Multidimensional assessment of meat quality across anatomical regions of Kazakh horses: an integrative evaluation of meat quality traits, amino acid profiles, and fatty acid composition

This study employed HE staining, LC-MS/MS, and related analytical techniques to assess meat quality, muscle fiber characteristics, and nutritional composition in five anatomical regions—longissimus dorsi (LD), diaphragm (DI), psoas major (PM), biceps femoris (BF), and semitendinosus (ST)—of the Kazakh horse. L* values in LD and PM were significantly elevated compared to the other regions (P < 0.01), whereas DI exhibited the highest a* value (P < 0.01). pH measurements indicated significantly higher values in LD, PM, BF, and ST relative to DI (P < 0.01). PM demonstrated superior tenderness, markedly surpassing that of BF. BF displayed the greatest cross-sectional area and diameter compared to other regions (P < 0.01). PM also presented significantly elevated essential amino acid (EAA)/non-essential amino acid (NEAA) and EAA/total amino acid (TAA) ratios (P < 0.01). ST contained the highest levels of total polyunsaturated fatty acids (PUFA) and exhibited the most significant n-6/n-3 PUFA ratio (P < 0.01). The observed intermuscular variation in physicochemical and nutritional properties provides a systematic foundation for quality stratification, targeted nutritional utilization, and processing optimization of Kazakh horse meat.

1 Introduction

The Kazakh horse (Equus caballus), an ancient local breed with a long history, has a domestication history that dates back to the nomadic period of Central Asia in the pre-Christian era. It is a fine local livestock breed, long selected by the Kazakh people, and is included in the national livestock genetic resources protection list due to its exceptional cold resistance, endurance, and adaptability. The Kazakh horse has a compact and well-proportioned body, characterized by a “balanced head and neck ratio, dense and drooping mane, deep and broad chest, and strong limbs,” with some individuals exhibiting features such as star-shaped markings on the forehead and white hooves. This breed demonstrates stable reproductive performance, and its unique metabolic adaptability allows it to maintain physiological homeostasis in environments with extreme temperature fluctuations. It also exhibits strong disease resistance, fine muscle fibers, and an even distribution of fat (Wubulikasimu et al., 2025).

Meat consumption has evolved from serving as a primary nutritional source to functioning as a strategic component of health-oriented dietary planning (Pogorzelska-Nowicka et al., 2018). Visual assessment of meat color remains a common method for evaluating freshness, while tenderness and juiciness substantially influence consumer purchasing behavior (You et al., 2023). Flavor development is primarily determined by the profile of amino acids, which serve as essential precursors to flavor compounds. Free amino acids contribute to the generation of volatile flavor substances via the Maillard reaction (Yin et al., 2023). The profile and concentration of fatty acids modulate both oxidative stability and sensory attributes, while also playing a decisive role in nutritional value (Schumacher et al., 2022). Excessive consumption of saturated fatty acids (SFAs) correlates with elevated triglyceride and low-density lipoprotein levels, thereby heightening the risk of obesity, diabetes, and coronary heart disease. In contrast, mono- and polyunsaturated fatty acids (PUFA) contribute to improved cardiovascular performance and immune function by reducing serum cholesterol (De Brito et al., 2017). Comparative analyses of various muscle regions reveal substantial divergence in nutritional composition and physicochemical traits, attributable to differing metabolic pathways, which subsequently impact overall flavor quality (Xu et al., 2024).

Horse meat exhibits distinct chemical and physical characteristics compared to other meat types (Stanisławczyk et al., 2024). It contains high-quality protein (Zhumanova et al., 2021), low levels of fat (Franco et al., 2011), a moderate cholesterol concentration, and a substantial proportion of unsaturated fatty acids, particularly omega-3 and omega-6 (Lorenzo and Carballo, 2015). Additionally, it is enriched with bioavailable minerals such as iron, zinc, phosphorus, and potassium (Vial et al., 2025), with heme iron constituting a significant component known for its superior absorption relative to non-heme sources (Marino et al., 2022). B vitamins, especially B12, are also present in considerable amounts (López-Pedrouso et al., 2023). Collectively, these attributes position horse meat as a nutritionally valuable and health-compatible protein source.

