Rapid, non-destructive detection of Listeria monocytogenes (LM) in fresh cheeses is necessary for ensuring food safety, especially in high-risk products like Queso fresco (QF). In this study, NIR spectroscopy was combined with machine learning (Random Forest (RF) and Support vector machine (SVM)) and deep learning (1D CNN) approaches to classify LM population (0.0 log 10 CFU/g, 1.0 log 10 CFU/g, 2.0 log 10 CFU/g, and 3.0 log 10 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. • Near infrared spectroscopy was used to non-destructively detect Listeria monocytogenes (LM) in Queso fresco (QF). • Conditional Generative Adversarial Network with Wasserstein loss (cGAN-WP) was used for spectral augmentation. • Data augmentation improved performance of ML and DL models. • Augmented data with Support vector machine yielded the highest classification accuracy. • NIR spectral data with GAN shows potential as an in-site tool to predict strain-level LM contamination in QF.
Meenakshi et al. (Wed,) studied this question.