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GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition)

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A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Analytical Methods, Instrumentation and Miniaturization".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 35280

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Special Issue Editors

Dr. María José Aliaño-González

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Department of Analytical Chemistry, University of Cádiz, Puerto Real, 11510 Cádiz, Spain
Interests: analytical chemistry; agri-food resources; forensic chemistry; adulterations; fire analysis; environmental analysis; circular economy; bioactive compounds; chromatography; spectrophotometry; ion mobility spectrometry
Special Issues, Collections and Topics in MDPI journals

Dr. Bruno Medronho

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1. MED—Mediterranean Institute for Agriculture, Environment and Development, Faculty of Sciences and Technology, University of Algarve, Campus de Gambelas, Ed. 8, 8005-139 Faro, Portugal
2. FSCN, Surface and Colloid Engineering, Mid Sweden University, SE-851 70 Sundsvall, Sweden
Interests: rheology; biopolymers; biomaterials; colloids; lignocellulose; polyphenol dissolution and extraction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Gas chromatography (GC) is an analytical technique used to separate volatile components from incredibly complex matrices (such as smoke, fuel spills, etc.) of a very varied nature for subsequent identification and/or quantification. GC has been coupled with multiple detectors, such as mass spectrometers (MS), which provide significantly high sensitivity (in the ppb range) in the analysis performed and for the exact identification of previously separated components. Recently, some researchers have started to use MS as a chemosensor in which each fragment ion (m/z ratio) acts as a sensor and its abundance is equivalent to the signal of this sensor, providing the characteristic total profile of each sample, like a fingerprint, which allows the resolution of an analytical problem without the identification of the compounds. This trend has also been observed among other GC-coupled detectors, such as ion mobility spectroscopy or even UV-Vis spectroscopy.

This Special Issue of Chemosensors, entitled “GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition)”, aims to provide a forum for the latest research in the application of gas chromatography and/or mass spectrometry in chemosensors for analytical purposes. Both review articles and research papers are welcome.

Dr. María José Aliaño-González
Dr. Bruno Medronho
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Chemosensors is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

gas chromatography
mass spectrometry
volatile compounds
total profile
chemosensors
fingerprint
analytical chemistry
complex matrix

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Published Papers (12 papers)

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Research

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15 pages, 3444 KB

Open AccessArticle

Comparative Characterisation of Meat Quality, Nutritional Composition, and Flavour Profile in Wuhua Yellow Chickens (Gallus domesticus) Assessed by Multi-Analytical Approaches

by Zhuoxian Weng, Yongjie Xu, Weina Li, Xunhe Huang, Liangjie Luo, Zhiwei Liu and Xiaonan Zhang

Chemosensors 2026, 14(5), 109; https://doi.org/10.3390/chemosensors14050109 - 2 May 2026

Viewed by 258

Abstract

Wuhua Yellow Chicken (WYC) is a Guangdong heritage breed known for its characteristic “three yellow” phenotype and distinctive meat flavour. Despite its commercial importance, data on muscle flavour chemistry remain scarce. In this study, 180 one-day-old chicks (90 cocks, 90 hens, 18 replicates of 5 chickens per sex) were raised to 20 weeks under cage conditions, after which slaughter traits, meat physicochemical indices, proximate composition, amino acid and fatty acid profiles, and volatile compounds were measured. Cocks were heavier and had higher eviscerated yields and leg muscle percentages, whereas hens accumulated more abdominal fat (6.47–0.46%, p < 0.01). Shear force was greater in cock breast muscle (2.86–2.13 kg·f, p < 0.01), indicating firmer texture. Cock breast muscle contained more crude protein (26.89%) and less crude fat. Amino acid totals were identical between sexes (21.10 g/100 g), with all six essential amino acids surpassing FAO/WHO reference values; lysine scored highest (168%). Unsaturated fatty acid proportions were 63.33% (cocks) and 66.64% (hens), with PUFA/SFA ratios of 61.95% and 53.60%, respectively. Gas chromatography-mass spectrometry identified 10 volatile compounds in cocks and 14 in hens; aldehydes dominated in both, with hexanal alone accounting for over 50%. Hen muscle contained a richer volatile profile, including additional ketone and ester compounds. These data collectively confirm that WYC is nutritionally dense, organoleptically appealing, and well-suited for further breed promotion. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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16 pages, 15328 KB