Litwinczuk et al. (2008) found that the pH of the longissimus dorsi muscle in 10-year-old horses (5.72) was significantly higher than that of the semitendinosus muscle (5.69). Seong PilNam et al. (2016) further confirmed significant differences in the L* and a* values, as well as shear force, among different muscle regions of horses. Tateo et al. (2008) and Lorenzo and Pateiro (2013) indicated that the brightness of the semitendinosus muscle was higher than that of the semimembranosus, latissimus dorsi, and biceps femoris muscles, while the triceps brachii had the darkest color. Most existing studies have focused on basic physicochemical parameters, such as pH and L* values, while there remains a lack of systematic analysis regarding the nutritional composition (amino acids/fatty acids), muscle fiber distribution, and their correlation with processing characteristics such as cooking loss and shear force in Kazakh horse meat. Therefore, this study selects the longissimus dorsi (LD), diaphragm (DI), psoas major (PM), biceps femoris (BF), and semitendinosus (ST) muscles of Kazakh horses for multidimensional comparison, aiming to clarify the quality differences between these muscle regions. The objective is to construct a muscle region–nutritional function correlation model, which will provide data support for the fine segmentation and deep processing of Kazakh horses and lay a theoretical foundation for the conservation and maintenance of local breed resources and diversity.

2 Materials and methods

2.1 Experimental animals

This study selected six 3.5-year-old Kazakh stallions (weighing between 375 kg and 400 kg) as the subjects. All animals originated from Changji, Xinjiang, China, and were maintained under identical environmental conditions. They were fed at 08:00 AM and 05:30 PM daily, with free access to water. The composition and nutritional levels of the diet are shown in Table 1.

Table 1

Table 1. Ingredient and nutrient levels of diets (%, DM basis, unless otherwise stated).

2.2 Sampling

Prior to slaughter, animals underwent a 24-hour fasting period and a 2-hour water deprivation. Within 30 minutes after slaughter, LD, DI, PM, BF, and ST muscle samples were harvested. Samples were transported under refrigerated conditions (4 °C) to the laboratory, where fascia, adipose, and connective tissues were promptly excised. Subsequently, samples were allocated into three groups: (1) fresh tissue (n=6) designated for immediate meat quality assessment; (2) muscle tissue fixed in 4% paraformaldehyde (v/v 1:10) for histological preservation; (3) 10 g samples rapidly snap-frozen in liquid nitrogen and stored at -80°C for subsequent amino acid and fatty acid profiling.

2.3 Meat quality assessment

2.3.1 PH_45min

Muscle pH values were recorded 45 minutes after slaughter using a Testo205 portable pH meter (Testo Instrument International Trading (Shanghai) Co., Ltd., Shanghai, China). Each muscle sample was analyzed in triplicate.

2.3.2 Meat color

The brightness (L*), redness (a*), and yellowness (b*) values were measured using a CR 400 colorimeter (Konica Minolta (China) Investment Co., Ltd., Shanghai, China). Measurements were taken at three different points, and the average value was calculated.

2.3.3 Cooking loss

Approximately 80 g of tissue from various anatomical regions were weighed (Wa), vacuum-sealed in retort pouches, and thermally processed in an 80 °C water bath. Upon the core temperature reaching 70 °C, as verified by a food core thermometer, samples were unwrapped, surface moisture was blotted, and the final mass (Wb) was recorded after cooling. All measurements were conducted in triplicate. Cooking loss (CL) was determined as follows:

$\text{CL}(\%)=\{(\text{Wa}-\text{Wb})/\text{Wa}\}\times 100\%$ (Zhang et al., 2019)

2.3.4 Shear force

Following thermal treatment and cooling, meat samples were sectioned along the longitudinal axis of the muscle fibers into uniform dimensions (20 mm × 10 mm × 10 mm). Shear force was quantified using a CT3 texture analyzer (Brookfield, USA) equipped with a TA-SBA dovetail shear probe (Hou et al., 2014). Triplicate measurements were obtained for each specimen.

