Xiaoai Wang

Review Open access Sep 2026

Advances in Solid-State Fermentation Technology for Oilseed Meal: Strain Selection, Fermentation Strategies, and High-Value Applications

Oilseed meal, the primary by-product of oil extraction, is rich in protein, dietary fiber, and minerals, offering significant development potential. However, its application in high-value feed and food is severely restricted due to anti-nutritional factors, leading to resource waste and environmental issues. Solid-state fermentation (SSF) provides a green and efficient approach for the high-value utilization of oilseed meal. This review comprehensively discusses the entire process of strain selection, fermentation strategies, and application of active products in the SSF of oilseed meal. Regarding strain selection, Bacillus spp. degrade macromolecular proteins and inhibit microbial contamination through protease and antimicrobial peptide production. Lactobacillus spp. enhance flavor and safety by producing acids and flavor compounds. Aspergillus spp. decompose cell walls and degrade phytate using their cellulase and phytase systems. For fermentation strategies, mixed fermentation achieves functional complementarity, enzyme–fungus synergy enhances substrate conversion, segmented fermentation optimizes the microbial environment, and physical field assistance boosts enzyme activity, collectively improving fermentation efficiency and nutritional quality. In product applications, fermented oilseed meal serves as both high-quality protein feed and a source of functional peptides and active polysaccharides with antioxidant and immunomodulatory activities, showing potential for functional foods and biomedicine. In conclusion, SSF technology effectively degrades anti-nutritional factors, improving the nutritional value and adding value to oilseed meal, thus representing a key strategy for resource conversion. Future efforts should prioritize high-performance strain selection, intelligent process monitoring, and green preparation of active products to promote industrial application and sustainable development.

Jing Wei, Chen-Cheng Yao, Musfira Akram et al. · Foods · 0 citations

Open access Jul 2026

Hear the Sweet Spot: Tennis Impact Localization via Single-Channel Audio

Identifying the impact location (“sweet spot”) on a tennis racket is crucial for performance evaluation in tennis training. However, existing approaches typically rely on expensive vision-based systems or specialized sensors, limiting their applicability in real-world scenarios. We propose a sound-sensor-based multi-task framework for racket impact localization using acoustic signals, combining radial region classification with continuous position regression. To effectively model complex acoustic patterns, we design a multi-expert convolutional neural network (CNN) architecture with multi-scale feature extraction and task-specific optimization. Each expert branch operates at a different temporal receptive field and is trained with tailored loss functions, enabling complementary learning of global patterns, class imbalance characteristics, and hard samples. The shared backbone jointly supports both classification and regression tasks, allowing the model to learn more informative and structured representations. Experimental results demonstrate that the proposed framework consistently outperforms conventional methods in radial region classification while achieving accurate impact position estimation. Furthermore, additive noise augmentation significantly improves robustness, enabling stable performance under noisy and practical sensing conditions.

Shaochi Zhang, Xiaoai Wang, Xuan Chang et al. · Applied Sciences · 0 citations