Meat Research

Dentition-based age influences physicochemical, nutritional and sensory quality of cooked Sahiwal crossbred cattle hide

MM Rahman — 2026-02-28

Cattle hide is a collagen-rich slaughter co-product with potential for food use, yet the effect of animal maturity on its quality is poorly characterized. This study evaluated cooked neck-hide from male Sahiwal crossbred cattle classified by dentition as T1 (2-tooth; one pair of permanent incisors; n=12) or T2 (4-tooth; two pairs; n=12). Physicochemical properties, proximate composition, calculated energy value, and trained-panel sensory attributes were determined using standardized laboratory procedures. Raw sample-level data were analyzed by one-way ANOVA, and selected instrumental-sensory relationships were examined using Pearson correlation. Compared with T1, T2 hide had higher pH (7.646 vs 6.768), drip loss (12.163 vs 8.354%), cooking loss (15.992 vs 4.589%), and shear force (258.481 vs 154.627 N; P<0.001), while CIE a* and b* did not differ. T1 contained more crude protein (33.465 vs 29.528%) and dry matter (36.193 vs 34.184%), whereas T2 contained more moisture, ether extract, ash, and carbohydrate. All trained-panel scores favored T1, including texture (4.275 vs 2.542), juiciness (4.467 vs 2.900), and overall acceptability (4.042 vs 2.683; P<0.001). Shear force was strongly inversely related to texture within both T1 (r=-0.921) and T2 (r=-0.947), and cooking loss was inversely related to juiciness (r=-0.939 and -0.988, respectively). These findings demonstrate that dentition-based maturity is strongly associated with the processing and eating quality of cooked cattle hide. Younger hide showed the more favorable profile for direct culinary use, although collagen chemistry, digestibility, microbiological safety, and broader consumer acceptance require further investigation before nutritional or commercial recommendations are made.

Separating prediction from maturity in cooked cattle hide: confound-partitioned machine learning reveals a bounded thermal-lability axis

MM Rahman — 2026-02-28

Small food-quality datasets can yield high machine-learning coefficients when a discrete biological factor separates samples, even if individual-level prediction is weak. Using cooked cattle hide as a collagen-dominated test system, we evaluated whether instrumental models predict trained-panel sensory quality beyond dentition class and whether predictive relationships persist across maturity domains. Neck hides from 24 male Sahiwal-crossbred cattle (two-tooth, n=12; four-tooth, n=12) was processed identically and characterized for pH, CIE L*a*b*, drip and cooking loss, Warner-Bratzler shear force, proximate composition and five sensory attributes. A seven-rung model ladder was evaluated by nested leave-one-animal-out cross-validation against intercept-only and dentition group-mean benchmarks. PC1 explained 78.70% of variance and the five core predictors were strongly collinear (VIF 11.15-43.03). Best cross-validated R² values were 0.980 for juiciness, 0.976 for texture and overall acceptability, 0.962 for color and 0.772 for flavor. For PLSR, fold-safe class centering reduced R² by only 5-27% (Φ=0.05-0.27), but every cross-class transfer had negative R². Exact retraining Shapley attribution across four physical blocks was nearly uniform (22.3-27.6%), precluding a unique instrumental driver. Cooking loss and shear force were correlated in younger hide (r=0.905) but not mature hide (r=-0.146); the correlations differed significantly (Fisher Z=3.49, P=0.00048). Thus, sensory prediction is substantial within the observed maturity structure but is not transportable across it, and the proposed thermal-lability axis is explicitly maturity-bounded.

Near-infrared and fourier-transform infrared spectroscopy for meat quality and safety assessment: A review

FR Shebli — 2026-02-28

Meat quality and safety assessment demands analytical tools that are rapid, nondestructive, and capable of resolving a broad spectrum of physicochemical and microbiological parameters simultaneously. Near-infrared (NIR) and Fourier-transform infrared (FTIR) spectroscopy have emerged as two of the most promising technologies in this regard, offering complementary analytical capabilities rooted in distinct physical mechanisms of light-matter interaction. This review systematically examines the application of NIR and FTIR spectroscopy across the full scope of meat quality and safety evaluation, beginning with their contrasting physical and chemical principles as applied to muscle matrices, followed by a comprehensive mapping of spectral absorption bands to individual quality parameters including moisture, protein, fat, lipid oxidation, water-holding capacity, color, tenderness, and pH. Their utility in meat safety and authenticity assessment, encompassing microbial spoilage monitoring, species adulteration detection, exogenous adulterant identification, and halal authentication, is critically evaluated. Key methodological challenges including sample heterogeneity, water interference, calibration transferability, and model interpretability are discussed. Future directions emphasizing portable instrumentation, hyperspectral imaging, artificial intelligence integration, and open-access spectral databases are proposed. The evidence collectively supports a complementary dual-spectroscopic framework as the most comprehensive and practically deployable strategy for nondestructive meat quality governance across the supply chain.