Selenium, a crucial trace element, has garnered significant attention in functional food development due to its effective conversion into organic forms. This study systematically investigated the selenium enrichment potential and metabolic regulation mechanisms of 50 lactic acid bacteria (LAB) strains from Xinjiang. Through sodium selenite tolerance tests, eight core strains with over 80% selenium enrichment were selected, with optimal enrichment conditions being a 37 °C temperature, 2% sodium chloride concentration, and pH of 6.0 in MRS medium. Functional tests demonstrated that selenium-enriched strains exhibited a significantly enhanced antioxidant capacity (demonstrated by DPPH and ABTS free radical scavenging activities) and improved gastrointestinal fluid tolerance, with strain No.41 showing the most outstanding performance. Scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM-EDX) revealed nanoscale selenium (1.34 keV) on cell surfaces. Further characterization showed that 68.94% of selenium was incorporated into selenoproteins, 7.61% into nucleic acids, and 7.02% into polysaccharides. Integrated metabolomic and proteomic studies have shown that selenium reduces the content of L-cysteine primarily by replacing sulfur and competing for key sites in cysteine-S-conjugate-β-lyase, S-adenosyl-L-cysteine hydrolase, and homocysteine synthase, ultimately leading to the synthesis of selenocysteine and selenomethionine. A correlation analysis between differential metabolites and proteins revealed selenium’s significant impacts on the metabolic networks of LAB, antioxidant mechanisms, energy metabolism, and membrane stability. This research provides new insights for developing selenium-enriched probiotics for functional dairy products and health supplements.

The aquatic products industry has experienced rapid development due to the growing global demand for high-quality proteins. However, conventional production models remain constrained by multifaceted impediments, including disease outbreaks, environmental stressors, microbial contamination, and operational inefficiencies in processing, quality control, and logistics. Artificial intelligence (AI) offers targeted methodological solutions to these challenges. From a functional perspective, this review categorizes artificial intelligence into four major types: perception, prediction, control, and generation, and systematically evaluates its application progress in aquaculture, processing, quality inspection, and traceability fields. Perception AI constitutes the data acquisition and digitization layer, utilizing computer vision, sonar, and multimodal fusion technologies to establish digital mappings from environmental parameters to biological indicators. Prediction AI employs machine learning and deep learning algorithms to transform historical datasets into quantitative forecasts regarding water quality dynamics, disease risks, and production trends. Control AI translates decision-making protocols into precise, autonomous regulatory actions for aquaculture environments and processing workflows through fuzzy logic and model-based predictive control. Generation AI leverages large language models and generative adversarial networks to demonstrate innovative capabilities in data augmentation, solution optimization, and virtual simulation. Collectively, these applications optimize core production processes while significantly enhancing product quality, processing efficiency, safety management, and traceability systems. Future research directions will prioritize the development of robust, interdisciplinary AI technologies with superior integration capabilities.