This study aimed at defining the infrared spectral signatures of volatile organic compounds (VOCs) of relevant interest for coffee bean authentication and quality control. Fourier Transform Infrared Spectroscopy was employed to acquire the mid-infrared absorption spectra of some representative coffee markers, namely Pyridine, 2-Methylpyrazine, 2,5-Dimethylpyrazine, Furfural, 5-Methylfurfural and Furfuryl Alcohol, with high resolution of 0.1 cm⁻¹. Mixtures of these VOCs simulating their amount in coffee seeds were analyzed using multilinear regression. The achieved results demonstrate the potentiality of coffee fingerprinting by VOC’s signature in the absorption spectra for discriminating coffee origin.

Edible nuts and dried fruits, usually traded together in the global market, are one of the cornerstones of the Mediterranean diet representing a source of essential nutrients and bioactives. The food industry has an interest in the selection of high-quality materials for new product development while also matching consumers’ expectations in terms of sensory quality. In this study, walnuts (Juglans regia), almonds (Prunus dulcis), and dried pineapples (Ananas comosus) are selected as food models to develop an integrated analytical strategy for the informative volatile organic compounds (VOCs) quali- and quantitative profiling. The study deals with VOCs monitoring over time (12 months) and in the function of storage conditions (temperature and atmosphere).VOCs are targeted within those: (i) with a role in the product’s aroma blueprint (i.e., key-aromas and potent odorants); (ii) responsible for sensory degradation (i.e., rancidity); and/or (iii) formed by lipid autoxidation process. By accurate quantitative determination of volatile lipid oxidation markers (i.e., hexanal, heptanal, octanal, nonanal, decanal, (E)-2-heptenal, (E)-2-octenal, (E)-2-nonenal) product quality benchmarking is achieved. The combination of detailed VOCs profiling by headspace solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) and accurate quantification of rancidity markers by multiple headspace-SPME (MHS-SPME) answers many different questions about shelf-life (i.e., aroma, storage stability, impact of temperature and storage atmosphere, rancidity level), while providing reliable and robust data for long-range studies and quality controls. The quantification associated with HS-SPME profiling is demonstrated and critically commented on to help the industrial research in a better understanding of the most suitable analytical strategies for supporting primary materials selection and new product development.

Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product’s sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy as a sensomics-based expert system predicts teas’ sensory features and acts as an AI smelling and taste machine suitable for quality controls.

The present study investigated the antifungal potential of hydroalcoholic extracts of propolis against the causal agent of grey mould, Botrytis cinerea, by in vitro and in vivo assays. Five different propolis from different Italian regions were subjected to hydroalcoholic extraction using different ethanol concentrations and extraction methods. The preliminary bio-assay showed significant inhibitory effects on B. cinerea mycelial growth of propolis extracts obtained using 90% ethanol and subjected to sonication. The calculation of EC50 values, based on the demonstrated efficacy of non-volatile and volatile metabolites of propolis extracts, was useful to understand the main fraction involved in the antifungal activity of the samples and to perform the in vivo assay on grape and blueberry fruits. Three of the propolis extracts showed a high amount of genistein. Conversely, the other two propolis showed a fair amount of apigenin, caffeic acid, chrysin, ferulic acid, kaempferol, luteolin, p-coumaric acid and quercetin. From the volatile analysis of propolis, the main compounds detected were α-cadinol, α-eudesmol, calamenene, cadinol, benzyl acetate, benzyl benzoate and benzyl alcohol. The results suggest that propolis extracts have potential as an effective postharvest antifungal treatment, with varying degrees of efficacy depending on the extraction method and the type of propolis metabolites.

Tea infusions are the most consumed beverages in the world after water, their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories guaranteeing high sample throughputs even without the human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LV-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy acts as an AI smelling and taste machine predicting teas sensory features without the human panel intervention.

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.