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

Area of Science:

• Food Science
• Microbiology
• Sensory Analysis

Background:

• Fresh meat's high perishability leads to significant food waste, exacerbated by fixed best-before dates.
• Current shelf-life estimations often fail to account for real-time environmental conditions during distribution and storage.

Purpose of the Study:

• To develop a dynamic shelf-life criterion (DSLC) for fresh pork filets.
• To integrate predictive microbiology and sensory modeling for real-time shelf-life assessment.
• To reduce food waste by providing a more accurate measure of pork freshness.

Main Methods:

• Investigated 647 ma-packed pork loin samples under various storage conditions.
• Analyzed meat quality parameters (pH, color, texture, sensory) and microbial counts (TVC, Pseudomonas, LAB, B. thermosphacta, Enterobacteriaceae).
• Employed dynamic modeling using modified Gompertz (microbial) and linear (sensory) models combined with the Arrhenius model.

Main Results:

• Developed a four-point scale grading system for the DSLC.
• The DSLC accurately predicts pork product status and shelf-life based on temperature data.
• Validated the DSLC in a pilot study under real-world supply chain conditions, confirming its predictive accuracy.

Conclusions:

• The developed DSLC offers a reliable method for real-time shelf-life prediction of fresh pork.
• This dynamic approach can significantly contribute to reducing food waste in the meat industry.
• The multi-model approach provides a robust framework for assessing perishable food product quality.