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

Antonia Albrecht,Judith Kreyenschmidt,Martin Hebel,Ulrike Herbert,Claudia Waldhans,Maureen Mittler

doi:10.3390/foods10112740

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.

Highlights

The mean contaminations with Pseudomonas spp. (PSE), LAB, Enterobacteriaceae, B. thermosphacta and yeasts and molds were under the detection limit
All quality parameters showed a pronounced deterioration during storage, which accelerated at higher storage temperatures
The dynamic shelf-life criterion (DSLC), in combination with adequate temperature monitoring solutions, is considered an effective tool to reflect the actual status of the product

Summary

Introduction

Dynamic shelf-life modeling is an emerging field within the sector of predictive microbiology of foods. The combination of sensor data and predictive models is one innovative strategy to support cold chain management and reduce food waste [2]. This is mainly performed by optimizing logistic decisions, display rotation at the retailer site, dynamic pricing, and an efficient use of the sales window [3,4,5]. The need to reduce food waste is urgent for perishable products with a high use of resources, such as meat. A main cause is the high perishability, which leads to a short shelf-life and selling time [7]

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