Rapid single-cell identification of foodborne pathogens with limited data: A peak-aware Raman attention deep learning...

Foodborne pathogens represent a major global threat to food safety, necessitating rapid and reliable identification technologies. While Raman spectroscopy provides a powerful label free tool for molecular fingerprinting, its performance is often compromised by high biochemical similarity between strains and the bottlenecks of data scarcity in practical scenarios. To address these challenges, we propose the Peak Aware Raman Attention Model (PARAM), a generative deep learning framework designed for high fidelity spectral augmentation. Unlike traditional generative models that often suffer from peak shifts or intensity distortions, PARAM integrates a peak aware attention mechanism. This mechanism explicitly preserves the structural integrity of biochemically significant spectral regions, such as the 1003 cm^-1 phenylalanine and 1330 cm^-1 nucleic acid modes, through peak guided attention mapping. We evaluated PARAM using four major pathogens under extreme data constraints of only 7 samples per class. Experimental results demonstrate that PARAM generated spectra achieve exceptional consistency with real data, exhibiting 100% peak position matching and a Pearson correlation coefficient of 0.9989. Furthermore, augmenting the training set with PARAM generated data significantly boosted the Random Forest classification accuracy from 87.67% to 97.95%. These findings highlight PARAM as a robust strategy to overcome data scarcity in food safety monitoring.