A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R²) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil.

Quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5 year old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight and dressing percentage were greater in the feedlot than grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with a 100% discrimination in the 400-2500 nm band. The spectral pre-processing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.

The traditional grazing system of the Qinghai-Tibet Plateau faces challenges such as feed shortages, despite the abundance of pastures during the warm season. However, the impact of supplemental feeding on yak meat quality during this period still requires further investigation. A total of 30 male yaks (with similar genetic backgrounds, aged 2.5–3 years, weighing 94.56 ± 3.9 kg) were evenly and randomly assigned to two groups: the traditional grazing group (G) and the supplemental feeding group (SF). This study evaluated the effects of supplementation on yak meat quality and metabolic characteristics, aiming to identify effective dietary strategies to improve the physical and nutritional quality of yak meat. Non-targeted metabolomics (UHPLC-QE-MS) was used to analyze biomarkers of meat quality. Results revealed that the SF group exhibited superior meat quality, with a 39.6% reduction in shear strength, a 22.4% reduction in cooking loss, a 15% increase in PUFA/SFA ratios, and an 18% increase in essential amino acid content. Metabolomic profiling indicated distinct differences between the two groups, with the SF group demonstrating significant upregulation of beneficial metabolites (e.g., pyruvic acid, L-tyrosine, and eicosapentaenoic acid) and downregulation of harmful metabolites (e.g., sulfates). These changes improved protein turnover, lipid metabolism, and glycolytic activity, enhancing meat tenderness, flavor, and nutritional value. This study provides novel insights into the metabolic mechanisms underlying feed-induced quality changes, highlighting the practical value of supplemental feeding in overcoming the limitations of traditional grazing systems and reducing ecological pressure on grasslands.

Near‐infrared spectroscopy (NIRS), which leverages molecular absorption of infrared light, possesses strong “fingerprint” identification capabilities. It has become an indispensable tool in compound identification, molecular structure analysis, and quantitative analysis. In recent years, NIRS, combined with chemometrics, has shown tremendous potential for rapid, nondestructive detection in complex systems. In this study, near‐infrared spectra of 48 yak meat samples with varying adulteration levels (400–2500 nm) were collected. After dividing the samples by concentration gradient, six multivariate calibration methods were compared: principal component regression (PCR), partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), support vector regression (SVR), backpropagation artificial neural network (BP‐ANN), and extreme learning machine (ELM). The PLS model was chosen as the base model. Furthermore, 14 spectral preprocessing methods were evaluated, including Savitzky–Golay smoothing, standard normal variate (SNV), multiplicative scatter correction (MSC), derivative spectra, and continuous wavelet transform (CWT). The CWT method was found to provide the best results. On this basis, seven variable selection algorithms, including uninformative variable elimination (UVE), Monte Carlo UVE (MCUVE), randomization test (RT), competitive adaptive reweighted sampling (CARS), whale optimization algorithm (WOA), gray wolf optimizer (GWO), and butterfly optimization algorithm (BOA), were applied. The WOA algorithm significantly improved model performance, selecting 11 and 21 key features for yak meat and beef content predictions, respectively. The final model achieved RMSEP values of 4.8607 (Rp = 0.9157) for yak meat and 3.8066 (Rp = 0.9486) for beef content. This study established the optimal modeling strategy for quantitative analysis of yak meat adulteration.