2.4 Muscle tissue morphology

After fixation for 24 hours, muscle samples were trimmed into approximately 0.5 cm tissue blocks, followed by dehydration, clearing, and paraffin embedding. Sections were then dewaxed and subjected to HE staining. Morphological assessment was conducted under an optical microscope across various anatomical regions in samples obtained from six horses. For each sample, five randomly selected non-overlapping fields at 200× magnification were analyzed. Measurements of muscle fiber diameter and cross-sectional area were performed using Image-Pro Plus 6.0 software (Media Cybernetics Corporation, Rockville, MD, USA).

2.5 Amino acid content determination

Amino acid content in the muscle samples was determined using high-performance liquid chromatography (HPLC) following the method described by Liang et al. (2024). The analysis was performed using an ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) system (ExionLC™ AD UHPLC-QTRAP 6500+, AB SCIEX, Massachusetts, USA). A total of 136 amino acid standards and five stable isotope-labeled internal standards were purchased from Sigma-Aldrich and Shanghai Zhenzhun Biotechnology Co., Ltd.

The amino acid standards were accurately weighed and prepared into a 5 mg/mL mixed stock solution. Isotope-labeled internal standards were added to obtain the internal standard working solution (IS), and all solutions were stored at -20 °C. The analysis was conducted using an ACQUITY UPLC BEH Amide column (2.1 × 100 mm, 1.7 μm) at 50 °C. The mobile phase A consisted of 5 mM ammonium acetate (with 0.1% formic acid), and phase B was acetonitrile (with 0.1% formic acid), with a flow rate of 0.30 mL/min. The gradient program was as follows: 0–1 min (90% B), 1–2 min (85% B), 2-3.5 min (75% B), 3.5–7 min (70% B), 7–10 min (45% B), 10-11.1 min (90% B). The ESI ion source parameters were set to a temperature of 550 °C, voltage of 5500 V, and gas pressure of 35 psi. Data were acquired in multiple reaction monitoring (MRM) mode, and quantification was performed using an external standard method (R² > 0.99). Concentrations were calculated based on the peak area of the MRM ion pairs.

2.6 Determination of fatty acid content

Fatty acids were analyzed using an UHPLC-MS/MS system (ExionLC™ AD UHPLC-QTRAP 6500+). A total of 50 fatty acid standards (Shanghai Zhenzhun) were used to prepare a 2000 μg/mL stock solution, which was then diluted with a 50% acetonitrile-isopropanol mixture to prepare the working standard solutions. The isotope-labeled internal standard (IS) and all solutions were stored at -20 °C. Lipid extraction was performed according to the method described by Crescenzo et al. (2015).

Chromatographic separation was conducted using a BEH C18 column (2.1 × 100 mm, 1.7 μm) at 40 °C. The mobile phases were: A, acetonitrile/water (1:1) with 0.1% formic acid; B, isopropanol/acetonitrile (1:1). The flow rate was set to 0.30 mL/min. The gradient program was as follows: 0–1 min (45% B), 1-4.5 min (45→70%), 4.5–9 min (70→75%), 9-12.5 min (75→80%), 12.5–14 min (80→100%), 14-15.1 min (100→45%), and 15.1–17 min (45%). Mass spectrometry was conducted in the ESI source mode with a temperature of 500 °C and a voltage of -4500 V. Data acquisition was performed in MRM mode, with quantification based on external standards (R² > 0.99) and concentration calculated using the peak area.

