Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.

The efficacy of low gaseous ozone concentrations (300 ppb and 400 ppb) in controlling spoilage microflora and preserving the quality of the aged Toma Piemontese PDO cheese was explored. The research integrates consumer tests, Gas Chromatography-Mass Spectrometry (GC-MS) with Solid phase Microextraction (SPME) fiber and Electronic Nose (e-nose) analysis to conduct a detailed assessment of the cheese's aromatic composition. Results indicate that low ozone concentrations significantly affected spoilage microflora, preserving the overall quality. Through GC-FID (Flame Ionization Detection) analysis, 22 of all identified compounds by GC-MS were quantified, including ethyl acetate (sweety), diacetyl and acetoin (buttery). Compared with the untreated sample, ozone treatments maintained the distinctive characteristics of Toma Piemontese PDO cheese, reducing the formation of off-flavors-related compounds (i.e., ethanol). Moreover, ozone-treated samples correlated with positive aroma scores given by consumers. However, sensory perception involves complex interactions among aroma compounds, highlighting the importance of advanced approaches. The utilization of a 12-sensor Quartz Microbalance (QMB) e-nose played a crucial role in identifying subtle differences in aroma, contributing to a more nuanced understanding of ozone treatments on the cheese's sensory profile. In conclusion, this research demonstrates the potential of ozone technology as a viable and effective method for improving the quality of aged Toma Piemontese PDO cheese.

BACKGROUND: The winemaking industry increasingly relies on non‐destructive techniques to support processing activities, particularly in response to climate change‐induced imbalances between technological and phenolic grape maturity. Spectroscopy‐based instruments have become widely available for monitoring key parameters such as total polyphenols, anthocyanins and tannins during maceration and fermentation. Near‐infrared (NIR) spectroscopy presents a promising solution for rapid and efficient analysis, potentially enhancing process control and decision‐making in winemaking.DISCUSSION: This study evaluates the feasibility of a low‐cost NIR spectroscopy‐based prototype for monitoring phenolic extraction in small‐scale fermentations. The device's performance was assessed by correlating spectral data with conventional reference measurements, including total polyphenol index, tannin concentration, colour intensity, anthocyanins and polymeric pigments. Predictive models were developed using partial least squares regression and compared with those obtained from a commercial Fourier transform (FT)‐NIR spectrophotometer to assess the prototype's accuracy and reliability.CONCLUSION: The results indicate that the low‐cost NIR prototype shows potential for monitoring phenolic extraction during fermentation, providing real‐time and non‐destructive measurements. While its predictive performance was slightly lower than that of the commercial FT‐NIR system, the prototype demonstrated promising accuracy, suggesting that with further optimization it could serve as a cost‐effective alternative for wineries seeking rapid and affordable phenolic analysis. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

This work evaluated red kiwifruit plants’ spray treatment with cis-3-hexenyl butyrate (HB) as an inductor of some metabolic mechanisms related to fruit ripening, including an increase in anthocyanin content and the red hue color parameter. Considering their key role as ripening parameters for postharvest fruit quality and sorting assessment, the soluble solid content (SSC) and the flesh firmness penetrometer (FFP) were also measured. Treated plants received an application of 50 mM HB, administered exactly 2 and 4 weeks before the commercial harvest. At harvest time and during postharvest fruit ripening, near-infrared (NIR) spectral acquisitions were performed in order to check the feasibility of a rapid and non-destructive prediction of fruit anthocyanin content and SSC, coupled to destructive measurements and chemometric modelling. Regarding technological and chemical results, HB treatment indicates an optimum overall qualitative storage at 30 days. The fruit from treated plants is characterized by good quality parameters, including higher SSC, enhanced red hue (a* value) and increased anthocyanin content, despite similar weight loss to the untreated fruit. The obtained chemometric results underscore the promise and feasibility of NIRs in terms of detecting and estimating anthocyanin content and SSC in red kiwifruit, in order to pursue an evident perspective of improvement.

The internal quality of hazelnuts (Corylus avellana L.), particularly in terms of the degradation of fat components, is widely recognized as a key factor in determining the appropriate type of industrial processing. Additionally, the internal composition and volatile profile of hazelnuts change significantly based on different roasting conditions. The here reported study investigates the efficiency of Electronic Nose (E‐nose) and Near‐Infrared Spectroscopy (FT‐NIR) technologies, combined with multivariate statistical techniques, for the rapid discrimination of hazelnuts subjected to different roasting conditions. Moreover, the study examines the ability of NIR to predict several key quality parameters in fresh and processed hazelnuts. Hazelnut samples were collected throughout the entire industrial processing chain, from delivery to roasting. The influence of three different roasting temperatures (140–150‐160°C) was evaluated, keeping the roasting time constant at 24 min. Partial Least Squares models were computed to estimate moisture content, total soluble solids, protein content, acidity, and peroxide index through correlation with FT‐NIR spectral data. Excellent regression performances were achieved for all quality parameters, except acidity, with correlations ranging between 0.951 and 0.918. Discriminant analysis models, specifically PLS‐DA and Cluster Analysis, were used to assess the ability to discriminate hazelnuts subjected to different roasting conditions using FT‐NIR and the Electronic Nose as non‐destructive tools. Obtained results from these non‐destructive techniques, particularly the volatile characterization GC/MS‐performed, accurately reflected the differentiation of samples observed through traditional chemical analyses, effectively distinguishing different groups of samples based on roasting temperature. The use of non‐destructive tools such as FT‐NIR and E‐nose during the post‐harvest life and processing of hazelnuts offers an excellent solution for monitoring key quality parameters significantly important for the food industry.

Sangiovese (Vitis vinifera L.) is one of Italy’s most important wine grape varieties, forming the basis of several iconic DOCG wines, including Chianti, Chianti Classico and Brunello di Montalcino. The production of Brunello di Montalcino is confined to a well-defined area in Tuscany, which can be further divided into four macro-zones that may influence the wine’s distinctive characteristics. However, potential compositional differences among the wines from these subzones have not yet been thoroughly explored. This study aimed to characterise Brunello di Montalcino wines from the four sub-areas in terms of their phenolic and aromatic profiles. Analyses were conducted using conventional techniques such as HPLC-DAD and GC-MS, alongside the non-destructive approaches electronic nose (E-nose) and AOTF-NIR spectroscopy. These latter tools were evaluated for their ability to differentiate wines based on their geographical origin. The results revealed pronounced differences among wines from the four subzones, particularly within specific phenolic classes and volatile compound profiles. Non-destructive methods showed promising discriminatory power in distinguishing wines according to their macro-area of origin. This study provides a deeper understanding of how intra-regional geographical differences within the Brunello di Montalcino DOCG influence wine composition and aromatic expression, and thereby reinforce the wine–terroir relationship. The results contribute to broader zoning objectives by enhancing communication of territorial identity, promoting quality through differentiation, informing consumers about grape origin, and fostering market interest through a more diversified product offering.