Comparison of carcass characteristics and meat quality traits between Nanyang yellow cattle and Nanyang yellow crossbred cattle

Comparison of carcass characteristics and meat quality traits between Nanyang yellow cattle and Nanyang yellow crossbred cattle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of carcass characteristics and meat quality traits between Nanyang yellow cattle and Nanyang yellow crossbred cattle Wentao Li, Jiashun Sheng, Liuyi Wang, Bo Zhang, Yanhao Gao, Dong Liang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8294515/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study systematically evaluated the carcass characteristics and meat quality of the Nanyang yellow cattle (NYC), Pinan cattle (Piedmontese × NYC; PNC) and Denan cattle (German yellow ×NYC; DNC). DNC and PNC exhibited significantly higher carcass weight, lean meat percentage, dressing percentage, and meat-to-bone ratio, while NYC demonstrated superior tenderness and water-holding capacity. NYC had higher saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA), whereas PNC contained more polyunsaturated fatty acids (PUFA) and potassium (K) content. The Mantel correlation analysis demonstrated that essential amino acids (EAA) and flavor amino acids (FAA) levels were closely associated with crude protein content. Furthermore, SFA, PUFA and MUFA exhibited significant relationships with lean meat yield, whereas K and phosphorus (P) were correlated with carcass weight and meat pH. Collectively, Nanyang yellow crossbred cattle could enhance production performance and slaughter performance, providing valuable insights for conservation and genetic improvement of Chinese indigenous cattle. Nanyang yellow cattle Crossbreeding Carcass characteristics Meat quality Fatty acid composition Figures Figure 1 Figure 2 1. Introduction As a vital source of animal-derived nutrition, beef provides essential nutrients such as high-quality protein, fatty acids, vitamins, and minerals, and play an indispensable role in human health and dietary structure (Davis et al., 2022). Meat quality is shaped by a complex interplay of genetic, environmental, managerial, and health-related factors, encompassing breed, sex, slaughter age, diet, disease status, pre-slaughter handling, and postmortem aging (Papanikolopoulou et al., 2025). Among these determinants, breed is widely recognized as a decisive factor influencing critical sensory and mechanical attributes, including tenderness, juiciness, and shear force (López-Pedrouso et al., 2020; Sakowski et al., 2022). Previous studies have demonstrated that crossbred progeny from native and foreign breeds exhibited superior performance and economic traits compared to indigenous breeds (Engle et al., 2025). Crossbreeding with Simmental has been reported to improve carcass characteristics and increase intramuscular fat deposition in native Chinese breeds such as Xuanhan and Jinjiang yellow cattle (Wang et al., 2021). Likewise, the introduction of Angus bloodlines has been associated with favorable changes in fatty acid profiles of Chinese Simmental cattle, contributing to enhanced meat quality (Liu et al., 2020). Moreover, Angus × Qinchuan crossbred offspring exhibit superior growth performance and carcass yield compared with Qinchuan cattle (Mei et al., 2019). Therefore, elucidating differences among crossbred progeny is essential, as crossbreeding has become an important strategy for improving production performance and meat quality. Nanyang cattle are known for their large body size, tender meat, and strong disease resistance (Shi et al., 2024). Despite these advantageous traits, NYC exhibit certain production limitations such as slow growth and low slaughter percentage. To address these issues, crossbreeding strategies have been implemented. For instance, Xianan cattle, derived from crossing NYC (dam) with Charolais bulls (sire), demonstrate improved growth performance and meat production compared to purebred NYC (Song et al., 2024). Similarly, Pinan cattle (PNC), a crossbred of NYC (dam) and Piedmontese cattle (sire), exhibiting the meat quality of NYC with the rapid growth and enhanced carcass characteristics of Piedmontese cattle (Shi et al., 2024). Denan cattle (DNC), developed by crossing NYC (dam) with German Yellow cattle (sire), were bred to improve overall body conformation and growth performance. Although previous studies have reported the phenotypic advantages of NYC, research on how crossbreeding with foreign breeds affects meat quality traits remains relatively limited. This study compared the carcass characteristics and meat quality between Nanyang yellow cattle and Nanyang yellow crossbred cattle. To explore associations between fatty acid profiles, amino acid composition, and meat quality traits, Mantel correlation analysis was conducted using Bray–Curtis distances, while Pearson correlation analysis was applied to evaluate the links between trace element concentrations and meat quality. Overall, the findings reveal breed-specific variations and provide essential evidence to guide crossbreeding strategies for improving production efficiency and meat quality in Chinese indigenous cattle. 2. Materials and methods 2.1 Animal management and sample collection A total of 16 male castrated cattle, including Nanyang cattle (NYC, n = 4), Pinan cattle (PNC, Piedmontese♂ × Nanyang♀, n = 8), and Denan cattle (DNC, German Yellow♂ × Nanyang♀, n = 4), were maintained under uniform management and feeding conditions at a commercial fattening farm. All animals had verified pedigrees, were healthy and disease-free. Feeding followed the “Beef Cattle Feeding Standard” (NY/T 815–2004). After approximately 12 months of fattening, all animals were slaughtered at Nanyang Kerqin Co., Ltd. Slaughtering procedures followed the national standard (GB/T 19477 − 2004). Following exsanguination, carcasses were chilled at 0–4°C for 24 h, after which samples of the longissimus dorsi muscle were excised from the region between the 12th and 13th ribs. The collected tissues were kept at 4°C and equilibrated to room temperature prior to analysis. All experimental procedures involving animals complied with the guidelines for animal protection and welfare issued by the Ministry of Agriculture and Rural Affairs of China, and muscle sampling was approved by the Ethics Committee of the College of Animal Science and Technology, Henan Agricultural University (HMUD2024031224). 2.2 Carcass characteristics determination Slaughter weight was recorded after 24 h of feed withdrawal. Carcass weight was defined as the weight after removal of the head, hooves, tail, hide, internal organs, and reproductive organs. Lean meat weight was defined as the carcass weight after removal of the bones. Lean meat percentage (%) was calculated as: (Lean meat weight/Slaughter weight) × 100%. Dressing percentage (%) was calculated as: (Carcass weight/Slaughter weight) × 100%. Meat/Bone ratio: (Lean meat weight/Total weight of all bones in the carcass) × 100%. 2.3 Meat quality characteristics The pH value was measured using the waterproof pH meter (HI98163, Hanna Instruments, USA). The pH meter was calibrated with standard buffers (pH 4.0 and pH 7.0) before each use. The probe was inserted laterally into the sample, and measurements were taken at three random points. Each point was measured three times with readings stabilized for more than five seconds, and the average was used as the final pH value (maximum deviation < 5%). Muscle strips approximately 5 cm in length, 3 cm in width, and 2 cm in height were prepared along the direction of the muscle fibers. Each strip was suspended by one end using a fine wire hook in the center of a food-grade plastic bag, which was then sealed. Samples were stored at a strictly controlled temperature of 0–4°C for 24 h. After removal, surface moisture was gently blotted with filter paper, and the samples were immediately weighed using an electronic scale with an accuracy of 0.1 g. Drip loss (%) = (Initial meat weight – Meat weight after hanging)/Initial meat weight × 100%. Shear force was measured using a TA-XT Plus texture analyzer (Stable Micro Systems, UK). Each 100 g sample was cut into 1.27 cm diameter cores along the muscle fibers, cooked at a constant center temperature of 75°C for 16 min, cooled to room temperature, and then sheared perpendicular to the muscle fiber direction. The above method parameters were described in previous study (Hou et al., 2014). 2.4 Fatty acid composition Fatty acid composition quantification was performed according to the National Food Safety Standard for Determination of Fatty Acids in Foods (GB 5009.168–2016). Brief, approximately 0.5 g of freeze-dried and homogenized meat into a 15 mL dry screw-thread glass tube. Sequentially add n-hexane, chloroacetyl-methanol solution, and saturated potassium carbonate solution, followed by vortex-mixing to generate fatty acid methyl esters (FAMEs). Quantify FAMEs using a GC-2014C gas chromatograph (Shimadzu Corp., Kyoto, Japan) with optimized temperature programming. Fatty acids were identified based on chromatographic peaks and quantified as relative percentages of the total fatty acids. Detailed experimental protocols followed the previous study (Zhang et al., 2022). 2.5 Amino acid profiles Free amino acids were quantified following the Chinese National Standard GB 5009.124–2016. Briefly, homogenized beef samples were hydrolyzed with 6 mol/L HCl under nitrogen at 110°C for 22–24 h, dried, re-dissolved in pH 2.2 citrate buffer, filtered (0.22 µm), and analyzed on an automatic amino acid analyzer (L-8900, Hitachi, Japan) with post-column ninhydrin detection at 570/440 nm. Calibration was performed using external standards and results were expressed as g/100 g fresh weight (Wu et al., 2024). Sixteen amino acids were quantified. Non-essential amino acids included threonine, valine, isoleucine, phenylalanine, methionine, and lysine; essential amino acids comprised aspartate, serine, glutamate, glycine, alanine, tyrosine, histidine, arginine, and proline. Additionally, glutamic acid, aspartic acid, phenylalanine, alanine, glycine, and tyrosine were identified as flavor amino acids contributing to the umami taste of the samples. 2.6 Nutritional composition profiles The contents of moisture, crude protein, and crude fat were determined in accordance with the Chinese National Standards. Moisture content was measured using the distillation method according to Determination of Moisture in Food (GB 5009.3–2016). Crude protein content was analyzed by the Kjeldahl method following Determination of Protein in Food (GB 5009.5–2016). Crude fat content was determined using the acid hydrolysis method combined with Soxhlet extraction in accordance with Determination of Fat in Food (GB 5009.6–2016). Trace elements were analyzed using an inductively coupled plasma optical emission spectrometer (ICP-OES, Agilent 5110, USA) according to the Chinese National Standard GB 5009.268–2016. The detailed procedures were based on this reference (Gerber et al., 2009). 2.7 Statistical analysis All data were analyzed using R software (version 4.3.1). Data manipulation and visualization were performed using the packages tidyverse and ggplot2. Group differences were assessed using one-way analysis of variance (One-way ANOVA) with the stats package. Tukey’s Honestly Significant Difference (HSD) post hoc tests were performed using the agricolae package. Independent samples t -tests for pairwise comparisons were conducted using the rstatix package. Mantel correlation analysis was performed with the vegan, linkET, dplyr, ggplot2, and RColorBrewer packages. Pearson correlations were calculated using linkET, mantel tests were applied to assess relationships among amino acid profiles, fatty acid composition, and nutrient data based on Bray–Curtis distances (Version 0.0.7.4. Retrieved from https://github.com/Hy4m/linkET ). 