Dataset of volatile profiles and sensory evaluation of commercial maize snacks from Colombia using headspace gas chromatography-mass spectrometry.

This article presents a dataset integrating Headspace Gas Chromatography-Mass Spectrometry (HS-GC-MS) analyses and descriptive sensory evaluation data for three commercially available maize-based snack products from the Colombian market. The dataset was developed to support research in food science, sensory analysis, chemometrics, and predictive modeling of food perception. HS-GC-MS analyses were performed in triplicate for each product, generating nine chromatographic files in (.cdf) format and a processed dataset containing retention times, retention indices, peak areas, and tentative volatile compound identifications. In parallel, a semi-trained sensory panel composed of 32 participants evaluated 17 sensory descriptors associated with odor, taste, and texture using a structured intensity scale. To assess the statistical value of chromatographic data, a Partial Least Squares Discriminant Analysis (PLS-DA) model was developed using volatile compound profiles. The model showed a clear discrimination among the three products, with the first two components explaining 97.3 % of the total variance. Variable Importance in Projection (VIP) analysis identified terpene-related compounds, including β-pinene, limonene, γ-terpinene, α-pinene, and p-cymene, as the main contributors to sample differentiation. Cross-validation demonstrated excellent model performance with R² = 0.99 and Q² = 0.98. The dataset provides openly accessible chromatographic and sensory information that may support comparative studies, food quality assessment, sensory prediction models, and the development of novel maize-based food products.