associated with a higher proportion of glycolytic fibers (Albechaalany et al., 2024). Nevertheless, when animals are raised under comparable conditions and meat is aged similarly, no significant differences in overall sensory quality between breeds have been observed (Conanec et al., 2021).

Sex-related differences in meat quality are largely driven by hormonal status. Castrated males deposit more fat than intact males due to lower testosterone levels, whereas intact males exhibit faster growth rates and lower intramuscular fat accumulation. Meat from steers is generally more tender than that from bulls, mainly because of higher intramuscular fat content associated with a greater proportion of oxidative fibers. However, within a given animal type, relationships between muscle characteristics—including fiber-type composition—and sensory scores remain weak and inconsistent (Gagaoua et al., 2016).

Meat tenderness generally decreases with increasing animal age, as, depending on muscles, collagen content may increase and/or become less soluble, notably due to enhanced cross-linking, making it more resistant to heat-induced degradation. As a result, muscle fibers also become tougher and more resistant to chewing. Conversely, meat flavor often increases with age, owing to the accumulation of intramuscular lipids that serve as precursors and carriers of aroma compounds (Kombolo Ngah et al., 2024).

Regarding the effect of diet on meat palatability, conflicting results have been reported. In beef, some studies have shown improved palatability in grain-fed cattle, likely due to increased marbling, whereas others have reported no significant effect (Pogorzelski et al., 2022). However, higher activities of enzymes characteristic of oxidative muscle fibers have been observed in muscles from grazing animals, highlighting the plasticity of muscle-fiber types in response to production and feeding systems (Cassar-Malek et al., 2009).

Preslaughter Stress and Muscle-Fiber Type

Following slaughter, muscle tissue remains metabolically active, with a progressive depletion of energy reserves. Once phosphocreatine stores are exhausted, ATP production relies primarily on glycolysis, leading to glycogen degradation. Pyruvate generated during glycolysis is converted into lactate, which accumulates in the absence of blood circulation. After an initial decline, the muscle pH subsequently stabilizes at the so-called ultimate pH (pHu), typically measured 24 h to 48 h postmortem.

Preslaughter stress, particularly during transport and handling, can disrupt this process and markedly impair meat quality (Terlouw, 2015). Stress induces excessive glycogen utilization before slaughter, without the possibility of postmortem replenishment. When glycogen levels are low at death due to prolonged stress, pHu values remain abnormally high, generally above the normal range of 5.7 to 5.9, depending on country-specific practices and slaughter conditions. This results in darker-colored meat that is more difficult to market, commonly referred to as dark, firm, and dry (DFD) meat (Adzitey and Nurul, 2011). DFD meat exhibits higher water-holding capacity, leading to a firmer texture. Minimizing preslaughter stress is therefore the most effective strategy to prevent DFD occurrence. Because glycolytic fibers contain higher glycogen reserves, DFD meat is assumed to occur less frequently in animals with muscles rich in glycolytic fibers. Accordingly, DFD conditions are more prevalent in red meats such as beef and lamb than in white meats, due to the higher proportion of Type I and IIA fibers (Pogorzelski et al., 2022). Consistent with this observation, higher proportions of Type I, IIA, and IIAX fibers have been reported in DFD meat (Zhang et al., 2017).

Conversely, muscles enriched in white glycolytic fibers are more susceptible to acute stress, which may lead to the development of the so-called pale, soft, and exudative meat. In such cases, elevated glycolytic capacity results in excessive lactate production and a rapid decline in pH during the early postmortem period, while muscle temperature remains high. This phenomenon is particularly prevalent in pork, especially in pigs with a high proportion of fast-twitch Type II fibers (Types IIX and IIB). Broilers and turkeys also possess a high proportion of Type II fibers. In these species, postmortem glycolysis proceeds more rapidly in white glycolytic muscles, such as the breast, compared with red muscles from the legs (Zhang et al., 2017).

Ageing, Cooking and Muscle-Fiber Type

Meat tenderization during ageing is primarily driven by proteolysis, involving several proteolytic systems. Genomic and metabolomic studies have demonstrated marked differences in metabolite composition among muscle types and fiber types, suggesting substantial intermuscular variation in postmortem metabolism during ageing. Major postmortem metabolic pathways, including glycolysis, purine metabolism, and amino acid degradation differ in both rate and extent between fast- and slow-twitch muscles (Muroya, 2023).