3. Results and discussion 3.1 Comparison of the slaughter performance Slaughter performance serves as a quantitative benchmark for evaluating meat yield and guiding genetic selection (Sakowski et al., 2022). Slaughter weight, dressing percentage, lean meat percentage, and meat-to-bone ratio collectively reflect muscular development and determine the economic value of the carcass (Gherissi et al., 2023). This study evaluated the slaughter performance of NYC, DNC, and PNC, with detailed comparative data presented in Table 1 . The results demonstrated that the mean values for slaughter weights of DNC (585.75 kg) and PNC (579.25 kg) were significantly higher than that of NYC (431.25 kg) ( P < 0.05), with no significant difference was detected between the two crossbred groups. A similar trend was observed in carcass weight, which averaged 233.67 kg, 334.47 kg, and 330.05 kg for NYC, DNC, and PNC, respectively. Furthermore, the lean meat weight was approximately 1.4 and 1.5 times higher in DNC and PNC compared to NYC, respectively ( P < 0.01). In addition, both crossbred groups also exhibited significantly higher dressing percentages and lean meat percentages than NYC ( P < 0.05). Interestingly, the dressing percentage of PNC was slightly higher than that of DNC, with a statistically significant difference ( P < 0.05). Previous studies suggested that this superiority may stem from beneficial genetic traits introduced by the Piedmontese breed (Wei et al., 2022). In conclusion, the results demonstrated that both DNC and PNC possessed significant advantages in improved the carcass characteristics of NYC. The meat-to-bone ratio is a critical indicator for assessing muscle deposition in different cattle populations, reflecting both meat architecture and carcass merit, and is widely used in carcass evaluation and yield prediction (Conroy et al., 2010), In this study, the meat-to-bone ratios of DNC and PNC was 6.50 and 6.48, respectively, both significantly higher than that of NYC (4.05) ( P < 0.05). These elevated ratios suggest improved meat production efficiency and a more favorable balance between skeletal growth and muscle development. These results further demonstrated the superior muscle deposition capacity of the DNC and PNC crossbred groups compared to the NYC. Table 1 Comparative analysis of carcass characteristics among DNC, PNC and NYC Breed NYC DNC PNC Slaughter weight (kg) 431.25 ± 52.00 b 585.75 ± 41.57 a 579.25 ± 35.35 a Carcass weight (kg) 233.67 ± 27.14 b 334.47 ± 23.37 a 330.05 ± 28.47 a Lean meat weight (kg) 187.29 ± 24.46 c 285.58 ± 24.38 ab 294.89 ± 26.99 a Lean meat percentage (%) 43.49 ± 3.16 b 48.72 ± 0.012 a 50.38 ± 0.02 a Dressing percentage (%) 54.27 ± 2.76 b 56.24 ± 0.01 a 58.68 ± 0.02 b Meat/bone ratio 4.05 ± 0.49 b 6.5 ± 0.52 a 6.48 ± 0.34 a Note: Values are presented as mean ± standard error. Different superscript letters within a subcolumn are significantly different at P < 0.05. 3.2 Comparison of the meat quality Beef quality encompasses multiple dimensions, including sensory attributes, nutritional composition, potential health benefits, and processing suitability. Physicochemical parameters such as pH, water-holding capacity, shear force, and meat color are key factors influencing consumer acceptability and the overall palatability of beef (Zhou et al., 2024). As shown in Table 2 , the average shear force value of NYC (3.8 kgf) was significantly lower than those of PNC (5.74 kgf) and DNC (7.73 kgf) ( P < 0.05). Previous studies have established a significant negative correlation between shear force and consumer sensory preference, with values below 4.0 kgf indicating superior tenderness (Lorenzen et al., 2003). These results demonstrate that NYC exhibits better tenderness compared to the Nanyang yellow crossbred cattle. Beef water-holding capacity (WHC) is closely associated with texture and eating quality, significantly influencing consumers' perception of juiciness and overall palatability(Liu et al., 2022). In this study, WHC was assessed by measuring drip loss. The results shown that NYC exhibited lower drip loss compared to PNC and DNC, which was consistent with its superior tenderness as reflected in shear force values. Although PNC and DNC demonstrated superior slaughter performance, NYC displayed advantages in both tenderness and moisture retention. These differences may arose from intrinsic factors such as muscle fiber type composition, connective tissue properties, and metabolic characteristics specific to purebred cattle (Hocquette et al., 2007). These findings underscore the importance of conserving local breeds that possess desirable meat quality traits in crossbreeding programs. As shown in Table 2 , the NYC exhibited a higher pH value of 6.60, whereas samples from DNC and PNC shown pH values ranging between 5.4 and 5.6. The elevated pH in NYC may be attributed to a slower rate of postmortem glycolysis or greater muscle glycogen reserves (Chauhan et al., 2019). A moderately high ultimate pH contributed to improved water-holding capacity and tenderness, which was supported by its lower drip loss and reduced shear force values. (Li et al., 2014). However, excessively high pH may adversely affected meat color and shelf life, as darker meat was generally less acceptable to consumers and more susceptible to microbial spoilage (Wu, 2020). In contrast, the lower pH values in PNC and DNC beef indicated a more rapid postmortem acidification, which may improve visual appearance but often at the expense of reduced tenderness and juiciness. These results underscore the multifaceted influence of pH on beef quality and emphasized the importance of balancing sensory attributes with technological properties in crossbreeding strategies. Table 2 Comparative analysis of meat quality traits among DNC, PNC and NYC Items DNC PNC NYC Shear force (kgf) 7.73 ± 1.76 a 5.74 ± 1.07 a 3.8 ± 0.5 b Drip loss(%) 6.72 ± 1.24 a 5.61 ± 1.11 a 4.02 ± 0.99 b pH 5.53 ± 0.05 a 5.32 ± 0.14 a 6.60 ± 0.16 b Note: The data are presented as mean ± standard deviation; a, b Differing superscript letters within the same row indicate significant differences ( P < 0.05) between breeds. 3.3 Comparison of amino acid composition Amino acids, as the fundamental building blocks of proteins, play vital roles in both human nutrition and animal physiology. The amino acid profile of muscle tissue is not only critical for dietary quality but also represents an economically important trait in beef cattle (Vieillevoye et al., 2020). This study measured 16 amino acids (Table 3 ), including nine essential amino acids (EAAs) and seven non-essential amino acids (NEAAs). In DNC samples, the mean contents of EAA, NEAA, flavor amino acids (FAA), and total amino acids (TAA) was 7.27 g/100 g, 13.94 g/100 g, 8.88 g/100 g, and 21.21 g/100 g, respectively. The corresponding values for PNC were 7.18 g/100 g, 14.08 g/100 g, 8.77 g/100 g, and 21.26 g/100 g, with no significant differences between the two crossbred groups. Among all amino acid, glutamic acid was the most abundant amino acid, followed by proline, aspartic acid, lysine, leucine, and arginine, with no significant differences between DNC and PNC. According to FAO/WHO standards, high-quality animal protein should contain approximately 40% EAAs and 60% NEAAs of the TAA (Ge et al., 2023). The EAA/TAA ratios in the longissimus dorsi muscle of both PNC and DNC were below 40%, suggested that the nutritional quality of crossbred beef groups remained slightly inferior to that of premium protein sources. Table 3 Comparative analysis of amino acid composition between DNC and PNC Amino Acid Content (g/100g) DNC PNC *Threonine ( Thr ) 0.96 ± 0.07 a 0.96 ± 0.07 a * Isoleucine (Ile) 0.69 ± 0.06 a 0.70 ± 0.06 a *Leucine (Leu) 1.58 ± 0.13 a 1.57 ± 0.12 a *Valine (Val) 0.88 ± 0.05 a 0.85 ± 0.07 a * Lysine (Lys) 1.82 ± 0.15 a 1.79 ± 0.14 a *Methionine (Met) 0.45 ± 0.06 a 0.45 ± 0.04 a *Phenylalanine (Phe) ▲ 0.89 ± 0.07 a 0.86 ± 0.09 a Asparagine (Asp) ▲ 2.03 ± 0.15 a 1.99 ± 0.14 a Glutamine (Glu) ▲ 3.21 ± 0.25 a 3.13 ± 0.19 a Glycine (Gly) ▲ 0.85 ± 0.05 a 0.88 ± 0.08 a Alanine (Ala) ▲ 1.23 ± 0.09 a 1.21 ± 0.09 a Tyrosine (Tyr) ▲ 0.67 ± 0.05 a 0.7 ± 0.09 a Serine (Ser) 0.96 ± 0.06 a 0.95 ± 0.07 a Histidine (His) 0.78 ± 0.07 a 0.81 ± 0.10 a Arginine (Arg) 1.40 ± 0.15 a 1.73 ± 0.65 a Proline (Pro) 2.81 ± 0.24 a 2.68 ± 0.13 a *Essential amino acid (EAA) 7.27 ± 0.59 a 7.18 ± 0.59 a Non-Essential amino acid (NEAA) 13.94 ± 1.11 a 14.08 ± 1.54 a Delicious amino acid (DAA) 8.88 ± 0.66 a 8.77 ± 0.77 a Total Amono Acid(TAA) 21.21 ± 1.77 a 21.26 ± 2.13 a EAA/NEAA 0.52 ± 0.00 a 0.51 ± 0.00 a EAA/TAA 0.34 ± 0.00 a 0.34 ± 0.00 a Note: “ * ” indicates essential amino acids, “▲” indicates flavor amino acids. Values are presented as mean ± standard error. Different superscript letters within a subcolumn are significantly different at P < 0.05. Certain amino acids, such as glutamic acid, aspartic acid, glycine, and alanine, are critical contributors to beef flavor (Ge et al., 2023). They impart umami and sweet tastes, and act as precursors for volatile flavor compounds (e.g., alcohols, aldehydes, and ketones) generated through Maillard reactions and thermal degradation during cooking (Dashdorj et al., 2015). This study analyzed key flavor-related amino acids, including phenylalanine, aspartic acid, glutamic acid, glycine, alanine, and tyrosine. The overall flavor amino acid profile was similar between DNC and PNC, with no statistically significant differences. 3.4 Composition of fatty acids Fatty acid composition is a key factor influencing beef quality, directly affecting flavor, juiciness, and texture (Rodriguez et al., 2024; Zhang et al., 2025). In addition, the nutritional value of beef is closely linked to its fatty acid profile, with certain fatty acids associated with cardiovascular and metabolic health benefits (Fan et al., 2020). Therefore, optimizing fatty acid composition is essential not only for enhancing meat quality and market appeal but also for promoting consumer health. A total of 19 fatty acid compositions were detected, including nine saturated fatty acids (SFAs), six monounsaturated fatty acids (MUFAs), and four polyunsaturated fatty acids (PUFAs). Among them, SFAs accounted for the highest proportion (45.69%), followed by MUFAs (32.02%) and PUFAs (21.66%). In this study, the fatty acid profiles of the three cattle groups (DNC, PNC, and NYC) exhibited distinct differences (Fig. 1 ). NYC as a native breed, shown the highest total SFA content (52.44%), largely due to elevated levels of palmitic acid (C16:0) and stearic acid (C18:0), which were consistent with the previous reports (Zhang et al., 2022). Myristic acid (C14:0), (C16:0), and (C18:0) were the predominant SFAs in both the NYC and DNC groups, and the levels of these three fatty acids were significantly different compared to the PNC group ( P < 0.001). Notably, the contents of capric acid (C10:0), heptadecanoic acid (C17:0), heneicosanoic acid (C21:0), and docosanoic acid (C22:0) were significantly higher in the PNC group ( P < 0.001). (C16:0) is a major product of de novo lipogenesis and plays a critical role in meat tenderness and fat deposition, whereas (C17:0), (C21:0) and (C22:0) contribute to the enhancement of meat flavor (Abebe et al., 2024; Bai et al., 2024). While high SFA levels could enhance flavor, excessive intake may adversely affect cardiovascular health (Praagman et al., 2016). In contrast, MUFAs and PUFAs were known to help reduce cholesterol and triglyceride levels and support lipid metabolism (Dicks, 2024). In this study, the MUFAs relative contents in NYC, DNC, and PNC groups was 44.27%, 39.38%, and 35.83% ( P < 0.001). The longissimus dorsi muscle of the DNY exhibited the lowest levels of myristoleic acid (C14:1n5), whereas the PNC showed higher concentrations of palmitoleic acid (C16:1n7) and erucic acid (C22:1n9) compared to the other two groups ( P < 0.001). (C22:1n9) has been shown to promote intramuscular fat deposition in some animals, potentially by upregulating lipogenic genes or suppressing fatty acid catabolism, thus contributing to lipid accumulation (Yu et al., 2025). Notably, the PUFA level in the PNC was significantly higher than those in DNC and NYC( P < 0.001). This was largely attributable to elevated concentrations ( P < 0.001) of linoleic acid (C18:2n6c) and arachidonic acid (C20:4n6), both of which play vital roles in anti-inflammatory processes and maintain cellular membrane integrity (Calder, 2015). Although the high SFA content in NYC may improve flavor and consumer acceptability, its relatively low MUFA and PUFA content could limit nutritional balance (Lukic et al., 2021). These results provide some evidence that crossbreeding has the potential to improve fat deposition. 