Open AccessArticle

Dynamic Characteristics of Primary and Secondary Polar Metabolites in Cabernet Sauvignon Grapes at Different Growth Stages in the Ningxia Wine Region

by Feng-Lian Ma, Jia-Nan Wang, Xue-Teng Guo, Hang Lv, Jia-Jia Fan, Gui-Juan Ma, Li-Hua Tang, Yi Lv and Yong-Jie Yu

Chemosensors 2026, 14(2), 50; https://doi.org/10.3390/chemosensors14020050 - 15 Feb 2026

Viewed by 851

Abstract

This study focuses on the Helan Mountain East Foothills region of Ningxia, a typical continental climate wine-growing area, with Cabernet Sauvignon grapes as the subject. It combines trimethylsilyl derivatization–Gas Chromatography–Mass Spectrometry (TMS-GC-MS) technology and the independently developed AntDAS-GCMS data analysis platform. The aim was to systematically characterize the temporal dynamics of primary and secondary polar metabolites throughout the entire growth cycle of Cabernet Sauvignon in this region. Results identified 50 metabolites exhibiting significant differences (fold change ≥1, p < 0.05) across growth stages, primarily comprising organic acids (18), sugars (7), and amino acids (13). Metabolite accumulation demonstrated distinct stage-specific patterns: organic acids (e.g., tartaric acid, malic acid) peaked before veraison and then declined significantly, while sugars (e.g., fructose) exhibited a marked increase in abundance during the late maturation stage. The underlying mechanisms of the relevant metabolic pathways require further validation through multi-omics approaches. This study elucidates the dynamic characteristics of primary and secondary metabolites throughout the entire growth stages of Cabernet Sauvignon in the region of Ningxia. It provides data support for understanding the metabolic basis of flavor development in grapes from this area and offers practical references for quality regulation and harvest timing optimization in local grape cultivation management. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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20 pages, 1444 KB

Open AccessArticle

Benchtop Volatilomics and Machine Learning for the Discrimination of Coffee Species

by Catherine Kiefer, Steffen Schwarz, Nima Naderi, Hadi Parastar, Sascha Rohn and Philipp Weller

Chemosensors 2026, 14(2), 34; https://doi.org/10.3390/chemosensors14020034 - 2 Feb 2026

Cited by 1 | Viewed by 1239

Abstract

The main characteristics of the large number of coffee species are differences in aroma and caffeine content. Labeled blends of Coffea arabica (C. arabica) and Coffea canephora (C. canephora) are common to broaden the flavor profile or enhance the stimulating effect of the beverage. New emerging species such as Coffea liberica (C. liberica) further increase the variability in blends. However, significant price differences between coffee species increase the risk of unlabeled blends and thus influence food quality and safety for consumers. In this study, a prototypic hyphenation of trapped headspace-gas chromatography-ion mobility spectrometry-quadrupole mass spectrometry (THS-GC-IMS-QMS) was used for the detection of characteristic compounds of C. arabica, C. canephora, and C. liberica in green and roasted coffee samples. For the discrimination of coffee species with IMS data, multivariate resolution with multivariate curve resolution–alternating least squares (MCR-ALS) prior to partial least squares–discriminant analysis (PLS-DA) was evaluated. With this approach, the classification accuracy, as well as sensitivity and specificity, of the PLS-DA model was significantly improved from an overall accuracy of 87% without prior feature selection to 92%. As MCR-ALS preserves the physical and chemical properties of the original data, characteristic features were determined for subsequent substance identification. The simultaneously generated QMS data allowed for partial annotation of the characteristic volatile organic compounds (VOC) of roasted coffee. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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12 pages, 980 KB

Open AccessArticle

HS-SPME-GC-MS Volatile Profile of “Aglio Rosso di Sulmona” (Sulmona Red Garlic) Floral Scape

by Samantha Reale, Rossella Ferretti, Alessandra Biancolillo, Valter Di Cecco, Luciano Di Martino, Marco Di Santo and Angelo Antonio D’Archivio

