Assessing Spoilage Risks in Raw Chicken Breast: A Convergence Approach Using Microbiological Analysis, Volatile Profiling, Metagenomics, and Data Analytics for Microbial Growth and Shelf-Life Prediction

Abstract

Disruptions in the cold chain at various stages, from poultry processing to storage, distribution, and retail, can significantly impact the shelf life and quality of raw chicken, particularly during less-than-truckload (LTL) transportation, where cyclic temperature abuse (TA) fluctuations may occur. This study evaluates the effects of short-term TA on the microbiome of fresh chicken breast and its implications for retail shelf life. To simulate real-world conditions, cyclic temperature abuse (30 minutes at 4°C followed by 1 hour at either 30°C or 37°C, repeated over 7.5 hours) was applied. While traditional spoilage studies primarily rely on standard microbiological techniques, this research integrates a novel, non-invasive electronic nose (e-nose) technology to analyze the volatile profile of raw chicken, providing an alternative approach for spoilage detection. Additionally, metagenomic analysis was conducted to characterize shifts in the spoilage microbiome. Furthermore, microbial data were used to develop a predictive feedforward neural network (FNN) model capable of estimating spoilage bacterial growth under TA conditions. This research bridges the fields of food microbiology and computational modeling, integrating advanced analytical techniques to enhance food safety and optimize supply chain management for perishable products.