3.5 Composition of micronutrients Mineral content is a key indicator influencing carcass meat quality, with existing studies suggesting a potential positive correlation with beef tenderness(Tizioto et al., 2014). As shown in Table 4 , the trace element profiles revealed minimal variation across breeds. A statistically significant difference was observed only in K content, which was higher in PNC (4140 mg/kg) than in DNC (3890 mg/kg) ( P < 0.05). Other elements (gallium, P, sodium, magnesium, iron, and zinc) shown no statistically significant differences. The higher potassium content in PNC, along with its higher dressing percentage, may synergistically enhance postmortem glycolysis, thereby contributing to improved meat tenderness (Ferguson & Gerrard, 2014). Table 4 Comparative analysis of trace elements between DNC and PNC Items DNC PNC Ga(mg/kg) 46.70 ± 13.01 a 72.99 ± 34.14 a Na(mg/kg) 4.69*10 2 ±0.44 a 5.05*10 2 ±0.66 a P(mg/kg) 1.89*10 3 ±0.05 a 1.98*10 3 ±0.08 a Mg(mg/kg) 2.38*10 2 ±0.06 a 2.46*10 2 ±0.11 a K(mg/kg) 3.89*10 3 ±0.18 a 4.14*10 3 ±0.17 b Fe(mg/kg) 20 ± 4.23 a 23.23 ± 6.90 a Zn(mg/kg) 51.65 ± 9.28 a 51.08 ± 9.43 a Note: Values are presented as mean ± standard error. Different superscript letters within a subcolumn are significantly different at P < 0.05. 3.6 Correlation analysis among traits To elucidate the relationships between carcass and meat quality traits in crossbred cattle, correlation analyses were performed on key biochemical indicators. Mantel tests were applied to assess the associations among amino acid composition, fatty acid composition, and micronutrient profiles. Nutritional composition data, including crude protein and moisture content, were presented in Table 5 . Table 5 Comparative analysis of essential nutrients among DNC, PNC and NYC Items DNC PNC Crude protein(g/100g) 21.72 ± 0.87 a 22.34 ± 0.92 a Moisture% 75.21 ± 0.89 a 75.09 ± 0.86 a Intramuscular fat% 2.28 ± 1.11 a 1.41 ± 0.81 a Note: Values are presented as mean ± standard error. Different superscript letters within a subcolumn are significantly different at P < 0.05. The results revealed significant correlations between these biochemical matrices and carcass characteristics (Fig. 2 ). Specifically, associations were observed between crude protein content and flavor amino acids, while SFAs and PUFAs exhibited significant positive correlations with muscle yield. Additionally, the K and P were significantly positively correlated with carcass weight and meat pH, which was similar to previous research (Nakamura & Kikuchi, 2017). In the present study, K and P were implicated in the regulation of muscle pH and may contribute to energy metabolism and protein synthesis, ultimately influencing both carcass weight and muscle development. These findings indicated that the biochemical composition of muscle tissue was intrinsically associated with meat quality attributes. Conclusions In summary, the present study offers a comprehensive database of carcass characteristics and meat quality in Nanyang yellow cattle and Nanyang yellow crossbred cattle. NYC was characterized by superior tenderness, higher water-holding capacity, and a favorable amino acid profile, whereas PNC and DNC displayed advantages in growth rate, carcass yield, specific fatty acid, and mineral traits. Further analyses indicated that amino acid, fatty acid, and micronutrient profiles were closely associated with carcass and meat quality traits. The study was designed to provide fundamental data on crossbred progeny derived from Chinese indigenous yellow cattle and to offer a scientific basis for informed crossbreeding strategies. Declarations Author Contribution W.L. designed the study, performed formal analysis and data curation, and wrote the original draft.J.S. conducted investigation and data curation and contributed to reviewing and editing the manuscript.L.W. contributed to investigation, visualization, and data processing.B.Z. contributed to investigation and validation.Y.G. contributed to methodology, data interpretation, and reviewing and editing.D.L. contributed to methodology, resources, and data curation.H.X. performed methodology and formal analysis.J.W. contributed to methodology and formal analysis.T.F. contributed to conceptualization, supervision, and funding acquisition.T.G. contributed to conceptualization, supervision, and project administration.Y.S. contributed to conceptualization, supervision, reviewing and editing, and funding acquisition.T.Z. contributed to conceptualization, supervision, reviewing and editing, visualization, and funding acquisition. Acknowledgement The authors would like to thank the staff at Nanyang Yellow Cattle Breeding Farm and Kerchin Cattle Industry (Nanyang) Co., Ltd. in Henan of China for caring for animals and collecting biological samples. This study was supported by the Science and Technology Innovation 2030—Major Projects (Grant No. 2023ZW0404802) and the Henan Province Science and Technology Project (Grant No. 242102111002). This project was also partially supported by the National Key Research and Development Program of China (Grant No. 2024YFF1000100) and Henan Agriculture Research System (HARS-22-13-G1) for data analysis and interpretation. Data Availability Data will be made available on request. Funding declaration: This work was supported by the Science and Technology Innovation 2030—Major Projects (2023ZW0404802), Henan Province Science and Technology Project (242102111002), National Key Research and Development Program of China (2024YFF1000100) and the Henan Agriculture Research System (HARS–22-13-G1). References Abebe, B. K., Wang, J., Guo, J., Wang, H., Li, A., & Zan, L. (2024). A review of emerging technologies, nutritional practices, and management strategies to improve intramuscular fat composition in beef cattle. Animal Biotechnology , 35 (1), 2388704. https://doi.org/10.1080/10495398.2024.2388704 Bai, H., Wang, L., Lambo, M. T., Li, Y., & Zhang, Y. (2024). Effect of changing the proportion of C16:0 and cis-9 C18:1 in fat supplements on rumen fermentation, glucose and lipid metabolism, antioxidation capacity, and visceral fatty acid profile in finishing Angus bulls. Animal Nutrition , 18 , 39-48. https://doi.org/https://doi.org/10.1016/j.aninu.2024.04.010 Calder, P. C. (2015). Functional Roles of Fatty Acids and Their Effects on Human Health. JPEN J Parenter Enteral Nutr , 39 (1 Suppl), 18s-32s. https://doi.org/10.1177/0148607115595980 Chauhan, S. S., LeMaster, M. N., Clark, D. L., Foster, M. K., Miller, C. E., & England, E. M. (2019). Glycolysis and pH Decline Terminate Prematurely in Oxidative Muscles despite the Presence of Excess Glycogen. Meat and Muscle Biology , 3 (1), 254. https://doi.org/10.22175/mmb2019.02.0006 Conroy, S. B., Drennan, M. J., McGee, M., Keane, M. G., Kenny, D. A., & Berry, D. P. (2010). Predicting beef carcass meat, fat and bone proportions from carcass conformation and fat scores or hindquarter dissection. Animal , 4 (2), 234-241. https://doi.org/10.1017/s1751731109991121 Dashdorj, D., Amna, T., & Hwang, I. (2015). Influence of specific taste-active components on meat flavor as affected by intrinsic and extrinsic factors: an overview. European Food Research and Technology , 241 (2), 157-171. https://doi.org/10.1007/s00217-015-2449-3 Davis, H., Magistrali, A., Butler, G., & Stergiadis, S. (2022). Nutritional Benefits from Fatty Acids in Organic and Grass-Fed Beef. Foods , 11 (5), 646. https://doi.org/10.3390/foods11050646 Dicks, L. M. T. (2024). How important are fatty acids in human health and can they be used in treating diseases? Gut Microbes , 16 (1), 2420765. https://doi.org/10.1080/19490976.2024.2420765 Engle, B. N., Thallman, R. M., Snelling, W. M., Wheeler, T. L., Shackelford, S. D., King, D. A., & Kuehn, L. A. (2025). Breed-specific heterosis for growth and carcass traits in 18 U.S. cattle breeds. Journal of Animal Science , 103, skaf048. https://doi.org/10.1093/jas/skaf048 Fan, Y., Han, Z., Arbab, A. A. I., Yang, Y., & Yang, Z. (2020). Effect of Aging Time on Meat Quality of Longissimus Dorsi from Yunling Cattle: A New Hybrid Beef Cattle. Animals (Basel) , 10 (10), 1897. https://doi.org/10.3390/ani10101897 Ferguson, D. M., & Gerrard, D. E. (2014). Regulation of post-mortem glycolysis in ruminant muscle. Animal Production Science , 54 (4), 464-481. https://doi.org/https://doi.org/10.1071/AN13088 Ge, F., Li, J., Gao, H., Wang, X., Zhang, X., Gao, H., Zhang, L., Xu, L., Gao, X., Zhu, B., Wang, Z., & Chen, Y. (2023). Comparative analysis of carcass traits and meat quality in indigenous Chinese cattle breeds. Journal of Food Composition and Analysis , 124 , 105645. https://doi.org/https://doi.org/10.1016/j.jfca.2023.105645 Gerber, N., Brogioli, R., Hattendorf, B., Scheeder, M. R., Wenk, C., & Günther, D. (2009). Variability of selected trace elements of different meat cuts determined by ICP-MS and DRC-ICPMS. Animal , 3 (1), 166-172. https://doi.org/10.1017/s1751731108003212 Gherissi, D., Lamraoui, R., Chacha, F., Moussa, C., Mohammed, T., & Gaouar, S. B. S. (2023). Slaughter performances, body composition and carcass traits of indigenous Algerian cattle "Brune de l'Atlas" . Research Square Platform LLC, 10, 21203. g/10.21203/rs.3.rs-3346136/v1 Hocquette, J. F., Lehnert, S., Barendse, W., Cassar-Malek, I., & Picard, B. (2007). Recent advances in cattle functional genomics and their application to beef quality. Animal , 1 (1), 159-173. https://doi.org/10.1017/S1751731107658042 Hou, X., Liang, R., Mao, Y., Zhang, Y., Niu, L., Wang, R., Liu, C., Liu, Y., & Luo, X. (2014). Effect of suspension method and aging time on meat quality of Chinese fattened cattle M. Longissimus dorsi. Meat Sci , 96 (1), 640-645. https://doi.org/10.1016/j.meatsci.2013.08.026 Li, P., Wang, T., Mao, Y., Zhang, Y., Niu, L., Liang, R., Zhu, L., & Luo, X. (2014). Effect of ultimate pH on postmortem myofibrillar protein degradation and meat quality characteristics of Chinese Yellow crossbreed cattle. ScientificWorldJournal , 2014 , 174253. https://doi.org/10.1155/2014/174253 Liu, J., Ellies-Oury, M.-P., Stoyanchev, T., & Hocquette, J.