Analysis of the Temperature Distribution in a Refrigerated Truck Body Depending on the Box Loading Patterns

Quality Changes in Live Ruditapes philippinarum During "Last Mile" Cold Chain Breakage: Effect of Packaging

The reliability of the "last mile" of cold-chain logistics is crucial for food safety. This study investigated the effect of different packaging treatments on the quality of anhydrously preserved live Ruditapes philippinarum (R. philippinarum) in "last mile" cold chain disruption. The temperature profiles of three packaging treatments at ambient temperature (25 °C) were monitored. Quality assessment was conducted based on sensory scoring, survival rate, total viable count (TVC), water-holding capacity (WHC), pH, total volatile basic nitrogen (TVB-N), thiobarbituric acid-reactive substances (TBA), color, and texture. Low-frequency nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging (MRI) were utilized to characterize the water state profile. The findings demonstrated a progressive increase in internal package temperature throughout the "last mile", with packages containing additional ice packs more effectively maintaining lower temperature and restricting the migration of "hot spots" towards the center. Specifically, the package with three ice packs maintained a markedly lower temperature, which effectively inhibited microbial activity, lipid oxidation, and the production of alkaline substances, resulting in higher survival rates, water-holding capacity, texture, sensory acceptability, and immobilized water fraction. Furthermore, LF-NMR relaxation parameters showed strong correlations with various physicochemical indices, suggesting a potential approach for real-time quality monitoring. This study provides insights for maintaining live R. philippinarum quality during the "last mile".

Evaluation of Temperature Uniformity in a Middle-Refrigerated Truck Loaded with Pig Carcasses

In this study, we constructed a calculation model to determine the internal temperature field distribution in a medium-sized refrigeration truck with the dimensions of 4.1 m × 2.2 m × 2.2 m. Wind speed, air temperature, and carcass temperature were designated as the initial conditions. The k-ε model of computational fluid dynamics was used to simulate different wind speeds and ventilation duct settings on the carriage. Additionally, under specific boundary conditions, the speed of the air outlet, the types of ventilation ducts, and the carcass loads were all varied to determine the uniformity of the temperature field. The results showed that, when the air outlet speed was 5 m/s, the temperature field in the refrigerated truck was relatively more uniform. The simulated results were in good agreement with the measured results. The average absolute error was 0.35 °C, and the average relative error was 9.23%.

Building “First Expire, First Out” models to predict food losses at retail due to cold chain disruption in the last mile

Current less-than-truckload (LTL) shipping practices allow for temperature abuse (TA) in the last segment (last mile) of the food supply chain. When this TA is combined with “First In, First Out” product rotation methods, it could lead to food spoilage and food waste; therefore, data-based decision models are needed to aid retail managers. An experiment was designed using pallets (4 layers/pallet × 5 boxes/layer) of commercially produced boneless chicken breast filet trays. The pallets were exposed to 24 h of simulated LTL TA (cyclic 2 h at 4°C, then 2 h at 23 ± 2°C). Filet temperatures were recorded for all 20 boxes using dataloggers with thermocouple wires. Additionally, microbiological sampling of filets [aerobic plate counts (APC) and psychrotrophic plate counts (PSY)] was conducted before (0 h of LTL TA) and after (24 h of LTL TA) the TA experiment for select boxes of the pallet and compared to control filets (maintained at 4°C). After TA, a shelf-life experiment was conducted by storing filets from predetermined boxes at 4°C until spoilage (7 log CFU/ml). Temperature and microbiological data were augmented using Monte Carlo simulations (MC) to build decision making models using two methods; (1) the risk of each box on the pallet reaching the bacterial “danger zone” (>4°C) was determined; and (2) the risk-of-loss (shelf-life < 4 days; minimum shelf-life required to prevent food waste) was determined. Temperature results indicated that boxes on the top and bottom layers reached 4°C faster than boxes comprising the middle layers while the perimeter boxes of each layer reached 4°C faster than centrally located boxes. Shelf-life results indicate simulated LTL TA reduced shelf-life by 2.25 and 1.5 days for APC and PSY, respectively. The first MC method showed the average risk of boxes reaching 4°C after 24 h of simulated LTL TA were 94.96%, 43.20%, 27.20%, and 75.12% for layers 1–4, respectively. The second MC method indicated that exposure at >4°C for 8 h results in a risk-of-loss of 43.8%. The findings indicate that LTL TA decreases shelf-life of chicken breast filets in a heterogenous manner according to location of boxes on the pallet. Therefore, predictive models are needed to make objective decisions so that a “First Expire, First Out” method can be implemented to reduce food wastes due to TA during the last mile.