Indeed, in beef and chicken, postmortem proteolysis has been shown to be muscle specific. For instance, Cheng et al. (2022a, 2022b) showed that the proteolytic activities of cathepsin B and of calpains were higher in fast-twitch muscles of chicken than in mixed muscles. In beef, proteolytic analysis revealed increased desmin and slow troponin-19 T degradation in the most oxidative muscle than in the most glycolytic one, the latter being characterized by a greater fast troponin-T degradation. These differences may be explained at least in part by variations in calpain-1 autolysis, calpastatin abundance, and caspase-3 activity between muscle types (Stafford et al., 2025). In addition, the changes in protease activity (mainly calpain, but also cathepsins B and L, and the 20S proteasome) exhibited different kinetics across bovine muscles during 14 d of cold storage (Song et al., 2022).

Before consumption, aged meat is subjected to cooking, during which both muscle fibers and connective tissues undergo shrinkage, membrane disruption, and protein denaturation. These changes affect the physical, chemical, and mechanical properties of meat, including pH, cooking losses, color, micronutrient retention, and texture, depending on the cooking method. Protein denaturation plays a central role in determining tenderness and overall palatability and is strongly temperature dependent: structural protein alterations begin around 50°C, muscle-fiber contraction becomes apparent at 60°C and intensifies at 70°C, and, at approximately 80°C, fiber breakdown and collagen denaturation are markedly increased (Alfaifi et al., 2023).

Consequently, through the combined effects of ageing and cooking (Figure 1), muscle-fiber type plays a key role in determining cooked meat quality, in interaction with ageing duration and cooking end-point temperature, as recently demonstrated in Boer goats (Ravindranathan et al., 2025).

Current Grading Systems

Sensory quality is influenced by the biological characteristics of muscle tissue, which are shaped by factors such as animal-production system, breed, sex, and age, as well as by postmortem changes occurring during ageing. Nevertheless, current grading systems largely overlook these determinants, with the notable exception of marbling in certain regions of the world. To better anticipate how indicators of muscle-fiber type could contribute to improving current carcass and meat-grading systems, it is first necessary to briefly describe current grading systems, their limitations and their potential for evolution.

The original objective of carcass classification systems was to facilitate trade by describing attributes of commercial importance (Polkinghorne and Thompson, 2010). Consequently, these systems have historically focused on meat yield rather than on consumer eating quality. Most grading schemes worldwide include relatively simple indicators such as animal sex, age, and carcass weight—factors known to influence muscle-fiber composition (Table 3).

Several decades later, some countries, including Japan, the United States, and Australia, began integrating criteria related to sensory quality—most notably marbling—into their grading systems, thereby moving closer to consumer expectations. However, indicators related to the connective tissue and muscle-fiber type are still not present.

In Europe, bovine carcasses are graded according to conformation and fatness only. In addition to grading, carcass weight influences the price paid to farmers through category-specific price per kilogram, taking breed or racial type into account. In France, beef tenderness is generally not considered in official rankings, except for self-service retail sales of grilling or roasting cuts, which are classified using a star-based system established by the interprofessional organization (Ministère de l’Économie, 2014). This system relies primarily on expert knowledge of muscle identity and anatomical location, degree of processing (e.g., trimming, peeling), and intended culinary use. However, it does not incorporate objective measurements of muscle-fiber characteristics, even though muscle type (based on its fiber-type composition) is implicitly taken into account.

In Europe, official quality labels play an important role in fostering trust between producers and consumers, particularly under conditions of uncertainty. Meat products bearing signs of quality and origin identification (SIQO) are expected to meet specific consumer requirements (Kombolo Ngah et al., 2024). Five SIQO schemes exist, including 4 at the European level: Protected Designation of Origin, Protected Geographical Indication, Label Rouge (specific to France), which certify a higher level of organoleptic quality, and Guaranteed Traditional Specialty, which refers to products whose specific qualities are linked to traditional composition or production methods (Kombolo Ngah et al., 2024).