-F. (2022). Consumer Perception of Beef Quality and How to Control, Improve and Predict It? Focus on Eating Quality. Foods , 11 (12), 10, 3390. https://doi.org/10.3390/foods11121732 Liu, T., Wu, J. P., Lei, Z. M., Zhang, M., Gong, X. Y., Cheng, S. R., Liang, Y., & Wang, J. F. (2020). Fatty Acid Profile of Muscles from Crossbred Angus-Simmental, Wagyu-Simmental, and Chinese Simmental Cattles. Food Sci Anim Resour , 40 (4), 563-577. https://doi.org/10.5851/kosfa.2020.e33 López-Pedrouso, M., Rodríguez-Vázquez, R., Purriños, L., Oliván, M., García-Torres, S., Sentandreu, M. Á., Lorenzo, J. M., Zapata, C., & Franco, D. (2020). Sensory and Physicochemical Analysis of Meat from Bovine Breeds in Different Livestock Production Systems, Pre-Slaughter Handling Conditions, and Ageing Time. Foods , 9 (2), 2304-8158. https://www.mdpi.com/2304-8158/9/2/176#. Lorenzen, C. L., Miller, R. K., Taylors, J. F., Neely, T. R., Tatum, J. D., Wise, J. W., Buyek, M. J., Reagan, J. O., & Savell, J. W. (2003). Beef customer satisfaction: trained sensory panel ratings and Warner-Bratzler shear force values. J Anim Sci , 81 (1), 143-149. https://doi.org/10.2527/2003.811143x Lukic, M., Trbovic, D., Karan, D., Petrovic, Z., Jovanovic, J., Babic Milijasevic, J., & Nikolic, A. (2021). The nutritional and health value of beef lipids - fatty acid composition in grass-fed and grain-fed beef. IOP Conference Series: Earth and Environmental Science , 854 (1), 012054. https://doi.org/10.1088/1755-1315/854/1/012054 Mei, C., Li, S., Abbas, S. H., Tian, W., Wang, H., Li, Y., Gui, L., Zhang, Y., Wu, X., & Zan, L. (2019). Performance Measurement and Comparative Transcriptome Analysis Revealed the Efforts on Hybrid Improvement of Qinchuan Cattle. Anim Biotechnol , 30 (1), 13-20. https://doi.org/10.1080/10495398.2017.1420662 Nakamura, Y., & Kikuchi, K. (2017). Utilization of porcine in vitro-produced parthenogenetic embryos for co-transfer with vitrified and warmed embryos. Anim Sci J , 88 (12), 1925-1933. https://doi.org/10.1111/asj.12869 Papanikolopoulou, V., Tsitsos, A., Dokou, S., Priskas, S., Vouraki, S., Economou, V., Stylianaki, I., Argyriadou, A., & Arsenos, G. (2025). Impact of Breed and Slaughter Hygiene on Beef Carcass Quality Traits in Northern Greece. Foods , 14 (10), 1766. https://doi.org/10.3390/foods14101776 Praagman, J., de Jonge, E. A., Kiefte-de Jong, J. C., Beulens, J. W., Sluijs, I., Schoufour, J. D., Hofman, A., van der Schouw, Y. T., & Franco, O. H. (2016). Dietary Saturated Fatty Acids and Coronary Heart Disease Risk in a Dutch Middle-Aged and Elderly Population. Arterioscler Thromb Vasc Biol , 36 (9), 2011-2018. https://doi.org/10.1161/atvbaha.116.307578 Rodriguez, E. E., Hamblen, H., Leal-Gutierrez, J. D., Carr, C., Scheffler, T., Scheffler, J. M., & Mateescu, R. G. (2024). Exploring the impact of fatty acid composition on carcass and meat quality in Bos taurus indicus influenced cattle. J Anim Sci , 102 . https://doi.org/10.1093/jas/skae306 Sakowski, T., Grodkowski, G., Gołebiewski, M., Slósarz, J., Kostusiak, P., Solarczyk, P., & Puppel, K. (2022). Genetic and Environmental Determinants of Beef Quality-A Review. Front Vet Sci , 9 , 819605. https://doi.org/10.3389/fvets.2022.819605 Shi, M., Huang, L., Meng, S., Wang, H., Zhang, J., Miao, Z., & Li, Z. (2024). Identification of several lncRNA-mRNA pairs associated with marbling trait between Nanyang and Angus cattle. BMC Genomics , 25 (1), 696. https://doi.org/10.1186/s12864-024-10590-x Song, X., Yao, Z., Zhang, Z., Lyu, S., Chen, N., Qi, X., Liu, X., Ma, W., Wang, W., Lei, C., Jiang, Y., Wang, E., & Huang, Y. (2024). Whole-genome sequencing reveals genomic diversity and selection signatures in Xia’nan cattle. BMC Genomics , 25 (1), 559. https://doi.org/10.1186/s12864-024-10463-3 Tizioto, P. C., Gromboni, C. F., Nogueira, A. R., de Souza, M. M., Mudadu Mde, A., Tholon, P., Rosa Ado, N., Tullio, R. R., Medeiros, S. R., Nassu, R. T., & Regitano, L. C. (2014). Calcium and potassium content in beef: influences on tenderness and associations with molecular markers in Nellore cattle. Meat Sci , 96 (1), 436-440. https://doi.org/10.1016/j.meatsci.2013.08.001 Vieillevoye, S., Poortmans, J. R., & Carpentier, A. (2020). Effects of essential amino acids supplementation on muscle damage following a heavy-load eccentric training session. Science & Sports , 35 (5), e125-e134. https://doi.org/10.1016/j.scispo.2019.06.010 Wang, Y., Wang, Z., Hu, R., Peng, Q., Xue, B., & Wang, L. (2021). Comparison of carcass characteristics and meat quality between Simmental crossbred cattle, cattle-yaks and Xuanhan yellow cattle. J Sci Food Agric , 101 (9), 3927-3932. https://doi.org/10.1002/jsfa.11032 Wei, X., Zhu, Y., Zhao, X., Zhao, Y., Jing, Y., Liu, G., Wang, S., Li, H., & Ma, Y. (2022). Transcriptome profiling of mRNAs in muscle tissue of Pinan cattle and Nanyang cattle. Gene , 825 , 146435. https://doi.org/10.1016/j.gene.2022.146435 Wu, G. (2020). Important roles of dietary taurine, creatine, carnosine, anserine and 4-hydroxyproline in human nutrition and health. Amino Acids , 52 (3), 329-360. https://doi.org/10.1007/s00726-020-02823-6 Wu, J., He, X., Yun, X., Qi, M., Menghe, B., Chen, L., Han, Y., Huang, Y., Wang, M., Sha, R., & Borjigin, G. (2024). Physical and chemical properties and sensory evaluation of camel meat and new camel meat jerky. Food Sci Nutr , 12 (10), 7591-7606. https://doi.org/10.1002/fsn3.4310 Yu, H., Guo, J., Li, B., Ma, J., Abebe, B. K., Mei, C., Raza, S. H. A., Cheng, G., & Zan, L. (2025). Erucic acid promotes intramuscular fat deposition through the PPARγ-FABP4/CD36 pathway. International Journal of Biological Macromolecules , 298 , 140121. https://doi.org/https://doi.org/10.1016/j.ijbiomac.2025.140121 Zhang, T., Niu, Q., Wang, T., Zheng, X., Li, H., Gao, X., Chen, Y., Gao, H., Zhang, L., Liu, G. E., Li, J., & Xu, L. (2022). Comparative Transcriptomic Analysis Reveals Diverse Expression Pattern Underlying Fatty Acid Composition among Different Beef Cuts. Foods , 11 (1), 117. https://doi.org/10.3390/foods11010117 Zhang, T., Wang, T., Gao, Y., Sheng, J., Rushdi, H. E., Li, W., Sun, Y., Fu, T., Lin, F., Gao, T., & Liu, S. (2025). Flavor, Lipid, and Transcriptomic Profiles of Chinese Wagyu Beef Cuts: Insights into Meat Quality Differences. Foods , 14 (5), 716. https://doi.org/10.3390/foods14050716 Zhou, J., Zhao, Y., Jiang, L., Ran, J., Luo, W., Xu, H., Lei, L., Ai, R., Tan, J., & Yu, B. (2024). Characterization of biodiversity and meat quality in Guizhou yellow cattle: Correlations among intrinsic factors. Journal of Food Composition and Analysis , 132 , 106297. https://doi.org/https://doi.org/10.1016/j.jfca.2024.106297 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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10:44:22","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89702,"visible":true,"origin":"","legend":"","description":"","filename":"abf2fde20b9042159ad89165bf7119e71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8294515/v1/c84b2dd537e2732f1282e46d.xml"},{"id":98780371,"identity":"2fa9c77e-9b23-447a-8394-77ec097ed81a","added_by":"auto","created_at":"2025-12-22 12:31:16","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":98163,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8294515/v1/e671a0528df2bf264ab5f0ab.html"},{"id":98771663,"identity":"8c0de841-623a-4683-82ed-1e2aa4ac91b0","added_by":"auto","created_at":"2025-12-22 10:44:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67683,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison analysis of the relative contents for fatty acids among NYC, DNC, and PNC\u003c/strong\u003e. (A) Comparative analysis of saturated fatty acids (SFAs). (B) Comparative analysis of unsaturated fatty acids (UFAs). Each bar represents the mean ± standard deviation (SD). Statistical differences among groups were determined by one-way ANOVA followed by Tukey’s multiple comparison test. *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8294515/v1/47b8c897273c952d678ec3fc.png"},{"id":98771664,"identity":"8ef86fc0-1971-4522-bd85-8284fa9b7717","added_by":"auto","created_at":"2025-12-22 10:44:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":578843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe correlation analysis of carcass characteristics and meat quality among DNC, PNC, and NYC\u003c/strong\u003e. (A) The correlation analysis between the amino acid composition and carcass characteristics as well as meat quality. (B) The correlation analysis between the fatty acid profiles and carcass characteristics as well as meat quality. (C) The correlation analysis between the mineral elements (Ca, P, K) and carcass characteristics as well as meat quality. The color scale represents Pearson correlation coefficients (r), where red and blue denote positive and negative correlations, respectively. The size of the colored circles corresponds to the correlation strength. Significant Mantel correlations (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05) are indicated with asterisks (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8294515/v1/8f57cbfdabe61f53cbe7706b.png"},{"id":100949615,"identity":"1a29ecf9-902d-4e69-a78b-1c8644339330","added_by":"auto","created_at":"2026-01-23 07:04:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1439134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8294515/v1/b3b9e1a5-6169-4fd9-883f-933552687143.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of carcass characteristics and meat quality traits between Nanyang yellow cattle and Nanyang yellow crossbred cattle","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs a vital source of animal-derived nutrition, beef provides essential nutrients such as high-quality protein, fatty acids, vitamins, and minerals, and play an indispensable role in human health and dietary structure (Davis et al., 2022). Meat quality is shaped by a complex interplay of genetic, environmental, managerial, and health-related factors, encompassing breed, sex, slaughter age, diet, disease status, pre-slaughter handling, and postmortem aging (Papanikolopoulou et al., 2025). Among these determinants, breed is widely recognized as a decisive factor influencing critical sensory and mechanical attributes, including tenderness, juiciness, and shear force (L\u0026oacute;pez-Pedrouso et al., 2020; Sakowski et al., 2022). Previous studies have demonstrated that crossbred progeny from native and foreign breeds exhibited superior performance and economic traits compared to indigenous breeds (Engle et al., 2025). Crossbreeding with Simmental has been reported to improve carcass characteristics and increase intramuscular fat deposition in native Chinese breeds such as Xuanhan and Jinjiang yellow cattle (Wang et al., 2021). Likewise, the introduction of Angus bloodlines has been associated with favorable changes in fatty acid profiles of Chinese Simmental cattle, contributing to enhanced meat quality (Liu et al., 2020). Moreover, Angus \u0026times; Qinchuan crossbred offspring exhibit superior growth performance and carcass yield compared with Qinchuan cattle (Mei et al., 2019). Therefore, elucidating differences among crossbred progeny is essential, as crossbreeding has become an important strategy for improving production performance and meat quality.\u003c/p\u003e \u003cp\u003eNanyang cattle are known for their large body size, tender meat, and strong disease resistance (Shi et al., 2024). Despite these advantageous traits, NYC exhibit certain production limitations such as slow growth and low slaughter percentage. To address these issues, crossbreeding strategies have been implemented. For instance, Xianan cattle, derived from crossing NYC (dam) with Charolais bulls (sire), demonstrate improved growth performance and meat production compared to purebred NYC (Song et al., 2024). Similarly, Pinan cattle (PNC), a crossbred of NYC (dam) and Piedmontese cattle (sire), exhibiting the meat quality of NYC with the rapid growth and enhanced carcass characteristics of Piedmontese cattle (Shi et al., 2024). Denan cattle (DNC), developed by crossing NYC (dam) with German Yellow cattle (sire), were bred to improve overall body conformation and growth performance. Although previous studies have reported the phenotypic advantages of NYC, research on how crossbreeding with foreign breeds affects meat quality traits remains relatively limited.\u003c/p\u003e \u003cp\u003eThis study compared the carcass characteristics and meat quality between Nanyang yellow cattle and Nanyang yellow crossbred cattle. To explore associations between fatty acid profiles, amino acid composition, and meat quality traits, Mantel correlation analysis was conducted using Bray\u0026ndash;Curtis distances, while Pearson correlation analysis was applied to evaluate the links between trace element concentrations and meat quality. Overall, the findings reveal breed-specific variations and provide essential evidence to guide crossbreeding strategies for improving production efficiency and meat quality in Chinese indigenous cattle.