2.7 Statistical analysis

Data were analyzed using SPSS 27.0 software. A one-way analysis of variance (ANOVA) was performed to compare the pH values, shear force, color differences (L*, a*, b* values), cooking loss, muscle fiber characteristics, amino acids, and fatty acids of different muscle sites. Multiple comparisons between groups were conducted using the LSD method. Data are expressed as the mean ± standard error (Mean ± SE). Correlation analysis of all data from different muscle sites was performed using the Pearson correlation coefficient. Significance levels were determined as follows: P < 0.05 indicates a significant difference, and P < 0.01 indicates a highly significant difference.

3 Results

3.1 Differences in meat quality traits of Kazakh horses across different anatomical regions

Table 2 lists the differences in meat quality among different muscles of the Kazakh horse. The L* values of LD and PM were significantly higher than those of other sites (P < 0.01), while the L* value of DI was significantly higher than that of BF and ST (P < 0.05). The pH45min values for LD, PM, BF, and ST surpassed those of DI significantly (P < 0.01). For cooking loss, DI, BF, and ST had significantly higher losses than LD and PM (P < 0.01), with BF and ST also showing significantly higher losses than LD and PM (P < 0.05). The shear force of BF and ST was significantly higher than that of PM (P < 0.01).

Table 2

Table 2. Comparison of horse meat quality traits across different anatomical regions.

3.2 Morphological differences of different muscle tissues

Muscle sections from various anatomical regions were subjected to HE staining, and representative histological images obtained via optical microscopy were presented in Figure 1. Quantification of muscle fiber cross-sectional area was performed within identical visual fields (Figure 1F). BF exhibited the largest mean fiber area, significantly exceeding those of all other regions (P < 0.01), while DI showed the smallest values with a statistically significant difference from the remaining groups (P < 0.01). Consistent trends were observed in muscle fiber diameter, with BF displaying the greatest average diameter (P < 0.01) and DI the smallest (P < 0.01) (Figure 1G).

Figure 1

Figure 1. Morphological analysis of distinct muscle tissues in Kazakh horse. (A) Longissimus dorsi (B) Psoas major (C) Diaphragm (D) Biceps femoris (E) Semitendinosus (F) Cross-sectional area of muscle fibers (G) Muscle fiber diameter. Distinct capital letters above bars denote highly statistically significant differences (P < 0.01).

3.3 Comparison of amino acid contents among different muscle tissues

A total of 20 amino acids, consisting of 9 essential amino acids (EAAs) and 11 non-essential amino acids (NEAAs), were detected in muscle samples from various anatomical regions of Kazakh horses (Table 3). The most prevalent amino acids were L-Lysine, L-Glutamic acid, and L-Aspartic acid. Significant differences in amino acid composition were observed between the different muscle sites. In the LD group, the concentrations of L-Lysine and L-Glutamic acid were the highest among all groups. The PM group exhibited the highest content of L-Leucine, along with significantly higher EAA/NEAA and EAA/TAA ratios compared to the other groups. The ST group showed the highest content of L-Methionine. The DI group was particularly notable for higher levels of L-Isoleucine, L-Histidine, and several non-essential amino acids, such as Homocysteine, L-Alanine, L-Proline, L-Asparagine, L-Arginine, and L-Arginine. Meanwhile, the BF group had significantly higher levels of L-Phenylalanine and Glycine compared to the other groups. All these differences were statistically highly significant (P < 0.01). Figure 2 presents a hierarchical clustering heatmap that illustrates the differential expression of amino acids across muscle tissues, identifying 18 differentially expressed amino acids (P < 0.05), grouped into three distinct clusters.

Table 3

Table 3. Amino acid composition and content of different muscle tissues of Kazakh horse (mg/100g).

Figure 2

Figure 2. Hierarchical clustering heatmap of differentially expressed amino acids in different muscle tissues of Kazakh horse.

