Impact of frozen storage on ground beef quality and artificial intelligence strategies for energy optimization in meat cold storage

Abstract

Two independent studies were conducted to evaluate the effectiveness of temperature intensive management in frozen food storage. The first study was designed to elucidate the impact of experimental frozen storage conditions on the microbial quality and lipid oxidation in ground beef products. The second study aimed to measure energy usage in response to Artificial Intelligence (AI) temperature management in cold storage facilities. As global meat supply chains face increased pressure to reduce environmental impacts, cold storage facilities are among the largest energy consumers, highlighting their pivotal role in promoting sustainability initiatives. Sustainability initiatives outlined by private sector companies align with the targets established by the United Nations Net Zero Coalition, aiming to achieve carbon neutrality by 2050. Therefore, the effective management within cold storage facilities, in both refrigeration and frozen conditions, is essential for optimizing performance in the evolving global market. Given the energy-intensive nature of frozen storage in meat supply chains, innovation is essential to meet sustainability goals without compromising food safety or product quality. The first experiment conducted aimed to evaluate the effects of highly controlled frozen storage temperatures (-20.6°C, -15.0°C, and -9.4°C) on the microbial viability and extent of lipid oxidation in vacuum-packaged ground beef samples held over a 30 d period. Ground beef was inoculated with a mixture of six common meat spoilage organisms to achieve a known concentration level of 4 log CFU/g. Microbial quality was evaluated using aerobic plate counts (APC), recorded as log CFU/g, while lipid oxidation was quantified using the thiobarbituric acid reactive substances assay (TBARS), and recorded as mg MDA/kg. Results of the study indicated no statistically significant differences (P ≥ 0.05) in meat spoilage indicators across the different temperature treatments. The key finding was that ground beef stored at -9.4°C, under precise temperature control (±0.01°C) systems, does not negatively impact meat quality over the 30 d frozen storage period. The second case study examined energy savings in industrial cold storage facilities when managing temperature variations with AI-controlled systems. For this study, we included two facility locations that were similar in size, ambient temperatures, and operational procedures: one in Colorado and the other in Illinois. The energy utilization in (kW) was recorded from both facilities, before and after implementation of energy-AI software (Crossnokaye's ATLAS) over a 94 d period. We analyzed the data using a two-way fixed effects ANOVA model. Results indicated a 32% reduction in overall power consumptions when using AI (P < 0.0001). The reduction of power usage over the 94 d period equated to an estimated cost saving of approximately $42,258 (USD) per facility. The reductions in energy could result in an overall decrease of Scope-II greenhouse gas emissions, thereby achieving a step towards carbon neutrality. Findings from these studies are relevant to discussions regarding adjustments to the frozen storage temperature setpoints in facilities. Raising temperature setpoints have the potential to reduce energy consumption, lower operational costs, and lower greenhouse gas emissions, all while maintaining the safety and quality of temperature-sensitive meat products. When facilities implement AI temperature management systems, they enable real-time adjustments to dynamic conditions. This management strategy provides a scalable pathway for the enhancement of operational efficiency and environmental sustainability within meat cold storage supply chains.