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

Food spoilage poses a global challenge, contributing to economic losses, food insecurity, and health risks from microbial contamination. Conventional detection methods are often destructive, time-consuming and ineffective at identifying early biochemical changes or stereospecific microbial by-products. We developed SkiNET-FoodSpec, a novel, non-invasive biosensor platform integrating biomolecular spectroscopy with an advanced self-organising map-based neural network (SkiNET) for rapid, real-time spoilage detection. The system achieves >93% classification accuracy across a range of food matrices, including meat, milk and leafy greens. It detects key spoilage markers, such as cadaverine in meat (LoD: 0.06875 mg/kg), D-/L-lactic acid enantiomers in milk (LoD:3 mmol/mL) and carotenoid and cellulose degradation in greens (LoD: 0.071 mg/kg). By generating matrix-specific spectral barcodes, SkiNET-FoodSpec identifies early spoilage prior to visible or olfactory cues. This advance in biotechnology enables intelligent, point-of-need diagnostics for food quality assurance, offering a powerful tool to enhance food safety, reduce waste and support resilient, sustainable food systems.