3.4 Comparison of fatty acid contents among different muscle tissues

A total of 19 fatty acids were detected in muscle samples from various parts of Kazakh horses, comprising 8 saturated fatty acids (SFAs), 7 monounsaturated fatty acids (MUFAs), and 4 polyunsaturated fatty acids (PUFAs) (Table 4). Among these, hexadecanoic acid (C16:0), elaidic acid (C18:1(n-9)T), and linoleic acid (C18:2(n-6)) were present in the highest amounts, with MUFAs being the predominant category. Fatty acid composition varied significantly across muscle sites. The total SFA content in the DI group was notably higher compared to all other groups (P < 0.01), with individual SFAs—including C12:0, C14:0, C15:0, C17:0, C18:0, and C22:0—also elevated. The highest total MUFA content was observed in the PM group (P < 0.01), with elaidic acid being the major constituent. Additionally, C17:1 levels were significantly higher in the ST group, while C14:1 was elevated in the LD group. The total PUFA content was significantly greater in the ST group than in all other groups (P < 0.01), primarily driven by n-6 PUFAs, particularly C18:2(n-6). Although the DI group exhibited significantly higher n-3 PUFA levels (P < 0.05), intergroup differences were relatively small. The n-6/n-3 PUFA ratio and the PUFA/SFA ratio were both significantly higher in the ST group compared to the other groups (P < 0.05). Figure 3 presents a hierarchical clustering heatmap illustrating differential fatty acid expression across muscle tissues, which reveals 15 fatty acids (P < 0.05) that were differentially expressed and grouped into three distinct clusters.

Table 4

Table 4. Fatty acid composition and content of different muscle tissues of Kazakh horse (mg/100g).

Figure 3

Figure 3. Hierarchical clustering heatmap of differentially expressed fatty acids in different muscle tissues of Kazakh horse.

3.5 Correlation analysis of muscle tissues

Amino acid composition exhibited a strong association with meat quality traits and muscle fiber characteristics (Figure 4). Specifically, L*, a*, and b* values showed significant positive correlations with L-Tryptophan and Homocysteine (P < 0.01), while inverse correlations were observed with Glycine (P < 0.01). Muscle fiber cross-sectional area and diameter demonstrated significant negative correlations with L-Tryptophan, Homocysteine, L-Isoleucine, L-Asparagine, L-Alanine, L-Threonine, L-Tyrosine, and L-Histidine (P < 0.05). In contrast, these fiber characteristics correlated positively with L-Glutamine, Glycine, L-Methionine, and L-Phenylalanine (P < 0.05).

Figure 4

Figure 4. Correlation heatmap of amino acids with meat quality traits and muscle fiber parameters. “*” indicates P < 0.05, “**” indicates P < 0.01.

Fatty acid compositions also revealed significant associations with meat quality and muscle fiber parameters (Figure 5). No significant correlations were identified between L*, a*, b*, pH, cooking loss, and shear force with C16:0 and C17:1. Muscle fiber characteristics exhibited a significant positive correlation with C18:1(n-7)T (P < 0.01), whereas negative correlations were identified with C14:1, C18:1(n-9)T, C14:0, C17:0, C12:0, C18:3(n-3), C15:0, and C19:1(n-9)T (P < 0.05).

Figure 5

Figure 5. Correlation heatmap of fatty acids with meat quality traits and muscle fiber parameters. “*” denotes P < 0.05, “**” denotes P < 0.01.

