Objective: This study investigated the effects of extruded flaxseed with and without herbs mixture on egg performance, yolk fatty acids (FAs), lipid components, blood biochemistry, serological enzymes, antioxidants, and immune system of Hy-Line W-36 hens for nine weeks.Methods: Two hundred forty laying hens were randomly distributed to eight treatments, resulting in six replicates with five hens. Graded levels of dietary extruded flaxseed (0, 90, 180, and 270 g/kg) with and without herbs mixture (24 g/kg: garlic, ginger, green tea, and turmeric 6 g/kg each) were designed as treatments.Results: The two-way analysis of variance indicated that hens fed herbs mixture had a higher value of egg production, yolk high-density lipoprotein (HDL), superoxide dismutase, glutathione peroxidase, and white blood cell and lower contents of yolk cholesterol, glucose, and blood low-density lipoprotein than those fed diets without herb mixtures (p<0.05). The Flx27 (270 g/kg flaxseed) (153.5 g/kg n-3 FAs) and Flx27+H (270 g/kg flaxseed plus 24 g/kg herbs mixture) (150.5 g/kg n-3 FAs) groups were the most promising treatments in terms of yolk n-3 FAs content. In-teraction effect (herbs- flaxseed) for blood cholesterol, HDL, malondialdehyde, glutaredoxin, alanine transaminase, (ALT), aspartate transaminase (AST), haemoglobin and immune parameters was significant (p<0.05). The results showed layers fed herbs mixture (Flx9+H, Flx18+H, and Flx27+H) had a better value of total antibody, immunoglobulin M, immunoglobulin G, ALT, AST, and blood HDL as compared with representative flaxseed levels without herbs.Conclusion: High inclusion levels of extruded flaxseed (270 g/kg) without herbs to enrich eggs with n-3 appears to impair the antioxidant system, immunohematological parameters, and sero-logical enzymes. Interestingly, the herbs mixture supplementation corrected those effects. Therefore, feeding layers with flaxseed-rich diets (270 g/kg) and herbs mixture can be a promising strategy to enrich eggs with n-3 FAs.

Walnuts are commercially important tree nuts whose moisture content (MC) influences quality, shelf life, and post-harvest processing. This study evaluated the potential of low-cost acoustic sensing combined with machine learning for non-destructive MC prediction. Sixty in-shell walnuts were subjected to controlled drying at 40 °C for 26 h, with acoustic recordings and physical measurements collected every two hours. Acoustic signals were processed using Wavelet Soft Threshold Denoising (WSTD), Short-Time Fourier Transform (STFT), and Variational Mode Decomposition (VMD), and features were extracted from the resulting signals. Predictive models included generalized linear models (GLM), random forests (RF), gradient boosting machines (GBM), and Partial Least Squares (PLS) approaches. Following grouped walnut-level validation, the highest MC prediction performance was achieved by the model combining dimensional and acoustic descriptors (RF: R² = 0.836; GBM: R² = 0.826), while the model combining drying time and acoustic descriptors achieved moderate predictive performance (RF: R² = 0.762; GBM: R² = 0.760). Overall, the results provide proof-of-concept evidence that acoustic descriptors may complement physical measurements for non-destructive walnut moisture-content prediction. However, substantially larger independent datasets collected across multiple cultivars, production batches, acquisition conditions, and external validation studies will be required before practical industrial implementation can be considered.