A Multi-Model Approach to Implement a Dynamic Shelf Life Criterion in Meat Supply Chains

Abstract

The high perishability of fresh meat results in short sales and consumption periods, which can lead to high amounts of food waste, especially when a fixed best-before date is stated. Thus, the aim of this study was the development of a real-time dynamic shelf-life criterion (DSLC) for fresh pork filets based on a multi-model approach combining predictive microbiology and sensory modeling. Therefore, 647 samples of ma-packed pork loin were investigated in isothermal and non-isothermal storage trials. For the identification of the most suitable spoilage predictors, typical meat quality parameters (pH-value, color, texture, and sensory characteristics) as well as microbial contamination (total viable count, Pseudomonas spp., lactic acid bacteria, Brochothrix thermosphacta, Enterobacteriaceae) were analyzed at specific investigation points. Dynamic modeling was conducted using a combination of the modified Gompertz model (microbial data) or a linear approach (sensory data) and the Arrhenius model. Based on these models, a four-point scale grading system for the DSLC was developed to predict the product status and shelf-life as a function of temperature data in the supply chain. The applicability of the DSLC was validated in a pilot study under real chain conditions and showed an accurate real-time prediction of the product status.

Frequency and Antimicrobial Resistance Patterns of Foodborne Pathogens in Ready-to-Eat Foods: An Evolving Public Health Challenge

“…The study found that many meals were contaminated with high levels of bacteria, including S. aureus and E. coli. When meat products are exposed to significant levels of bacterial contamination, their characteristics are likely to change quickly [20,63,64]. The total bacteria count (TBC) is an indicator of hygienic quality and can also be used to assess the quality of food [30,65].…”

Ambient Parameter Monitoring in Fresh Fruit and Vegetable Supply Chains Using Internet of Things-Enabled Sensor and Communication Technology

“…Firstly, an IoT-based monitoring system can be used for getting insight into the current product status, performing weakness analysis on parameter profiles, and enhancing process control. Secondly, the combination of a monitoring system with predictive food models enables prediction of the remaining shelf life in each step of the chain on actual sensor data [ 112 ]. This approach facilitates optimization of storage and logistics [ 17 , 78 , 113 ].…”

“…For example, instead of the conventionally used FIFO (first in first out) principle, the FEFO (first expired first out) principle could be applied in warehouse management, which allows for shelf life-driven distribution of products [ 17 ]. To realize this, shelf life models for respective products are required [ 112 , 114 ]. If fresh fruit and vegetable supply chains should benefit from the combined use of IoT-based sensors and dynamic shelf life, a range of specific and accurate models is needed.…”