4 Discussion

Color constitutes a primary determinant of meat quality attributes, directly affecting consumer selection and primarily influenced by muscle type, dietary regimen, and age (Zhang et al., 2023). Experimental outcomes indicated significantly elevated L* values in LD and PM compared to DI, BF, and ST, aligning with findings reported for Italian Heavy Draft horses by Tateo et al. (2008). The predominance of oxidative fibers (e.g., type I) in LD and PM—characterized by smaller diameters and dense packing—permits more uniform light reflection, thereby yielding higher lightness values (Maqsood et al., 2016). In contrast, BF and ST exhibit a greater abundance of fast glycolytic fibers (e.g., type IIB), which are associated with larger diameters and increased connective tissue content (Li C. et al., 2024), leading to intensified light scattering and correspondingly reduced L* values (Choe and Kim, 2014). In this study, DI displayed a markedly higher a* value relative to other muscles, potentially attributable to its specialized functional role. As a muscle engaged in sustained contraction, DI possesses a comparatively denser capillary network (Cao et al., 2022). Adequate oxygen supply enhances the proportion of oxygenated myoglobin (bright red) in muscle tissue (Denzer et al., 2024). As an indicator of acidity, pH fluctuates in response to postmortem processes such as rigor mortis, aging, dissolution, and spoilage (Li H. et al., 2024). No significant differences were observed in the pH values of LD, PM, BF, and ST muscles (ranging from 5.89 to 5.93), aligning with earlier findings (Tateo et al., 2008; Franco and Lorenzo, 2014). The slightly acidic nature of horse meat is likely attributable to metabolic distinctions stemming from variations in muscle fiber type composition (Jin et al., 2024). Tenderness remains a primary determinant of meat palatability and overall sensory quality (Devatkal et al., 2018). PM muscle exhibited a significantly lower shear force compared to other regions, corroborating the findings of Roy et al (Roy and Bruce, 2024). Regional differences in tenderness are primarily influenced by muscle function, structural organization, metabolic profile, and the quantity and characteristics of collagen and connective tissue, which collectively modulate myofibrillar protein content and, consequently, tenderness.

Muscle fiber diameter and cross-sectional area are closely linked to meat quality traits such as tenderness, pH, and color. In this study, the BF site exhibited a significantly larger muscle fiber diameter compared to the LD, PM, and ST sites, alongside a lower water-holding capacity, which is consistent with the findings of Bünger et al. (2009). Muscle fiber diameter also showed significant correlations with fatty acid composition, suggesting its potential role in regulating intramuscular lipid metabolism and influencing water-holding capacity. Specifically, larger muscle fibers were associated with an accumulation of C18:1(n-7)T and a reduction in medium- and short-chain fatty acids, such as C14:0 and C18:1(n-9)T. The lower surface area-to-volume ratio of these fibers may contribute to a reduced density of sarcolemmal Na⁺/K⁺-ATPase, impairing the maintenance of ionic gradients and potentially leading to water loss (Vigh-Larsen et al., 2025). The BF site, characterized by a larger cross-sectional area and higher shear force, may be associated with increased myosin heavy chain (MyHC) expression and muscle fiber type transitions (Fan et al., 2024). Furthermore, muscle fiber structural parameters were significantly correlated with amino acid composition. Larger fibers exhibited negative correlations with eight amino acids, including L-Tryptophan and Homocysteine, which may be linked to energy metabolism and proteolysis. In contrast, positive correlations were observed with L-Glutamine and Glycine, suggesting their potential roles in maintaining muscle fiber structure or promoting collagen synthesis. In conclusion, muscle fiber characteristics likely influence meat shear properties by modulating amino acid and protein metabolism. The contrasting trends observed in the PM site, compared to BF, further support the existence of a systematic relationship between muscle fiber properties and meat quality indicators.

