Advanced Real-Time, Non-Destructive Spectral Fingerprinting for Early microbial Spoilage Detection: AI-Integrated...

Area of Science:

• Biotechnology
• Food Science
• Spectroscopy

Background:

• Food spoilage presents significant global challenges including economic losses, food insecurity, and health risks due to microbial contamination.
• Conventional food spoilage detection methods are often destructive, time-consuming, and lack sensitivity for early biochemical changes or specific microbial by-products.

Purpose of the Study:

• To develop a novel, non-invasive biosensor platform for rapid, real-time detection of food spoilage.
• To integrate biomolecular spectroscopy with a self-organising map-based neural network (SkiNET) for enhanced spoilage identification.

Main Methods:

• Development of the SkiNET-FoodSpec biosensor platform.
• Integration of biomolecular spectroscopy with a self-organising map-based neural network (SkiNET).
• Testing across various food matrices including meat, milk, and leafy greens.

Main Results:

• Achieved >93% classification accuracy in detecting spoilage across different food types.
• Successfully detected key spoilage markers: cadaverine in meat (LoD: 0.06875 mg/kg), D-/L-lactic acid enantiomers in milk (LoD: 3 mmol/mL), and degradation in greens (LoD: 0.071 mg/kg).
• Generated matrix-specific spectral barcodes for early spoilage identification prior to sensory detection.

Conclusions:

• The SkiNET-FoodSpec platform offers a powerful tool for intelligent, point-of-need diagnostics in food quality assurance.
• This advance in biotechnology enhances food safety, reduces food waste, and supports sustainable food systems.