Mechanism-Driven Green Extraction of Plant Polyphenols: From Molecular Interactions to Process Integration and Intelligent Optimization.

Plant polyphenols are valuable secondary metabolites with significant bioactivities; however, their efficient extraction faces multiple challenges, including the structural complexity arising from their coexistence in free and bound forms within plant matrices, as well as their sensitivity to oxidation and heat. Although emerging green extraction technologies such as deep eutectic solvents, supercritical fluid extraction, and physical field enhancement show potential, current research largely remains method-oriented, lacking an in-depth understanding of the coupling mechanisms between molecular interactions and mass transfer processes. This review explicitly proposes a "mechanism-driven, synergistic integration" framework for the green extraction of plant polyphenols. By systematically analyzing the molecular basis of extractability and the complementarity among emerging technologies, this framework provides theoretical guidance and a practical blueprint for transitioning from empirical optimization to intelligent, synergistic system design. Specifically, it begins by systematically dissecting the structural characteristics of polyphenols and their interactions with cell wall components to clarify the molecular basis of extractability. Next, it critically reviews the mechanisms, advantages, and engineering bottlenecks of representative green technologies, with a focus on how synergistic integration strategies based on complementary mechanisms can overcome the limitations of single technologies to achieve higher extraction efficiency and selectivity. Furthermore, it evaluates the application of response surface methodology and artificial neural networks in process modeling. Finally, it highlights critical challenges such as industrial scale-up, sustainability assessment, and intelligent manufacturing. This review advocates a paradigm shift from optimizing single techniques toward designing intelligent, synergistic systems grounded in mechanistic insights.