The amino acid profile of animal muscle plays a significant role in determining meat quality (Ribeiro et al., 2019). According to FAO/WHO standards, an ideal protein should have an EAA/TAA ratio of approximately 40% (Chen et al., 2021). Our results align with this standard, suggesting that the samples are a high-quality source of essential amino acids. The amino acid content varied significantly across different muscle sites. Specifically, the DI site exhibited a considerably higher total amino acid (TAA) content compared to other sites, while essential amino acid (EAA) levels in the PM and DI were significantly greater than those in the LD, BF, and ST. Non-essential amino acid (NEAA) contents in the DI and LD muscles were significantly higher than those in the PM, BF, and ST. These variations may be attributed to differences in the ratio of fast-twitch to slow-twitch muscle fibers, as well as disparities in protein synthesis and catabolism across muscle types (Chandel, 2021). Notably, consistent with Franco et al. (2010), the general pattern of amino acid composition was similar across muscle sites, suggesting that different muscles may follow comparable metabolic pathways to maintain homeostasis (J Ryan et al., 2021). L-Lysine was the most abundant essential amino acid, showing a significant positive correlation with pH, indicating its potential role in regulating acid-base balance. Additionally, L-Lysine plays a crucial role in lipid metabolism and helps maintain blood lipid stability (Steiber et al., 2004). The varying energy demands across muscle sites may account for its differential content. Methionine (Met), a precursor of cysteine (Cys) and a key factor in glutathione (GSH) synthesis, is converted to Cys through the transsulfuration pathway, promoting GSH production and enhancing antioxidant capacity (Shoveller et al., 2022). The significantly higher L-Methionine content in the ST site compared to the LD site may be due to the ST muscle’s frequent recruitment and dominance of type II fibers, which exhibit higher oxidative phosphorylation and increased ROS production. This requires an upregulation of the transsulfuration pathway to meet antioxidant demands. Furthermore, S-Adenosylmethionine (SAMe), a metabolite of Met, can activate adipogenic genes such as PPARγ through methylation, promoting intramuscular fat deposition (Niculescu and Zeisel, 2002), thereby providing energy support for the ST muscle. The correlation heatmap presented in Figure 4 further illustrates the close associations between amino acid composition and meat quality traits. Meat color parameters (L*, a*, b*) were significantly positively correlated with L-Tryptophan and Homocysteine, but negatively correlated with Glycine. These correlations suggest underlying biological mechanisms: L-Tryptophan may influence meat color through its metabolites, which are involved in pigment deposition or redox regulation (Ahmad et al., 2020). Homocysteine, as an intermediate in Met metabolism, may reflect the activity of the transsulfuration pathway, indirectly contributing to color stability by enhancing antioxidant defense (Li et al., 2016). Glycine may reduce color parameter values by promoting collagen synthesis, altering myofibrillar light-scattering properties, or through its antioxidant effects that inhibit myoglobin oxidation (He et al., 2024).In conclusion, amino acids not only serve as fundamental building blocks for proteins but also directly or indirectly regulate meat quality through multiple metabolic pathways, providing deeper insights into the biological functions of muscle tissue.

