Fuente: PubMed "honey" Rapid Commun Mass Spectrom. 2026 Oct 15;40(19):e70134. doi: 10.1002/rcm.70134.ABSTRACTRATIONALE: Food classification and adulteration detection are challenged by the complexity of chemical matrices. Traditional descriptor-based methods fail to capture interactions in chromatography-mass spectrometry (CMS) data. This study compares a convolutional neural network (CNN) against a random forest (RF) baseline using engineered descriptors to classify 15 food items to demonstrate deep learning advantage for objective, scalable food authentication.METHODS: Three thousand CMS spectra is computationally simulated (200 per class) based on FooDB compound profiles. Six engineered descriptors (mean intensity, standard deviation, spectral entropy, etc.) are extracted for RF training. A SimpleCNN with two convolutional layers (32/64 filters, kernel size 5) followed by max-pooling, a fully connected layer (128 units, dropout 0.3), and softmax output is trained (Adam, lr = 0.001, 50 epochs). t-SNE and K-means (k = 5) visualize the chemical space, while adulteration are simulated by mixing corn syrup into honey spectra (10%-50% v/v).RESULTS: The CNN achieves 93.1% accuracy, significantly outperforming RF (87.3%). t-SNE reveales five coherent clusters (silhouette coefficient = 0.71; Davies-Bouldin index = 0.43). Adulteration detection reaches 94.2% sensitivity and 96.8% specificity. The chemical similarity network identified novel pairings (e.g., chocolate-coffee, cosine similarity = 0.804) consistent with shared Maillard reaction products.CONCLUSION: End-to-end deep learning on CMS fingerprints substantially surpasses descriptor-based classification to provide a robust pipeline for food authentication, adulteration screening, and data-driven pairing discovery. Validation on experimentally acquired spectra remains a necessary next step.PMID:42421483 | DOI:10.1002/rcm.70134