Chemosensors 2025, 13(10), 361; https://doi.org/10.3390/chemosensors13100361 - 2 Oct 2025

Viewed by 1239

Abstract

Garlic (Allium Sativum L.) is a source of organosulphur compounds with well-known sensorial and biological activity. Organosulphur precursors of garlic aroma are also detected in the plant leaves, but limited literature on this subject is available. This study is aimed at the characterization of the volatile profile of the floral scapes of Sulmona red garlic (aglio rosso di Sulmona) cultivated in the Abruzzo region (Italy). Floral scapes are manually removed from the plant before flowering and used as an ingredient of local gastronomy. The organosulphur volatile profile of the scapes is investigated by HS-SPME-GC-MS and compared to that provided by the clove. The GC-MS chromatogram of garlic clove, which is characterized by the predominant contribution of a few organosulphur organic compounds, is significantly more intense than that of the scapes. Almost all the organosulphur compounds contributing to the clove aroma were detected in the scape volatile profile, which, however, exhibits a more balanced contribution of major and minor organo sulphur compounds. Moreover, a significantly higher relative abundance of terpenes and aldehydes is observed in the scape aroma. The geographical/varietal origin of clove seeds (Sulmona versus Spain or France) and cultivation area interactively influence the aroma profile of Sulmona red garlic scapes. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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14 pages, 1756 KB

Open AccessArticle

In-Depth Investigation of the Chemical Profile of Pelargonium odoratissimum (L.) L’Hér. Hydrolate by SPME-GC/MS, GC/MS, LVI-GC/MS and PTR-Tof-MS Techniques

by Cosimo Taiti, Vittorio Vinciguerra, Monica Mollica Graziano, Elisa Masi and Stefania Garzoli

Chemosensors 2025, 13(9), 325; https://doi.org/10.3390/chemosensors13090325 - 1 Sep 2025

Viewed by 1148

Abstract

Hydrolates are aromatic aqueous solutions saturated with volatile water-soluble compounds of essential oil. Despite their potential, hydrolates remain less explored than essential oils. In this work, the hydrolate of Pelargonium odoratissimum (L.) L’Hér. has been analyzed by multiple analytical techniques in order to describe its chemical composition. Headspace (HS-) and Direct Immersion-Solid Phase Microextraction-Gas Chromatography/Mass spectrometry (DI-SPME-GC/MS) and Proton Transfer Reaction Time-of-Flight Mass Spectrometry (PTR-ToF-MS) were employed to reveal the VOC emission from the hydrolate. Further, a direct injection of the pure hydrolate and of the hydrolate after extraction with hexane was performed by Large-Volume Injection Gas Chromatography/Mass Spectrometry (LVI-GC/MS) and GC/MS. The results obtained by HS- and DI-SPME-GC/MS highlighted a nearly overlapping chemical profile with linalool, isomenthone, and α-terpineol as the main volatiles. On the other hand, analysis of the hydrolate by GC/MS after solvent extraction revealed a lower overall number of compounds but allowed the detection of thujone and cis-linalool oxide. In comparison, LVI-GC/MS was the technique that allowed the identification of a higher number of volatiles with citronellol, linalool, and α-terpineol as the principal compounds. Finally, PTR-ToF-MS was a fundamental approach to quantify and evaluate total terpene emissions from this complex matrix starting from low-molecular-weight compounds such as acetylene, methanol, acetaldehyde, acetone, and ethanol, which were the most abundant. Among the detected compounds, dimethyl sulfide and small amounts of dimethyl-furan and 2-butylfuran were also identified. Overall, the findings showed that the hydrolate was rich in monoterpene compounds while sesquiterpene compounds were missing. A very low intensity relating to sesquiterpenes was recorded only by PTR-ToF-MS technique. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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11 pages, 474 KB

Open AccessArticle

Comparison of Hydrodistillation and Headspace Solid-Phase Microextraction to Analyze Volatiles from Brazilian Propolis by GC-MS

by Mariana Budóia Gabriel, Guilherme Perez Pinheiro, Leandro Wang Hantao and Alexandra Christine Helena Frankland Sawaya

