From Quality Detection to Intelligent Management: A Mechanism-Informed Framework Integrating Foodomics, Microbiome Science and Explainable Artificial Intelligence for Fish Flesh Quality

Fish flesh quality is a multidimensional phenotype shaped across the production–post-harvest continuum by interacting metabolic, structural, lipid, redox, and microbial processes. Conventional sensory, physicochemical, biochemical, and instrumental measurements remain essential for defining quality phenotypes, while foodomics and microbiome approaches increasingly resolve the biological mechanisms underlying quality formation and deterioration. This review integrates recent advances in fish flesh quality assessment, foodomics, microbiome science, intelligent sensing, machine learning, and explainable artificial intelligence from a mechanism-informed perspective. Particular emphasis is placed on energy metabolism, lipid remodeling, redox regulation, muscle-fiber and collagen organization, microbial metabolites, and spoilage-related microbial functions that connect biological state with measurable quality traits. We further examine the transition from current-state recognition toward prospective quality prediction, with emphasis on biological interpretability, transferability, external validation, and uncertainty. Building on this evidence, we propose a dual-stage framework in which a context-dependent harvest biological-state vector integrates energetic, structural, lipid/nutritional, redox, and product-associated microbial dimensions to connect pre-harvest quality formation with post-harvest deterioration trajectories. Together, these advances provide a basis for moving from quality detection toward prospective prediction and mechanism-informed intelligent management of fish flesh quality.