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Animal management and sample collection\u003c/h2\u003e \u003cp\u003eA total of 16 male castrated cattle, including Nanyang cattle (NYC, n\u0026thinsp;=\u0026thinsp;4), Pinan cattle (PNC, Piedmontese♂ \u0026times; Nanyang♀, n\u0026thinsp;=\u0026thinsp;8), and Denan cattle (DNC, German Yellow♂ \u0026times; Nanyang♀, n\u0026thinsp;=\u0026thinsp;4), were maintained under uniform management and feeding conditions at a commercial fattening farm. All animals had verified pedigrees, were healthy and disease-free. Feeding followed the \u0026ldquo;Beef Cattle Feeding Standard\u0026rdquo; (NY/T 815\u0026ndash;2004). After approximately 12 months of fattening, all animals were slaughtered at Nanyang Kerqin Co., Ltd. Slaughtering procedures followed the national standard (GB/T 19477\u0026thinsp;\u0026minus;\u0026thinsp;2004). Following exsanguination, carcasses were chilled at 0\u0026ndash;4\u0026deg;C for 24 h, after which samples of the longissimus dorsi muscle were excised from the region between the 12th and 13th ribs. The collected tissues were kept at 4\u0026deg;C and equilibrated to room temperature prior to analysis. All experimental procedures involving animals complied with the guidelines for animal protection and welfare issued by the Ministry of Agriculture and Rural Affairs of China, and muscle sampling was approved by the Ethics Committee of the College of Animal Science and Technology, Henan Agricultural University (HMUD2024031224).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Carcass characteristics determination\u003c/h2\u003e \u003cp\u003eSlaughter weight was recorded after 24 h of feed withdrawal. Carcass weight was defined as the weight after removal of the head, hooves, tail, hide, internal organs, and reproductive organs. Lean meat weight was defined as the carcass weight after removal of the bones. Lean meat percentage (%) was calculated as: (Lean meat weight/Slaughter weight) \u0026times; 100%. Dressing percentage (%) was calculated as: (Carcass weight/Slaughter weight) \u0026times; 100%. Meat/Bone ratio: (Lean meat weight/Total weight of all bones in the carcass) \u0026times; 100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Meat quality characteristics\u003c/h2\u003e \u003cp\u003eThe pH value was measured using the waterproof pH meter (HI98163, Hanna Instruments, USA). The pH meter was calibrated with standard buffers (pH 4.0 and pH 7.0) before each use. The probe was inserted laterally into the sample, and measurements were taken at three random points. Each point was measured three times with readings stabilized for more than five seconds, and the average was used as the final pH value (maximum deviation\u0026thinsp;\u0026lt;\u0026thinsp;5%). Muscle strips approximately 5 cm in length, 3 cm in width, and 2 cm in height were prepared along the direction of the muscle fibers. Each strip was suspended by one end using a fine wire hook in the center of a food-grade plastic bag, which was then sealed. Samples were stored at a strictly controlled temperature of 0\u0026ndash;4\u0026deg;C for 24 h. After removal, surface moisture was gently blotted with filter paper, and the samples were immediately weighed using an electronic scale with an accuracy of 0.1 g. Drip loss (%) = (Initial meat weight \u0026ndash; Meat weight after hanging)/Initial meat weight \u0026times; 100%. Shear force was measured using a TA-XT Plus texture analyzer (Stable Micro Systems, UK). Each 100 g sample was cut into 1.27 cm diameter cores along the muscle fibers, cooked at a constant center temperature of 75\u0026deg;C for 16 min, cooled to room temperature, and then sheared perpendicular to the muscle fiber direction. The above method parameters were described in previous study (Hou et al., 2014).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Fatty acid composition\u003c/h2\u003e \u003cp\u003e Fatty acid composition quantification was performed according to the National Food Safety Standard for Determination of Fatty Acids in Foods (GB 5009.168\u0026ndash;2016). Brief, approximately 0.5 g of freeze-dried and homogenized meat into a 15 mL dry screw-thread glass tube. Sequentially add n-hexane, chloroacetyl-methanol solution, and saturated potassium carbonate solution, followed by vortex-mixing to generate fatty acid methyl esters (FAMEs). Quantify FAMEs using a GC-2014C gas chromatograph (Shimadzu Corp., Kyoto, Japan) with optimized temperature programming. Fatty acids were identified based on chromatographic peaks and quantified as relative percentages of the total fatty acids. Detailed experimental protocols followed the previous study (Zhang et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Amino acid profiles\u003c/h2\u003e \u003cp\u003eFree amino acids were quantified following the Chinese National Standard GB 5009.124\u0026ndash;2016. Briefly, homogenized beef samples were hydrolyzed with 6 mol/L HCl under nitrogen at 110\u0026deg;C for 22\u0026ndash;24 h, dried, re-dissolved in pH 2.2 citrate buffer, filtered (0.22 \u0026micro;m), and analyzed on an automatic amino acid analyzer (L-8900, Hitachi, Japan) with post-column ninhydrin detection at 570/440 nm. Calibration was performed using external standards and results were expressed as g/100 g fresh weight (Wu et al., 2024). Sixteen amino acids were quantified. Non-essential amino acids included threonine, valine, isoleucine, phenylalanine, methionine, and lysine; essential amino acids comprised aspartate, serine, glutamate, glycine, alanine, tyrosine, histidine, arginine, and proline. Additionally, glutamic acid, aspartic acid, phenylalanine, alanine, glycine, and tyrosine were identified as flavor amino acids contributing to the umami taste of the samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Nutritional composition profiles\u003c/h2\u003e \u003cp\u003eThe contents of moisture, crude protein, and crude fat were determined in accordance with the Chinese National Standards. Moisture content was measured using the distillation method according to Determination of Moisture in Food (GB 5009.3\u0026ndash;2016). Crude protein content was analyzed by the Kjeldahl method following Determination of Protein in Food (GB 5009.5\u0026ndash;2016). Crude fat content was determined using the acid hydrolysis method combined with Soxhlet extraction in accordance with Determination of Fat in Food (GB 5009.6\u0026ndash;2016). Trace elements were analyzed using an inductively coupled plasma optical emission spectrometer (ICP-OES, Agilent 5110, USA) according to the Chinese National Standard GB 5009.268\u0026ndash;2016. The detailed procedures were based on this reference (Gerber et al., 2009).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll data were analyzed using R software (version 4.3.1). Data manipulation and visualization were performed using the packages tidyverse and ggplot2. Group differences were assessed using one-way analysis of variance (One-way ANOVA) with the stats package. Tukey\u0026rsquo;s Honestly Significant Difference (HSD) post hoc tests were performed using the agricolae package. Independent samples \u003cem\u003et\u003c/em\u003e-tests for pairwise comparisons were conducted using the rstatix package. Mantel correlation analysis was performed with the vegan, linkET, dplyr, ggplot2, and RColorBrewer packages. Pearson correlations were calculated using linkET, mantel tests were applied to assess relationships among amino acid profiles, fatty acid composition, and nutrient data based on Bray\u0026ndash;Curtis distances (Version 0.0.7.4. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Hy4m/linkET\u003c/span\u003e\u003cspan address=\"https://github.com/Hy4m/linkET\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparison of the slaughter performance\u003c/h2\u003e \u003cp\u003eSlaughter performance serves as a quantitative benchmark for evaluating meat yield and guiding genetic selection (Sakowski et al., 2022). Slaughter weight, dressing percentage, lean meat percentage, and meat-to-bone ratio collectively reflect muscular development and determine the economic value of the carcass (Gherissi et al., 2023). This study evaluated the slaughter performance of NYC, DNC, and PNC, with detailed comparative data presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results demonstrated that the mean values for slaughter weights of DNC (585.75 kg) and PNC (579.25 kg) were significantly higher than that of NYC (431.25 kg) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant difference was detected between the two crossbred groups. A similar trend was observed in carcass weight, which averaged 233.67 kg, 334.47 kg, and 330.05 kg for NYC, DNC, and PNC, respectively. Furthermore, the lean meat weight was approximately 1.4 and 1.5 times higher in DNC and PNC compared to NYC, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, both crossbred groups also exhibited significantly higher dressing percentages and lean meat percentages than NYC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interestingly, the dressing percentage of PNC was slightly higher than that of DNC, with a statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Previous studies suggested that this superiority may stem from beneficial genetic traits introduced by the Piedmontese breed (Wei et al., 2022). In conclusion, the results demonstrated that both DNC and PNC possessed significant advantages in improved the carcass characteristics of NYC.\u003c/p\u003e \u003cp\u003eThe meat-to-bone ratio is a critical indicator for assessing muscle deposition in different cattle populations, reflecting both meat architecture and carcass merit, and is widely used in carcass evaluation and yield prediction (Conroy et al., 2010), In this study, the meat-to-bone ratios of DNC and PNC was 6.50 and 6.48, respectively, both significantly higher than that of NYC (4.05) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These elevated ratios suggest improved meat production efficiency and a more favorable balance between skeletal growth and muscle development. These results further demonstrated the superior muscle deposition capacity of the DNC and PNC crossbred groups compared to the NYC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of carcass characteristics among DNC, PNC and NYC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNYC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePNC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlaughter weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e431.25\u0026thinsp;\u0026plusmn;\u0026thinsp;52.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585.75\u0026thinsp;\u0026plusmn;\u0026thinsp;41.57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e579.25\u0026thinsp;\u0026plusmn;\u0026thinsp;35.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarcass weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233.67\u0026thinsp;\u0026plusmn;\u0026thinsp;27.14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334.47\u0026thinsp;\u0026plusmn;\u0026thinsp;23.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330.05\u0026thinsp;\u0026plusmn;\u0026thinsp;28.