Chemosensors 2025, 13(9), 322; https://doi.org/10.3390/chemosensors13090322 - 1 Sep 2025

Viewed by 1418

Abstract

Propolis is a substance produced by bees from the collection of plant resins, with a chemical composition that varies according to the available flora and region, and it has several biological activities. Stingless bee propolis is often produced in reduced amounts, posing a challenge to the study of their volatile compounds, as traditional hydrodistillation extraction would demand more raw propolis than available. These bees collect resins from various sources, resulting in a variable composition, so a standardized reproducible method is fundamental for their analysis. Headspace solid-phase microextraction (HS-SPME), associated with gas chromatography, appears to be an efficient alternative for the analysis of these volatiles. In this study, the GC-MS results of three types of SPME fibers were compared to those of extracts obtained by hydrodistillation to evaluate their efficiency in representing the composition of essential oils from (geo)propolis of different species. The extraction time and temperature were also standardized. Among the fibers tested, PDMS/DVB extracted the volatiles in a similar manner to the essential oil obtained by hydrodistillation for all the samples tested, indicating this to be the best choice of fiber coating for propolis volatile extraction and analysis. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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17 pages, 1602 KB

Open AccessArticle

Deep Transfer Learning for Automatic Analysis of Ignitable Liquid Residues in Fire Debris Samples

by Ting-Yu Huang and Jorn Chi Chung Yu

Chemosensors 2025, 13(9), 320; https://doi.org/10.3390/chemosensors13090320 - 26 Aug 2025

Cited by 1 | Viewed by 1914

Abstract

Interpreting chemical analysis results to identify ignitable liquid (IL) residues in fire debris samples is challenging, owing to the complex chemical composition of ILs and the diverse sample matrices. This work investigated a transfer learning approach with convolutional neural networks (CNNs), pre-trained for image recognition, to classify gas chromatography and mass spectrometry (GC/MS) data transformed into scalogram images. A small data set containing neat gasoline samples with diluted concentrations and burned Nylon carpets with varying weights was prepared to retrain six CNNs: GoogLeNet, AlexNet, SqueezeNet, VGG-16, ResNet-50, and Inception-v3. The classification tasks involved two classes: “positive of gasoline” and “negative of gasoline.” The results demonstrated that the CNNs performed very well in predicting the trained class data. When predicting untrained intra-laboratory class data, GoogLeNet had the highest accuracy (0.98 ± 0.01), precision (1.00 ± 0.01), sensitivity (0.97 ± 0.01), and specificity (1.00 ± 0.00). When predicting untrained inter-laboratory class data, GoogLeNet exhibited a sensitivity of 1.00 ± 0.00, while ResNet-50 achieved 0.94 ± 0.01 for neat gasoline. For simulated fire debris samples, both models attained sensitivities of 0.86 ± 0.02 and 0.89 ± 0.02, respectively. The new deep transfer learning approach enables automated pattern recognition in GC/MS data, facilitates high-throughput forensic analysis, and improves consistency in interpretation across various laboratories, making it a valuable tool for fire debris analysis. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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28 pages, 1682 KB

Open AccessArticle

Anti-Aging Potential of Illyrian Iris Rhizome Extract: Preliminary Chemical and Biological Profiling and Chemosensor Analysis via GC/MS and UHPLC-DAD-MS/MS Combined with HPTLC Bioautography

by Ivana Stojiljković, Đurđa Ivković, Jelena Stanojević, Jelena Zvezdanović, Jelena Beloica, Maja Krstić Ristivojević, Dalibor Stanković, Mihajlo Jakanovski and Petar Ristivojević

Chemosensors 2025, 13(9), 319; https://doi.org/10.3390/chemosensors13090319 - 25 Aug 2025

