Dates (Phoenix dactylifera L.) are nutrient-rich fruits with health-promoting properties and broad applications in the food and beverage industries. This study analyzes the chemical properties and volatile profile of fermented date products—juice, alcoholic derivative, and vinegar—to develop a high-quality vinegar with distinct sensory traits. Using HS-SPME-GC-MS, about 50 volatile compounds were identified across six major chemical classes. Juice processing significantly increased volatile release, especially fusel alcohols and furanic aldehydes, due to thermal and mechanical disruption. Fermentation further modified the volatilome, with increased esters and acids in alcoholic and vinegar products. Vinegar was characterized by high levels of acetic acid, fatty acids, phenols, and acetoin (855 mg/L), indicating active microbial metabolism. Ethanol and acidity levels met international standards. Total phenolic content rose from juice (138 mg/L) to vinegar (181 mg/L), reflecting microbial enzymatic activity and acid-driven extraction. These results highlight the metabolic complexity, sensory richness, and functional potential of date-derived fermented products while promoting sustainable use of underutilized fruit resources.

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.

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.