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLean meat weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187.29\u0026thinsp;\u0026plusmn;\u0026thinsp;24.46\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285.58\u0026thinsp;\u0026plusmn;\u0026thinsp;24.38\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e294.89\u0026thinsp;\u0026plusmn;\u0026thinsp;26.99\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLean meat percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDressing percentage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat/bone ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Different superscript letters within a subcolumn are significantly different at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of the meat quality\u003c/h2\u003e \u003cp\u003eBeef quality encompasses multiple dimensions, including sensory attributes, nutritional composition, potential health benefits, and processing suitability. Physicochemical parameters such as pH, water-holding capacity, shear force, and meat color are key factors influencing consumer acceptability and the overall palatability of beef (Zhou et al., 2024). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the average shear force value of NYC (3.8 kgf) was significantly lower than those of PNC (5.74 kgf) and DNC (7.73 kgf) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Previous studies have established a significant negative correlation between shear force and consumer sensory preference, with values below 4.0 kgf indicating superior tenderness (Lorenzen et al., 2003). These results demonstrate that NYC exhibits better tenderness compared to the Nanyang yellow crossbred cattle.\u003c/p\u003e \u003cp\u003eBeef water-holding capacity (WHC) is closely associated with texture and eating quality, significantly influencing consumers' perception of juiciness and overall palatability(Liu et al., 2022). In this study, WHC was assessed by measuring drip loss. The results shown that NYC exhibited lower drip loss compared to PNC and DNC, which was consistent with its superior tenderness as reflected in shear force values. Although PNC and DNC demonstrated superior slaughter performance, NYC displayed advantages in both tenderness and moisture retention. These differences may arose from intrinsic factors such as muscle fiber type composition, connective tissue properties, and metabolic characteristics specific to purebred cattle (Hocquette et al., 2007). These findings underscore the importance of conserving local breeds that possess desirable meat quality traits in crossbreeding programs.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the NYC exhibited a higher pH value of 6.60, whereas samples from DNC and PNC shown pH values ranging between 5.4 and 5.6. The elevated pH in NYC may be attributed to a slower rate of postmortem glycolysis or greater muscle glycogen reserves (Chauhan et al., 2019). A moderately high ultimate pH contributed to improved water-holding capacity and tenderness, which was supported by its lower drip loss and reduced shear force values. (Li et al., 2014). However, excessively high pH may adversely affected meat color and shelf life, as darker meat was generally less acceptable to consumers and more susceptible to microbial spoilage (Wu, 2020). In contrast, the lower pH values in PNC and DNC beef indicated a more rapid postmortem acidification, which may improve visual appearance but often at the expense of reduced tenderness and juiciness. These results underscore the multifaceted influence of pH on beef quality and emphasized the importance of balancing sensory attributes with technological properties in crossbreeding strategies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of meat quality traits among DNC, PNC and NYC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNYC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShear force (kgf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrip loss(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: The data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; a, b Differing superscript letters within the same row indicate significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between breeds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparison of amino acid composition\u003c/h2\u003e \u003cp\u003eAmino acids, as the fundamental building blocks of proteins, play vital roles in both human nutrition and animal physiology. The amino acid profile of muscle tissue is not only critical for dietary quality but also represents an economically important trait in beef cattle (Vieillevoye et al., 2020). This study measured 16 amino acids (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), including nine essential amino acids (EAAs) and seven non-essential amino acids (NEAAs). In DNC samples, the mean contents of EAA, NEAA, flavor amino acids (FAA), and total amino acids (TAA) was 7.27 g/100 g, 13.94 g/100 g, 8.88 g/100 g, and 21.21 g/100 g, respectively. The corresponding values for PNC were 7.18 g/100 g, 14.08 g/100 g, 8.77 g/100 g, and 21.26 g/100 g, with no significant differences between the two crossbred groups. Among all amino acid, glutamic acid was the most abundant amino acid, followed by proline, aspartic acid, lysine, leucine, and arginine, with no significant differences between DNC and PNC. According to FAO/WHO standards, high-quality animal protein should contain approximately 40% EAAs and 60% NEAAs of the TAA (Ge et al., 2023). The EAA/TAA ratios in the \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle of both PNC and DNC were below 40%, suggested that the nutritional quality of crossbred beef groups remained slightly inferior to that of premium protein sources.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of amino acid composition between DNC and PNC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino Acid Content (g/100g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Threonine ( Thr )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e* Isoleucine (Ile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Leucine (Leu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Valine (Val)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e* Lysine (Lys)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Methionine (Met)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Phenylalanine (Phe) ▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsparagine (Asp) ▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamine (Glu) ▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine (Gly) ▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine (Ala) ▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine (Tyr) ▲\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerine (Ser)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine (His)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine (Arg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline (Pro)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Essential amino acid (EAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Essential amino acid (NEAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelicious amino acid (DAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Amono Acid(TAA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAA/NEAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAA/TAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: \u0026ldquo; * \u0026rdquo; indicates essential amino acids, \u0026ldquo;▲\u0026rdquo; indicates flavor amino acids. Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Different superscript letters within a subcolumn are significantly different at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCertain amino acids, such as glutamic acid, aspartic acid, glycine, and alanine, are critical contributors to beef flavor (Ge et al., 2023). They impart umami and sweet tastes, and act as precursors for volatile flavor compounds (e.g., alcohols, aldehydes, and ketones) generated through Maillard reactions and thermal degradation during cooking (Dashdorj et al., 2015). This study analyzed key flavor-related amino acids, including phenylalanine, aspartic acid, glutamic acid, glycine, alanine, and tyrosine. The overall flavor amino acid profile was similar between DNC and PNC, with no statistically significant differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Composition of fatty acids\u003c/h2\u003e \u003cp\u003eFatty acid composition is a key factor influencing beef quality, directly affecting flavor, juiciness, and texture (Rodriguez et al., 2024; Zhang et al., 2025). In addition, the nutritional value of beef is closely linked to its fatty acid profile, with certain fatty acids associated with cardiovascular and metabolic health benefits (Fan et al., 2020). Therefore, optimizing fatty acid composition is essential not only for enhancing meat quality and market appeal but also for promoting consumer health. A total of 19 fatty acid compositions were detected, including nine saturated fatty acids (SFAs), six monounsaturated fatty acids (MUFAs), and four polyunsaturated fatty acids (PUFAs). Among them, SFAs accounted for the highest proportion (45.69%), followed by MUFAs (32.02%) and PUFAs (21.66%). In this study, the fatty acid profiles of the three cattle groups (DNC, PNC, and NYC) exhibited distinct differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNYC as a native breed, shown the highest total SFA content (52.44%), largely due to elevated levels of palmitic acid (C16:0) and stearic acid (C18:0), which were consistent with the previous reports (Zhang et al., 2022). Myristic acid (C14:0), (C16:0), and (C18:0) were the predominant SFAs in both the NYC and DNC groups, and the levels of these three fatty acids were significantly different compared to the PNC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the contents of capric acid (C10:0), heptadecanoic acid (C17:0), heneicosanoic acid (C21:0), and docosanoic acid (C22:0) were significantly higher in the PNC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (C16:0) is a major product of de novo lipogenesis and plays a critical role in meat tenderness and fat deposition, whereas (C17:0), (C21:0) and (C22:0) contribute to the enhancement of meat flavor (Abebe et al., 2024; Bai et al., 2024). While high SFA levels could enhance flavor, excessive intake may adversely affect cardiovascular health (Praagman et al., 2016).\u003c/p\u003e \u003cp\u003eIn contrast, MUFAs and PUFAs were known to help reduce cholesterol and triglyceride levels and support lipid metabolism (Dicks, 2024). In this study, the MUFAs relative contents in NYC, DNC, and PNC groups was 44.27%, 39.38%, and 35.83% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The longissimus dorsi muscle of the DNY exhibited the lowest levels of myristoleic acid (C14:1n5), whereas the PNC showed higher concentrations of palmitoleic acid (C16:1n7) and erucic acid (C22:1n9) compared to the other two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (C22:1n9) has been shown to promote intramuscular fat deposition in some animals, potentially by upregulating lipogenic genes or suppressing fatty acid catabolism, thus contributing to lipid accumulation (Yu et al., 2025). Notably, the PUFA level in the PNC was significantly higher than those in DNC and NYC(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This was largely attributable to elevated concentrations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of linoleic acid (C18:2n6c) and arachidonic acid (C20:4n6), both of which play vital roles in anti-inflammatory processes and maintain cellular membrane integrity (Calder, 2015). Although the high SFA content in NYC may improve flavor and consumer acceptability, its relatively low MUFA and PUFA content could limit nutritional balance (Lukic et al., 2021). These results provide some evidence that crossbreeding has the potential to improve fat deposition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Composition of micronutrients\u003c/h2\u003e \u003cp\u003eMineral content is a key indicator influencing carcass meat quality, with existing studies suggesting a potential positive correlation with beef tenderness(Tizioto et al., 2014). