350 Award Talk: Developing a smart-aging strategy to improve meat quality and value.

Abstract Postmortem aging is a widely practiced, value-adding process in the meat industry due to its well-documented benefits on the eating quality of fresh meat. Notable improvements in tenderness, juiciness, and flavor are achieved through the action of endogenous proteolytic systems during extended aging. It is well-established that individual muscles exhibit unique aging responses, particularly in terms of proteolysis and resultant sensory qualities. However, the optimal conditions for maximizing aging benefits across different muscle types remain incompletely defined. While extended aging enhances palatability attributes, our research has revealed that it can negatively impact color and lipid oxidative stability. These adverse effects are also muscle-specific, with certain muscles exhibiting greater susceptibility to aging-induced oxidation. To address this, our group developed a novel “Smart-aging” system—a tailored, post-harvest strategy designed to optimize aging conditions based on muscle-specific responses, thereby improving overall meat quality and product value. We have elucidated key biochemical mechanisms underlying these differential aging responses, particularly regarding color, oxidative stability, and spoilage in various beef muscles. Through this work, we identified molecular markers in muscle tissues and exudates that hold potential for the development of predictive systems to assess spoilage risk and quality attributes. Such marker-based tools would support the delivery of fresh, safe, and high-quality meat products while reducing food loss and waste. The Smart-aging system also provides a platform to identify optimal aging parameters under various post-harvest conditions. For example, our group demonstrated that dry-aging can be further optimized by precisely controlling environmental variables, thereby enhancing eating quality even in lower-grade beef. Using a metabolomics approach, our group identified key metabolic precursors involved in generating volatile compounds associated with the distinctive flavor of dry-aged beef. In parallel, we developed a Smart-tumbling system for fresh beef sections that does not require brine enhancement. This approach induces significant structural disruption at the cellular level, resulting in an immediate 40% improvement in tenderness. Building on this innovation, Dr. Kim’s team is currently developing an AI-driven predictive model for tumbling optimization using machine learning techniques. This model aims to ensure consistent quality and provide a natural, scalable processing solution for the meat industry. These advancements represent a significant step forward in precision meat processing, enabling the consistent production of high-quality, value-added meat products through simple, data-informed, and muscle-specific interventions.