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

Area of Science:

• Food Science
• Microbiology
• Data Science

Background:

• Cold-chain disruptions in raw poultry storage and distribution reduce shelf-life and safety.
• Understanding the impact of temperature abuse (TA) on raw chicken quality is essential for the poultry industry.

Purpose of the Study:

• To evaluate the effects of short-term cyclic temperature abuse on the retail shelf-life of fresh chicken breast.
• To combine food microbiology, machine learning (ML), metagenomics, and volatile compound (VC) analysis to assess spoilage.

Main Methods:

• Fresh chicken breast trays were subjected to control (4°C) or cyclic temperature abuse (alternating 4°C with 30°C or 37°C).
• Microbial counts (aerobic, facultative anaerobic, LAB), volatile compounds (electronic nose), and metagenomics were analyzed over 8 days.
• A neural network model was developed to predict bacterial growth.

Main Results:

• Temperature abuse, especially at higher temperatures (37°C), significantly accelerated spoilage rates.
• Metagenomics revealed a shift towards Pseudomonas spp. dominance under TA conditions, reducing microbial diversity.
• The neural network model moderately predicted bacterial growth, with R² values of 0.65-0.75 for aerobic and facultative anaerobic microorganisms.

Conclusions:

• Even short-term temperature abuse significantly alters the raw poultry microbiome and accelerates spoilage.
• Maintaining cold-chain integrity is critical to prevent microbial shifts and extend the shelf-life of raw chicken.
• Integrating predictive models with spoilage detection tools can help the poultry industry monitor and mitigate losses due to temperature abuse.