Work

A Multidisciplinary Approach for Predicting the Microbiological Spoilage of Chicken Breast Fillets due to Cold-Chain Disruption

Year: 2026

Type: article

Abstract: Cold-chain disruptions during the storage and distribution of raw poultry can significantly reduce product shelf-life and safety. This study evaluated the effects of short-term cyclic temperature abuse (TA) on the retail shelf-life of fresh chicken breast, using a multidisciplinary approach that combined food microbiology, machine learning (ML), metagenomics, and volatile compound (VC) analysis. Boneless, skinless chicken breast trays ( n = 450) were obtained from a commercial poultry processor and transported under refrigeration (4 °C) to Auburn University. In three independent trials, trays were randomly assigned to one of three treatments: (1) Control at 4 °C for 24 h; (2) TA Cycle 1: alternating 30 min at 4 °C and 1 h at 30 °C; and (3) TA Cycle 2: alternating 30 min at 4 °C and 1 h at 37 °C. TA cycles lasted 7.5 h, after which all trays were stored at 4 °C. Samples were analyzed on days 0, 2, 4, 6, and 8 for aerobic, facultative anaerobic, and lactic acid bacteria (LAB) counts, and for VCs using an electronic nose. Rinsates from Trial 3 were stored at −80 °C for metagenomic analysis. TA Cycle 2 resulted in the highest spoilage rates, followed by TA Cycle 1. A neural network model moderately predicted bacterial growth (R 2 = 0.65–0.75 for aerobic and facultative anaerobic microorganisms; lower for LAB). Metagenomics demonstrated a shift toward Pseudomonas spp. dominance under TA conditions, while control samples retained microbial diversity. These findings underscore that even short-term TA significantly alters the microbiome and accelerates spoilage in raw poultry, emphasizing the importance of cold-chain integrity. Practical Relevance. This study shows that short, high-temperature abuse events from refrigeration failure, handling delays, or other supply chain disruptions can accelerate spoilage in raw chicken. By combining rapid spoilage-detection tools with predictive models, poultry producers and retailers may better monitor these events, helping to maintain cold-chain integrity and reduce losses.

Source: Journal of Food Protection

DOI: 10.1016/j.jfp.2026.100754