Fatty acids influence both the physical properties of adipose tissue and the sensory qualities of meat, including flavor and palatability (Larsson et al., 2004; Lee et al., 2025). In this study, distinct anatomical muscle regions in horses exhibited significant variation in fatty acid profiles, aligning with observations by Franco and Lorenzo (Franco and Lorenzo, 2014). These differences likely stem from a combination of factors, including fiber type composition, metabolic demand, and energy storage patterns. The DI region exhibited the highest concentration of SFA, particularly stearic acid (C18:0), a pattern attributed to its role as a core respiratory muscle engaged in sustained, high-frequency contractions (Barclay, 2017). The higher SFA content helps enhance membrane rigidity, maintain structural stability, and mitigate contraction-induced membrane damage (Zhao et al., 2022). Among the monounsaturated fatty acids (MUFAs), C18:1(n-9)T was the most abundant, likely due to its efficient synthesis from C18:0 in a single step catalyzed by Δ9-desaturase, a process that is both low in energy cost and provides high oxidative stability (Nagao et al., 2019). In contrast, other MUFAs (e.g., C16:1) require more complex multi-step processes, including elongation and desaturation of palmitic acid (C16:0) (Ziemlińska et al., 2021). Polyunsaturated fatty acids (PUFAs) were predominantly of the n-6 series, with linoleic acid (C18:2n-6) being the most abundant. This pattern is likely due to horses’ limited intestinal microbial hydrogenation, as monogastric animals, and their diets, which are typically high in n-6 PUFAs, coupled with active endogenous n-6 synthesis pathways, while n-3 PUFA synthesis is limited (Mariamenatu and Abdu, 2021). Although the PUFA/SFA ratio is an important indicator of nutritional quality, not all PUFAs are beneficial for cardiovascular health. Both linoleic acid and arachidonic acid have been inversely associated with cardiovascular disease risk, and a high n-6/n-3 ratio has been linked to increased risks of cancer and coronary heart disease (Marangoni et al., 2020). In this study, the PUFA/SFA ratio exceeded 0.40, and the n-6/n-3 ratio was below 4.0, meeting international recommendations (Smith et al., 2020), indicating favorable nutritional quality. The correlation heatmap in Figure 5 further revealed significant associations between fatty acids and meat quality traits: the redness (a*) was positively correlated with C23:0, C16:1, C22:0, C20:3(n-3), C18:0, and C20:3(n-6), likely due to the role of these fatty acids in maintaining the reduced state of myoglobin and regulating local microcirculation (Hendrickse and Degens, 2019). Both a* and yellowness (b*) showed positive correlations with medium-chain and odd-chain SFAs (e.g., C12:0, C14:0, C15:0, C17:0) as well as with C18:3(n-3) and C19:1(n-9)T, possibly through modifications in the optical properties of adipose tissue. In contrast, pH, cooking loss, and shear force were negatively correlated with the same set of fatty acids. Increased proportions of odd-chain SFAs (C15:0 and C17:0) were associated with reduced TBARS (a marker of lipid peroxidation) in mitochondrial and sarcolemmal membranes, improved membrane integrity, reduced cooking loss, and enhanced water-holding capacity (Wu et al., 2024). Medium-chain SFAs (C12:0, C14:0) may reduce membrane fluidity and calcium ion leakage, thereby inhibiting excessive contraction of myofibrillar proteins and decreasing shear force, which contributes to improved tenderness (Louesdon et al., 2015). No significant correlations were found between C16:0 or C17:1 and any of the measured traits, suggesting that these fatty acids may not be key factors influencing meat quality. Overall, these findings suggest that specific fatty acids can directly or indirectly influence meat color, water-holding capacity, and texture through multiple pathways, such as modulation of oxidative stability, membrane properties, and molecular interactions. These results offer new insights into the relationship between intramuscular fat metabolism and the formation of meat quality. Although the sample size (n=6) in this study is consistent with standard practice in similar animal experiments, it may limit the accuracy of the findings, and further validation is necessary.

5 Conclusion

The comprehensive analysis of Kazakh horse LD, PM, DI, BF, and ST muscles revealed distinct variations in meat quality traits, nutritional composition, and muscle fiber characteristics. Among all examined muscles, PM exhibited superior pH, L* value, and tenderness (P < 0.05), with water-holding capacity ranking second to LD (P < 0.05). Characteristic assessment of muscle fibers indicated a positive correlation between fiber area in BF and shear force, while fiber diameter in LD, PM, BF, and ST displayed an inverse relationship with water retention. Regarding amino acid profiles, PM demonstrated significantly elevated EAA/NEAA and EAA/TAA ratios compared to the other groups (P < 0.01), indicating a more favorable amino acid balance. Fatty acid analysis showed that ST possessed the highest PUFA content (P < 0.01), whereas PM had the highest MUFA concentration. Collectively, PM displayed favorable characteristics in meat quality, amino acid balance, and MUFA concentrations. These results contribute to a foundational reference for the nutritional optimization, processing strategies, and utilization of horse meat products.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

The animal study was approved by Animal Welfare and Ethics Committee of Xinjiang Agricultural University. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

LL: Data curation, Writing – original draft. WR: Writing – original draft, Formal analysis. RW: Validation, Writing – original draft. ZL: Writing – original draft, Software. SM: Writing – original draft, Validation. QH: Writing – original draft, Supervision. YS: Formal analysis, Writing – original draft. DS: Methodology, Writing – original draft. JW: Writing – review & editing, Funding acquisition.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. This research was funded by Key R&D projects of Xinjiang Uygur Autonomous Region (2024B02013-3-2), Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology (XJMFY202406), Central Guidance for Local Science and Technology Development Fund (ZYYD2025JD02), and Basic Research Funding Projects for Scientific Research in Xinjiang Universities (XJEDU2025J057).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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The author(s) declare that no Generative AI was used in the creation of this manuscript.

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