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the trace element profiles revealed minimal variation across breeds. A statistically significant difference was observed only in K content, which was higher in PNC (4140 mg/kg) than in DNC (3890 mg/kg) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Other elements (gallium, P, sodium, magnesium, iron, and zinc) shown no statistically significant differences. The higher potassium content in PNC, along with its higher dressing percentage, may synergistically enhance postmortem glycolysis, thereby contributing to improved meat tenderness (Ferguson \u0026amp; Gerrard, 2014).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of trace elements between DNC and PNC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGa(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.70\u0026thinsp;\u0026plusmn;\u0026thinsp;13.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.99\u0026thinsp;\u0026plusmn;\u0026thinsp;34.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.69*10\u003csup\u003e2\u003c/sup\u003e\u0026plusmn;0.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.05*10\u003csup\u003e2\u003c/sup\u003e\u0026plusmn;0.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89*10\u003csup\u003e3\u003c/sup\u003e\u0026plusmn;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98*10\u003csup\u003e3\u003c/sup\u003e\u0026plusmn;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38*10\u003csup\u003e2\u003c/sup\u003e\u0026plusmn;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46*10\u003csup\u003e2\u003c/sup\u003e\u0026plusmn;0.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89*10\u003csup\u003e3\u003c/sup\u003e\u0026plusmn;0.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14*10\u003csup\u003e3\u003c/sup\u003e\u0026plusmn;0.17\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.90\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.43\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Different superscript letters within a subcolumn are significantly different at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation analysis among traits\u003c/h2\u003e \u003cp\u003eTo elucidate the relationships between carcass and meat quality traits in crossbred cattle, correlation analyses were performed on key biochemical indicators. Mantel tests were applied to assess the associations among amino acid composition, fatty acid composition, and micronutrient profiles. Nutritional composition data, including crude protein and moisture content, were presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of essential nutrients among DNC, PNC and NYC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude protein(g/100g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntramuscular fat%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. Different superscript letters within a subcolumn are significantly different at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe results revealed significant correlations between these biochemical matrices and carcass characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, associations were observed between crude protein content and flavor amino acids, while SFAs and PUFAs exhibited significant positive correlations with muscle yield. Additionally, the K and P were significantly positively correlated with carcass weight and meat pH, which was similar to previous research (Nakamura \u0026amp; Kikuchi, 2017). In the present study, K and P were implicated in the regulation of muscle pH and may contribute to energy metabolism and protein synthesis, ultimately influencing both carcass weight and muscle development. These findings indicated that the biochemical composition of muscle tissue was intrinsically associated with meat quality attributes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, the present study offers a comprehensive database of carcass characteristics and meat quality in Nanyang yellow cattle and Nanyang yellow crossbred cattle. NYC was characterized by superior tenderness, higher water-holding capacity, and a favorable amino acid profile, whereas PNC and DNC displayed advantages in growth rate, carcass yield, specific fatty acid, and mineral traits. Further analyses indicated that amino acid, fatty acid, and micronutrient profiles were closely associated with carcass and meat quality traits. The study was designed to provide fundamental data on crossbred progeny derived from Chinese indigenous yellow cattle and to offer a scientific basis for informed crossbreeding strategies.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.L. designed the study, performed formal analysis and data curation, and wrote the original draft.J.S. conducted investigation and data curation and contributed to reviewing and editing the manuscript.L.W. contributed to investigation, visualization, and data processing.B.Z. contributed to investigation and validation.Y.G. contributed to methodology, data interpretation, and reviewing and editing.D.L. contributed to methodology, resources, and data curation.H.X. performed methodology and formal analysis.J.W. contributed to methodology and formal analysis.T.F. contributed to conceptualization, supervision, and funding acquisition.T.G. contributed to conceptualization, supervision, and project administration.Y.S. contributed to conceptualization, supervision, reviewing and editing, and funding acquisition.T.Z. contributed to conceptualization, supervision, reviewing and editing, visualization, and funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the staff at Nanyang Yellow Cattle Breeding Farm and Kerchin Cattle Industry (Nanyang) Co., Ltd. in Henan of China for caring for animals and collecting biological samples. This study was supported by the Science and Technology Innovation 2030\u0026mdash;Major Projects (Grant No. 2023ZW0404802) and the Henan Province Science and Technology Project (Grant No. 242102111002). This project was also partially supported by the National Key Research and Development Program of China (Grant No. 2024YFF1000100) and Henan Agriculture Research System (HARS-22-13-G1) for data analysis and interpretation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration:\u003c/strong\u003e This work was supported by the Science and Technology Innovation 2030—Major Projects (2023ZW0404802), Henan Province Science and Technology Project (242102111002), National Key Research and Development Program of China (2024YFF1000100) and the Henan Agriculture Research System (HARS–22-13-G1).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbebe, B. K., Wang, J., Guo, J., Wang, H., Li, A., \u0026amp; Zan, L. (2024). A review of emerging technologies, nutritional practices, and management strategies to improve intramuscular fat composition in beef cattle. \u003cem\u003eAnimal Biotechnology\u003c/em\u003e,\u003cem\u003e 35\u003c/em\u003e(1), 2388704. https://doi.org/10.1080/10495398.2024.2388704 \u003c/li\u003e\n\u003cli\u003eBai, H., Wang, L., Lambo, M. T., Li, Y., \u0026amp; Zhang, Y. (2024). Effect of changing the proportion of C16:0 and cis-9 C18:1 in fat supplements on rumen fermentation, glucose and lipid metabolism, antioxidation capacity, and visceral fatty acid profile in finishing Angus bulls. \u003cem\u003eAnimal Nutrition\u003c/em\u003e,\u003cem\u003e 18\u003c/em\u003e, 39-48. https://doi.org/https://doi.org/10.1016/j.aninu.2024.04.010 \u003c/li\u003e\n\u003cli\u003eCalder, P. C. (2015). Functional Roles of Fatty Acids and Their Effects on Human Health. \u003cem\u003eJPEN J Parenter Enteral Nutr\u003c/em\u003e,\u003cem\u003e 39\u003c/em\u003e(1 Suppl), 18s-32s. https://doi.org/10.1177/0148607115595980 \u003c/li\u003e\n\u003cli\u003eChauhan, S. S., LeMaster, M. N., Clark, D. L., Foster, M. K., Miller, C. E., \u0026amp; England, E. M. (2019). Glycolysis and pH Decline Terminate Prematurely in Oxidative Muscles despite the Presence of Excess Glycogen. \u003cem\u003eMeat and Muscle Biology\u003c/em\u003e,\u003cem\u003e 3\u003c/em\u003e(1), 254. https://doi.org/10.22175/mmb2019.02.0006 \u003c/li\u003e\n\u003cli\u003eConroy, S. B., Drennan, M. J., McGee, M., Keane, M. G., Kenny, D. A., \u0026amp; Berry, D. P. (2010). Predicting beef carcass meat, fat and bone proportions from carcass conformation and fat scores or hindquarter dissection. \u003cem\u003eAnimal\u003c/em\u003e,\u003cem\u003e 4\u003c/em\u003e(2), 234-241. https://doi.org/10.1017/s1751731109991121 \u003c/li\u003e\n\u003cli\u003eDashdorj, D., Amna, T., \u0026amp; Hwang, I. (2015). Influence of specific taste-active components on meat flavor as affected by intrinsic and extrinsic factors: an overview. \u003cem\u003eEuropean Food Research and Technology\u003c/em\u003e,\u003cem\u003e 241\u003c/em\u003e(2), 157-171. https://doi.org/10.1007/s00217-015-2449-3 \u003c/li\u003e\n\u003cli\u003eDavis, H., Magistrali, A., Butler, G., \u0026amp; Stergiadis, S. (2022). Nutritional Benefits from Fatty Acids in Organic and Grass-Fed Beef. \u003cem\u003eFoods\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(5), 646. https://doi.org/10.3390/foods11050646 \u003c/li\u003e\n\u003cli\u003eDicks, L. M. T. (2024). How important are fatty acids in human health and can they be used in treating diseases? \u003cem\u003eGut Microbes\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(1), 2420765. https://doi.org/10.1080/19490976.2024.2420765 \u003c/li\u003e\n\u003cli\u003eEngle, B. N., Thallman, R. M., Snelling, W. M., Wheeler, T. L., Shackelford, S. D., King, D. A., \u0026amp; Kuehn, L. A. (2025). Breed-specific heterosis for growth and carcass traits in 18 U.S. cattle breeds. \u003cem\u003eJournal of Animal Science\u003c/em\u003e,\u003cem\u003e 103,\u003c/em\u003e skaf048. https://doi.org/10.1093/jas/skaf048 \u003c/li\u003e\n\u003cli\u003eFan, Y., Han, Z., Arbab, A. A. I., Yang, Y., \u0026amp; Yang, Z. (2020). Effect of Aging Time on Meat Quality of Longissimus Dorsi from Yunling Cattle: A New Hybrid Beef Cattle. \u003cem\u003eAnimals (Basel)\u003c/em\u003e,\u003cem\u003e 10\u003c/em\u003e(10), 1897. https://doi.org/10.3390/ani10101897 \u003c/li\u003e\n\u003cli\u003eFerguson, D. M., \u0026amp; Gerrard, D. E. (2014). Regulation of post-mortem glycolysis in ruminant muscle. \u003cem\u003eAnimal Production Science\u003c/em\u003e,\u003cem\u003e 54\u003c/em\u003e(4), 464-481. https://doi.org/https://doi.org/10.1071/AN13088 \u003c/li\u003e\n\u003cli\u003eGe, F., Li, J., Gao, H., Wang, X., Zhang, X., Gao, H., Zhang, L., Xu, L., Gao, X., Zhu, B., Wang, Z., \u0026amp; Chen, Y. (2023). Comparative analysis of carcass traits and meat quality in indigenous Chinese cattle breeds. \u003cem\u003eJournal of Food Composition and Analysis\u003c/em\u003e,\u003cem\u003e 124\u003c/em\u003e, 105645. https://doi.org/https://doi.org/10.1016/j.jfca.2023.105645 \u003c/li\u003e\n\u003cli\u003eGerber, N., Brogioli, R., Hattendorf, B., Scheeder, M. R., Wenk, C., \u0026amp; G\u0026uuml;nther, D. (2009). Variability of selected trace elements of different meat cuts determined by ICP-MS and DRC-ICPMS. \u003cem\u003eAnimal\u003c/em\u003e,\u003cem\u003e 3\u003c/em\u003e(1), 166-172. https://doi.org/10.1017/s1751731108003212 \u003c/li\u003e\n\u003cli\u003eGherissi, D., Lamraoui, R., Chacha, F., Moussa, C., Mohammed, T., \u0026amp; Gaouar, S. B. S. (2023). \u003cem\u003eSlaughter performances, body composition and carcass traits of indigenous Algerian cattle \u0026quot;Brune de l\u0026apos;Atlas\u0026quot;\u003c/em\u003e. Research Square Platform LLC, 10, 21203. g/10.21203/rs.3.rs-3346136/v1 \u003c/li\u003e\n\u003cli\u003eHocquette, J. F., Lehnert, S., Barendse, W., Cassar-Malek, I., \u0026amp; Picard, B. (2007). Recent advances in cattle functional genomics and their application to beef quality. \u003cem\u003eAnimal\u003c/em\u003e,\u003cem\u003e 1\u003c/em\u003e(1), 159-173. https://doi.org/10.1017/S1751731107658042 \u003c/li\u003e\n\u003cli\u003eHou, X., Liang, R., Mao, Y., Zhang, Y., Niu, L., Wang, R., Liu, C., Liu, Y., \u0026amp; Luo, X. (2014). Effect of suspension method and aging time on meat quality of Chinese fattened cattle M. Longissimus dorsi. \u003cem\u003eMeat Sci\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e(1), 640-645. https://doi.org/10.1016/j.meatsci.2013.08.026 \u003c/li\u003e\n\u003cli\u003eLi, P., Wang, T., Mao, Y., Zhang, Y., Niu, L., Liang, R., Zhu, L., \u0026amp; Luo, X. (2014). Effect of ultimate pH on postmortem myofibrillar protein degradation and meat quality characteristics of Chinese Yellow crossbreed cattle. \u003cem\u003eScientificWorldJournal\u003c/em\u003e,\u003cem\u003e 2014\u003c/em\u003e, 174253. https://doi.org/10.1155/2014/174253 \u003c/li\u003e\n\u003cli\u003eLiu, J., Ellies-Oury, M.