Importantly, none of these carcass-grading systems or official quality labels explicitly incorporate indicators of muscle biology, apart from marbling. In particular, no criteria related to muscle-fiber characteristics are currently considered and they cannot be incorporated into SIQO definitions. If it were possible to develop rapid, minimally invasive, and inexpensive techniques for muscle-fiber typing as suggested in the previous sections, this type of measurement could be incorporated into meat-grading systems but not SIQO; therefore, this is what we will examine in the following section.

Muscle Biochemical Traits and Meat Quality

As discussed above, muscle biochemical traits related to intramuscular fat, connective tissue, and muscle fibers are widely used to describe and differentiate meat cuts across species. For example, a recent review in pork production highlighted that muscle-fiber characteristics—not only fiber type but also fiber size and density—contribute substantially to quality differences among pork cuts and may help design postharvest strategies for controlling meat quality (Park et al., 2024).

However, studies in beef have shown that although biochemical measurements are good predictors of eating quality when comparing different muscles, only intramuscular fat and moisture contents significantly improve the precision of commercial eating-quality prediction models once muscle type is already included (Bonny et al., 2015). This may be explained by the fact that muscle-fiber composition is largely determined by muscle function and anatomical location. Deep postural muscles tend to be more oxidative, whereas superficial muscles involved in rapid movements exhibit a higher proportion of glycolytic fibers (Picard and Gagaoua, 2020).

Evolution Toward Modern Grading Systems

The European EUROP classification of beef does not adequately reflect eating quality (Bonny et al., 2016; Liu et al., 2020). Consequently, additional criteria, such as marbling, have been proposed to better account for sensory quality (Monteils et al., 2017). In this context, the French beef sector announced plans in 2022 to eventually integrate marbling into the national classification system. Introducing biomarkers of muscle-fiber types in grading systems has also been considered by scientists, provided that rapid tools are available to assess these biomarkers, whether dedicated DNA chips or protein-level tools (e.g., ELISA), which have been mentioned previously.

The Meat Standards Australia (MSA) grading system is now internationally recognized for its ability to predict beef-eating quality using a comprehensive farm-to-plate approach. A key feature of this system is that it does not rely directly on measurements of muscle biology before and after slaughter but rather on the factors regulating these biological traits, including animal and carcass characteristics known to regulate muscle-fiber types, slaughter conditions, ageing duration, and cooking methods (Table 3). More recently, an international initiative, the International Meat 3G Foundation, was established to promote collaborative research in this area (Hocquette et al., 2020).

The MSA system predicts the sensory quality of individual beef cuts by integrating multiple pre- and postslaughter factors and has also been shown to generate substantial added value across the supply chain (Bonny et al., 2018; Neveu et al., 2019). This system is sufficiently powerful and flexible to potentially incorporate new quality indicators into the prediction model, provided that these indicators can be easily measured. Initially developed using Australian data, the MSA model has since incorporated consumer testing and production data from 13 international markets (Meat & Livestock Australia, 2025), with, in some cases, an adaptation of the indicators used in the predictive model.

A fundamental principle of the MSA system is the use of untrained (naïve) consumers rather than expert panels to assess sensory quality, thereby better reflecting real consumer expectations. Notably, relationships between muscle-fiber characteristics and sensory quality have never been investigated using sensory evaluations conducted with untrained consumers. Most previous studies relied indeed on expert sensory panels or instrumental measurements, particularly shear force.

Nevertheless, many variables included in the MSA and 3G models are known regulators of muscle-fiber type, especially preslaughter factors. These include the proportion of Bos indicus genetics (estimated via hump height), animal type, carcass weight, use of hormonal implants, milk-feeding history, and exposure to livestock markets, which may influence stress levels.

Overall, the MSA and 3G models represent robust tools for predicting beef-sensory quality. Their predictive accuracy is likely to improve further as larger and more diverse datasets become available across species, production systems, and countries. The development of standardized methodologies at an international level is also a stated objective of the United Nations Economic Commission for Europe. However, further research is required to clarify the contribution of muscle-fiber characteristics to sensory quality as assessed by consumer-based grading systems. Given that MSA and 3G already integrate many factors regulating muscle-fiber composition, these effects are likely to be biologically meaningful.