Coffee is a highly complex and variable matrix, with volatile profiles shaped by multiple factors including botanical origin, climatic and soil conditions, post-harvest treatments, and roasting parameters. This variability generates complex chemical patterns, encompassing hundreds of volatile compounds from diverse chemical classes including pyrazines, furans, aldehydes, ketones, and terpenes. The resulting chemical dimensionality poses significant analytical challenges, making the accurate identification of characteristic compounds and reliable discrimination of coffee origins particularly difficult. In this study, we applied comprehensive two-dimensional gas chromatography (GC×GC) coupled with computer vision (CV) to address these challenges. The workflow begins with untargeted fingerprinting, capturing all detectable compounds in a feature template. Multiple sample chromatograms are then combined into composite class images, representing the typical chemical features of each origin while minimising individual variability, which enables rapid pairwise comparison of different origins. CV-based pairwise comparisons highlight differential peaks, which are integrated into a targeted template for subsequent peak extraction. Multivariate analyses then identify the key discriminant compounds driving origin differentiation. Post-processing strategies, such as ion-specific intensity mapping, further enhance interpretability, enabling visualisation of compositional differences across key chemical families. Overall, this GC×GC-CV workflow provides a robust, rapid, and visually intuitive platform for comprehensive chemical characterisation and origin classification of coffee, integrating untargeted and targeted analyses in a single framework.

Background: Coffee quality is strongly influenced by origin-related factors, or terroir, which shape chemical composition and sensory characteristics. In the specialty coffee sector, where authenticity, traceability, and flavour distinctiveness drive value, understanding the molecular basis of sensory attributes, particularly perceived intensity, is essential. Methods: This study combined analytical chemistry and explainable artificial intelligence to explore relationships between volatile composition, coffee origin, and sensory intensity. Roasted and ground single-origin coffees from five provenances were analysed using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC–MS). A Support Vector Machine (SVM) classifier discriminated coffee origins based on volatile profile, and SHapley Additive exPlanations (SHAP) identified key compounds. Ridge Regression (RR) was applied to predict sensory intensity values assigned by an expert panel. Results: The SVM model classified coffee origins with 91% accuracy, and SHAP analysis highlighted the volatiles most responsible for differentiation. RR predicted sensory intensity with R² = 0.88 and RMSE = 0.38, linking molecular profiles with panel-assigned intensity scores. Conclusions: This approach connects molecular profile with packaging-declared aroma intensity, offering an indirect yet informative link to sensory perception and illustrating the potential of data-driven methods in sensory science. Overall, the proposed explainable AI approach provides a transparent and reproducible connection between chemical composition, sensory traits, and perceived quality. This strategy supports more objective and traceable quality assessment systems, aligning analytical precision with sensory expertise, which is an essential step toward the evolution of quality control in industrial applications.

Sweet basil (Ocimum basilicum L.), the defining ingredient of “pesto alla Genovese”—a traditional uncooked Ligurian sauce prepared by blending fresh basil leaves with olive oil, pine nuts, garlic, salt, and aged cheeses- has become an increasingly important industrial raw material, yet current knowledge remains fragmented across agronomy, postharvest physiology, and food processing. This review critically proposes “raw material functionality” as an integrative framework describing the measurable ability of basil to retain the physicochemical, sensory, and technological properties required for high-quality pesto production throughout processing and storage. Evidence from the literature is critically analyzed to connect preharvest factors, postharvest handling, and processing technologies with key functional attributes, including pigment stability, phenolic content, volatile composition, enzymatic activity, microbial quality and oxidative stability. The review highlights how agronomic and technological variables jointly determine color retention, aroma preservation, shelf-life, and product consistency, while identifying major knowledge gaps in linking basil composition with industrial performance. Finally future perspectives are discussed, emphasizing predictive, quality-by-design approaches that integrate compositional markers with processing behavior and shelf-life outcomes to support more consistent and sustainable industrial pesto production.

Fermentative skin contact is increasingly applied in white winemaking, yet its compositional and sensory consequences remain underexplored. This study evaluated Arneis and Erbaluce wines produced under four maceration regimes (0, 2, 7, and 14 days), as well as the effect of post-fermentative oxygenation for each wine obtained. Phenolic composition was quantified by LC-MS/MS and spectrophotometry, volatile compounds by GC-MS, and sensory attributes through a trained panel using a Rate-All-That-Apply (RATA) approach. Extended maceration substantially increased proanthocyanidins, flavan-3-ols, hydroxycinnamates, and flavonols, reaching levels comparable to red wines, while color shifted toward deeper yellow hues. Oxygenation reduced monomeric flavanols and esters and amplified oxidative notes. Sensory evaluation revealed heightened astringency and dryness in long-macerated samples, alongside diminished fruity aromas. These findings demonstrate that fermentative maceration strongly modulates the phenolic and aroma profiles of white wines, with oxygen exposure further refining their sensory expression.