-P., Stoyanchev, T., \u0026amp; Hocquette, J.-F. (2022). Consumer Perception of Beef Quality and How to Control, Improve and Predict It? Focus on Eating Quality. \u003cem\u003eFoods\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(12), 10, 3390. https://doi.org/10.3390/foods11121732 \u003c/li\u003e\n\u003cli\u003eLiu, T., Wu, J. P., Lei, Z. M., Zhang, M., Gong, X. Y., Cheng, S. R., Liang, Y., \u0026amp; Wang, J. F. (2020). Fatty Acid Profile of Muscles from Crossbred Angus-Simmental, Wagyu-Simmental, and Chinese Simmental Cattles. \u003cem\u003eFood Sci Anim Resour\u003c/em\u003e,\u003cem\u003e 40\u003c/em\u003e(4), 563-577. https://doi.org/10.5851/kosfa.2020.e33 \u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Pedrouso, M., Rodr\u0026iacute;guez-V\u0026aacute;zquez, R., Purri\u0026ntilde;os, L., Oliv\u0026aacute;n, M., Garc\u0026iacute;a-Torres, S., Sentandreu, M. \u0026Aacute;., Lorenzo, J. M., Zapata, C., \u0026amp; Franco, D. (2020). Sensory and Physicochemical Analysis of Meat from Bovine Breeds in Different Livestock Production Systems, Pre-Slaughter Handling Conditions, and Ageing Time. \u003cem\u003eFoods\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(2), 2304-8158. https://www.mdpi.com/2304-8158/9/2/176#. \u003c/li\u003e\n\u003cli\u003eLorenzen, C. L., Miller, R. K., Taylors, J. F., Neely, T. R., Tatum, J. D., Wise, J. W., Buyek, M. J., Reagan, J. O., \u0026amp; Savell, J. W. (2003). Beef customer satisfaction: trained sensory panel ratings and Warner-Bratzler shear force values. \u003cem\u003eJ Anim Sci\u003c/em\u003e,\u003cem\u003e 81\u003c/em\u003e(1), 143-149. https://doi.org/10.2527/2003.811143x \u003c/li\u003e\n\u003cli\u003eLukic, M., Trbovic, D., Karan, D., Petrovic, Z., Jovanovic, J., Babic Milijasevic, J., \u0026amp; Nikolic, A. (2021). The nutritional and health value of beef lipids - fatty acid composition in grass-fed and grain-fed beef. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e,\u003cem\u003e 854\u003c/em\u003e(1), 012054. https://doi.org/10.1088/1755-1315/854/1/012054 \u003c/li\u003e\n\u003cli\u003eMei, C., Li, S., Abbas, S. H., Tian, W., Wang, H., Li, Y., Gui, L., Zhang, Y., Wu, X., \u0026amp; Zan, L. (2019). Performance Measurement and Comparative Transcriptome Analysis Revealed the Efforts on Hybrid Improvement of Qinchuan Cattle. \u003cem\u003eAnim Biotechnol\u003c/em\u003e,\u003cem\u003e 30\u003c/em\u003e(1), 13-20. https://doi.org/10.1080/10495398.2017.1420662 \u003c/li\u003e\n\u003cli\u003eNakamura, Y., \u0026amp; Kikuchi, K. (2017). Utilization of porcine in vitro-produced parthenogenetic embryos for co-transfer with vitrified and warmed embryos. \u003cem\u003eAnim Sci J\u003c/em\u003e,\u003cem\u003e 88\u003c/em\u003e(12), 1925-1933. https://doi.org/10.1111/asj.12869 \u003c/li\u003e\n\u003cli\u003ePapanikolopoulou, V., Tsitsos, A., Dokou, S., Priskas, S., Vouraki, S., Economou, V., Stylianaki, I., Argyriadou, A., \u0026amp; Arsenos, G. (2025). Impact of Breed and Slaughter Hygiene on Beef Carcass Quality Traits in Northern Greece. \u003cem\u003eFoods\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(10), 1766. https://doi.org/10.3390/foods14101776\u003c/li\u003e\n\u003cli\u003ePraagman, J., de Jonge, E. A., Kiefte-de Jong, J. C., Beulens, J. W., Sluijs, I., Schoufour, J. D., Hofman, A., van der Schouw, Y. T., \u0026amp; Franco, O. H. (2016). Dietary Saturated Fatty Acids and Coronary Heart Disease Risk in a Dutch Middle-Aged and Elderly Population. \u003cem\u003eArterioscler Thromb Vasc Biol\u003c/em\u003e,\u003cem\u003e 36\u003c/em\u003e(9), 2011-2018. https://doi.org/10.1161/atvbaha.116.307578 \u003c/li\u003e\n\u003cli\u003eRodriguez, E. E., Hamblen, H., Leal-Gutierrez, J. D., Carr, C., Scheffler, T., Scheffler, J. M., \u0026amp; Mateescu, R. G. (2024). Exploring the impact of fatty acid composition on carcass and meat quality in Bos taurus indicus influenced cattle. \u003cem\u003eJ Anim Sci\u003c/em\u003e,\u003cem\u003e 102\u003c/em\u003e. https://doi.org/10.1093/jas/skae306 \u003c/li\u003e\n\u003cli\u003eSakowski, T., Grodkowski, G., Gołebiewski, M., Sl\u0026oacute;sarz, J., Kostusiak, P., Solarczyk, P., \u0026amp; Puppel, K. (2022). Genetic and Environmental Determinants of Beef Quality-A Review. \u003cem\u003eFront Vet Sci\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e, 819605. https://doi.org/10.3389/fvets.2022.819605 \u003c/li\u003e\n\u003cli\u003eShi, M., Huang, L., Meng, S., Wang, H., Zhang, J., Miao, Z., \u0026amp; Li, Z. (2024). Identification of several lncRNA-mRNA pairs associated with marbling trait between Nanyang and Angus cattle. \u003cem\u003eBMC Genomics\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(1), 696. https://doi.org/10.1186/s12864-024-10590-x \u003c/li\u003e\n\u003cli\u003eSong, X., Yao, Z., Zhang, Z., Lyu, S., Chen, N., Qi, X., Liu, X., Ma, W., Wang, W., Lei, C., Jiang, Y., Wang, E., \u0026amp; Huang, Y. (2024). Whole-genome sequencing reveals genomic diversity and selection signatures in Xia\u0026rsquo;nan cattle. \u003cem\u003eBMC Genomics\u003c/em\u003e,\u003cem\u003e 25\u003c/em\u003e(1), 559. https://doi.org/10.1186/s12864-024-10463-3 \u003c/li\u003e\n\u003cli\u003eTizioto, P. C., Gromboni, C. F., Nogueira, A. R., de Souza, M. M., Mudadu Mde, A., Tholon, P., Rosa Ado, N., Tullio, R. R., Medeiros, S. R., Nassu, R. T., \u0026amp; Regitano, L. C. (2014). Calcium and potassium content in beef: influences on tenderness and associations with molecular markers in Nellore cattle. \u003cem\u003eMeat Sci\u003c/em\u003e,\u003cem\u003e 96\u003c/em\u003e(1), 436-440. https://doi.org/10.1016/j.meatsci.2013.08.001 \u003c/li\u003e\n\u003cli\u003eVieillevoye, S., Poortmans, J. R., \u0026amp; Carpentier, A. (2020). Effects of essential amino acids supplementation on muscle damage following a heavy-load eccentric training session. \u003cem\u003eScience \u0026amp; Sports\u003c/em\u003e,\u003cem\u003e 35\u003c/em\u003e(5), e125-e134. https://doi.org/10.1016/j.scispo.2019.06.010\u003c/li\u003e\n\u003cli\u003eWang, Y., Wang, Z., Hu, R., Peng, Q., Xue, B., \u0026amp; Wang, L. (2021). Comparison of carcass characteristics and meat quality between Simmental crossbred cattle, cattle-yaks and Xuanhan yellow cattle. \u003cem\u003eJ Sci Food Agric\u003c/em\u003e,\u003cem\u003e 101\u003c/em\u003e(9), 3927-3932. https://doi.org/10.1002/jsfa.11032 \u003c/li\u003e\n\u003cli\u003eWei, X., Zhu, Y., Zhao, X., Zhao, Y., Jing, Y., Liu, G., Wang, S., Li, H., \u0026amp; Ma, Y. (2022). Transcriptome profiling of mRNAs in muscle tissue of Pinan cattle and Nanyang cattle. \u003cem\u003eGene\u003c/em\u003e,\u003cem\u003e 825\u003c/em\u003e, 146435. https://doi.org/10.1016/j.gene.2022.146435 \u003c/li\u003e\n\u003cli\u003eWu, G. (2020). Important roles of dietary taurine, creatine, carnosine, anserine and 4-hydroxyproline in human nutrition and health. \u003cem\u003eAmino Acids\u003c/em\u003e,\u003cem\u003e 52\u003c/em\u003e(3), 329-360. https://doi.org/10.1007/s00726-020-02823-6 \u003c/li\u003e\n\u003cli\u003eWu, J., He, X., Yun, X., Qi, M., Menghe, B., Chen, L., Han, Y., Huang, Y., Wang, M., Sha, R., \u0026amp; Borjigin, G. (2024). Physical and chemical properties and sensory evaluation of camel meat and new camel meat jerky. \u003cem\u003eFood Sci Nutr\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(10), 7591-7606. https://doi.org/10.1002/fsn3.4310 \u003c/li\u003e\n\u003cli\u003eYu, H., Guo, J., Li, B., Ma, J., Abebe, B. K., Mei, C., Raza, S. H. A., Cheng, G., \u0026amp; Zan, L. (2025). Erucic acid promotes intramuscular fat deposition through the PPAR\u0026gamma;-FABP4/CD36 pathway. \u003cem\u003eInternational Journal of Biological Macromolecules\u003c/em\u003e,\u003cem\u003e 298\u003c/em\u003e, 140121. https://doi.org/https://doi.org/10.1016/j.ijbiomac.2025.140121 \u003c/li\u003e\n\u003cli\u003eZhang, T., Niu, Q., Wang, T., Zheng, X., Li, H., Gao, X., Chen, Y., Gao, H., Zhang, L., Liu, G. E., Li, J., \u0026amp; Xu, L. (2022). Comparative Transcriptomic Analysis Reveals Diverse Expression Pattern Underlying Fatty Acid Composition among Different Beef Cuts. \u003cem\u003eFoods\u003c/em\u003e,\u003cem\u003e 11\u003c/em\u003e(1), 117. https://doi.org/10.3390/foods11010117 \u003c/li\u003e\n\u003cli\u003eZhang, T., Wang, T., Gao, Y., Sheng, J., Rushdi, H. E., Li, W., Sun, Y., Fu, T., Lin, F., Gao, T., \u0026amp; Liu, S. (2025). Flavor, Lipid, and Transcriptomic Profiles of Chinese Wagyu Beef Cuts: Insights into Meat Quality Differences. \u003cem\u003eFoods\u003c/em\u003e,\u003cem\u003e 14\u003c/em\u003e(5), 716. https://doi.org/10.3390/foods14050716 \u003c/li\u003e\n\u003cli\u003eZhou, J., Zhao, Y., Jiang, L., Ran, J., Luo, W., Xu, H., Lei, L., Ai, R., Tan, J., \u0026amp; Yu, B. (2024). Characterization of biodiversity and meat quality in Guizhou yellow cattle: Correlations among intrinsic factors. \u003cem\u003eJournal of Food Composition and Analysis\u003c/em\u003e,\u003cem\u003e 132\u003c/em\u003e, 106297. https://doi.org/https://doi.org/10.1016/j.jfca.2024.106297 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nanyang yellow cattle, Crossbreeding, Carcass characteristics, Meat quality, Fatty acid composition","lastPublishedDoi":"10.21203/rs.3.rs-8294515/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8294515/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study systematically evaluated the carcass characteristics and meat quality of the Nanyang yellow cattle (NYC), Pinan cattle (Piedmontese × NYC; PNC) and Denan cattle (German yellow ×NYC; DNC). DNC and PNC exhibited significantly higher carcass weight, lean meat percentage, dressing percentage, and meat-to-bone ratio, while NYC demonstrated superior tenderness and water-holding capacity. NYC had higher saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA), whereas PNC contained more polyunsaturated fatty acids (PUFA) and potassium (K) content. The Mantel correlation analysis demonstrated that essential amino acids (EAA) and flavor amino acids (FAA) levels were closely associated with crude protein content. Furthermore, SFA, PUFA and MUFA exhibited significant relationships with lean meat yield, whereas K and phosphorus (P) were correlated with carcass weight and meat pH. Collectively, Nanyang yellow crossbred cattle could enhance production performance and slaughter performance, providing valuable insights for conservation and genetic improvement of Chinese indigenous cattle.\u003c/p\u003e","manuscriptTitle":"Comparison of carcass characteristics and meat quality traits between Nanyang yellow cattle and Nanyang yellow crossbred cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:44:17","doi":"10.21203/rs.3.rs-8294515/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"00a8f05a-1dd8-4bc4-92dc-a6a40deda2dd","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T14:13:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:44:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8294515","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8294515","identity":"rs-8294515","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}