Improving detection of Listeria monocytogenes in Queso fresco using generative adversarial network (GAN) augmented NIR spectral analysis

CFU/g) and LM strain (ATCC® 43256™, ATCC® 43257™, and ATCC® BAA-3134™). To enhance the size of the dataset, spectral augmentation was performed using a conditional Wasserstein generative adversarial network with gradient penalty (cWGAN-GP), and model performance was evaluated using both real and augmented spectra. The results showed that GAN-based augmentation substantially improved strain-level classification performance compared with models that trained only on real spectra. Among the classification algorithms, SVM (95.7%) and 1D CNN (95.6%) achieved the highest classification accuracies on the augmented dataset, highlighting the benefit of synthetic spectral generation for improving robustness. Overall, this study emphasizes the potential of GAN-assisted NIR spectral augmentation along with machine learning (ML)/deep learning (DL) models as an on-site screening approach for LM detection and strain differentiation in fresh cheese.