Cited by 1 | Viewed by 2272

Abstract

Illyrian iris (Iris pallida subsp. illyrica (Tomm. ex Vis.) K.Richt.) is a rhizomatous geophyte, an endemic species (subspecies), occurring within a limited range along the eastern coast of the Adriatic Sea. The study presents the first in-depth chemical and functional investigation of its rhizome extracts using both conventional and greener solvents, as well as essential oil (EO) via hydrodistillation, employing gas chromatography-mass spectrometry (GC/MS) and ultra-high-performance liquid chromatography-diode array detector-tandem mass spectrometry (UHPLC-DAD-MS/MS) for metabolic fingerprinting, which was further interpreted through a chemosensory lens. High-performance thin-layer chromatography (HPTLC) bioautography (HPTLC-DPPH/ HPTLC-Tyrosinase) was applied for the first time to this species, revealing zones of bioactivity. HaCaT cell viability and spectrophotometric assays were employed to further evaluate the cosmetic potential. Results showed a distinctive volatile profile of EO, including, to the best of our knowledge, the first identification of a silphiperfol-type sesquiterpenoid in the Illyrian iris rhizome. UHPLC-DAD-MS/MS and HPTLC fingerprinting further supported solvent-dependent differences in metabolite composition. Notably, acetone, ethyl acetate, and ethanol extracts exhibited similar chemical profiles, while greener extracts showed more divergent patterns. The results provide a foundation for the future exploration of Illyrian iris in sustainable cosmetic applications, emphasizing the need for further in vitro and in vivo validation. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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Review

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32 pages, 5367 KB

Open AccessReview

Sensors and Mass Spectrometry Connection for Food Analysis: A Systematic Review of Methodological Synergies

by Fabiola Eugelio, Marcello Mascini, Federico Fanti, Sara Palmieri and Michele Del Carlo

Chemosensors 2026, 14(4), 100; https://doi.org/10.3390/chemosensors14040100 - 20 Apr 2026

Viewed by 367

Abstract

Background: Sensors and mass spectrometry (MS) are frequently used in combination for food safety and quality assessment, yet their functional integration lacks a formal methodological framework. This review categorizes the synergies between these technologies into distinct Relational Connections. Methodology: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 155 original research articles published between 2015 and 2025 were systematically analyzed. Records were identified via the Scopus database within the food science domain. Experimental meta-data, including extraction protocols, instrumental configurations (ionization source, mass analyzer, cost tier), and chemometric strategies, were extracted to identify core methodological patterns. Statistical associations were quantified using chi-squared tests with Cramer’s V effect sizes. Results: Five Relational Connections were identified: (1) MS as reference for sensor validation (25.2%); (2) MS-sensor correlative analysis (10.3%); (3) MS quantifying data to train predictive sensor models (6.5%); (4) MS identifying targets for sensor detection (7.1%); and (5) MS enabling sensor classification models (51.0%). Technology pairing is governed by a three-level hierarchy: analyte polarity determines the ionization source (V = 0.69), required precision determines the mass analyzer (V = 0.64), and cost/availability constraints shape the practical integration strategy. Gas Chromatography (GC)-MS is predominantly coupled with Electronic Noses for volatile profiling (86% of classification studies), while Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) pairs with biosensors for contaminant analysis (74% of reference validation studies). Systematic analysis of the full pairing matrix reveals that 75% of theoretically possible MS-sensor combinations remain unexplored or underrepresented, identifying both technical boundaries and innovation frontiers. Discussion: The findings clarify the strategic logic behind technology pairings, demonstrating that MS provides the quantitative molecular data required for sensor training. The hierarchical decision framework and identification of underexplored pairings provide an evidence-based guide for designing future integrated food analysis systems. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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28 pages, 1347 KB

Open AccessReview

Bioactive Peptides from Natural Sources: Biological Functions, Therapeutic Potential and Applications

by Francisca Rodríguez-Cabello, Lyanne Rodríguez, Fanny Guzmán, Basilio Carrasco, Sigrid Sanzana, Andrés Trostchansky, Iván Palomo and Eduardo Fuentes

Chemosensors 2026, 14(2), 30; https://doi.org/10.3390/chemosensors14020030 - 27 Jan 2026

