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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. • AI-enabled Raman spectroscopy enables early, non-destructive food spoilage detection. • SkiNET-FoodSpec discriminates healthy and E. coli -infected food samples via SOM analysis. • Raman biomarkers reveal microbial activity, cellulose loss and chlorophyll degradation. • Distinct Raman fingerprints differentiate D- and L-lactic acid in milk proteins. • High specificity (≥93%) and rapid analysis support real-time , Industry 5.0 monitoring.
Bhowmik et al. (Fri,) studied this question.