Viewed by 4400

Abstract

Natural bioactive peptides have emerged as pivotal candidates in modern science due to their multifaceted biological activities and versatile applications across biomedicine, biotechnology, and nutraceuticals. These molecules exhibit a broad pharmacological spectrum including antimicrobial, antiplatelet, antioxidant, antihypertensive, and antitumor properties, positioning them as potent therapeutic agents and essential functional food constituents. Compared to synthetic alternatives, their inherent structural diversity, biocompatibility, and biodegradability offer a superior safety profile by minimizing systemic toxicity and adverse effects. This review provides a comprehensive analysis of the primary natural reservoirs of these peptides, which encompass terrestrial flora and fauna as well as marine organisms and microorganisms, while elucidating their complex mechanisms of action and structure–function relationships. Furthermore, we evaluate contemporary methodologies for peptide identification and optimization, such as high-throughput proteomics, computational modeling, and strategic chemical modifications aimed at enhancing metabolic stability and bioavailability. Although bottlenecks in extraction, scalable production, and proteolytic susceptibility persist, recent breakthroughs in recombinant technology and rational design are facilitating their industrial translation. Finally, we discuss future perspectives focused on the synergy between artificial intelligence, nanotechnology, and sustainable circular economy strategies to maximize the therapeutic accessibility and functional efficacy of natural peptides. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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52 pages, 1966 KB

Open AccessReview

Emerging Novel Psychoactive Substances (2020–2025): GC-MS Approaches for Separation, Detection, and Characterization

by Dušan Dimić

Chemosensors 2025, 13(12), 426; https://doi.org/10.3390/chemosensors13120426 - 9 Dec 2025

Cited by 3 | Viewed by 11005

Abstract

The rapid emergence of novel psychoactive substances (NPSs) after 2020 has created one of the most dynamic analytical challenges in modern forensic science. Hundreds of new synthetic cannabinoids, synthetic cathinones, synthetic opioids, hallucinogens, and dissociatives, appearing as hybrid or structurally modified analogues of conventional drugs, have entered the illicit market, frequently found in complex polydrug mixtures. This review summarizes recent advances in gas chromatography-mass spectrometry (GC-MS) for their detection, structural elucidation, and differentiation between 2020 and 2025 based on the ScienceDirect and Google Scholar databases. Due to its reproducible electron-ionization spectra, established reference libraries, and robustness toward complex matrices, GC-MS remains the primary tool for the separation and identification of emerging NPS. The current literature highlights significant improvements in extraction and pre-concentration procedures, derivatization strategies for thermally unstable analogues, and chromatographic optimization that enable discrimination between positional and stereoisomers. This review covers a wide range of matrices, including powders, herbal materials, vaping liquids, and infused papers, as well as biological specimens such as blood, urine, and hair. Chemometric interpretation of GC-MS data now supports automated classification and prediction of fragmentation pathways, while coupling with complementary spectroscopic techniques strengthens compound confirmation. The review emphasizes how continuous innovation in GC-MS methodology has paralleled the rapid evolution of the NPS landscape, ensuring its enduring role as a reliable, adaptable, and cost-effective platform for monitoring emerging psychoactive substances in seized materials. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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35 pages, 2021 KB

Open AccessReview

From Volatile Profiling to Sensory Prediction: Recent Advances in Wine Aroma Modeling Using Chemometrics and Sensor Technologies

by Fernanda Cosme, Alice Vilela, Ivo Oliveira, Alfredo Aires, Teresa Pinto and Berta Gonçalves

Chemosensors 2025, 13(9), 337; https://doi.org/10.3390/chemosensors13090337 - 5 Sep 2025

Cited by 5 | Viewed by 8252

Abstract

Wine quality is closely linked to sensory attributes such as aroma, taste, and mouthfeel, all of which are influenced by grape variety, “terroir”, and vinification practices. Among these, aroma is particularly important for consumer preference, and it results from a complex interplay of numerous volatile compounds. Conventional sensory methods, such as descriptive analysis (DA) performed by trained panels, offer valuable insights but are often time-consuming, resource-intensive, and subject to individual variability. Recent advances in sensor technologies—including electronic nose (E-nose) and electronic tongue (E-tongue)—combined with chemometric techniques and machine learning algorithms, offer more efficient, objective, and predictive approaches to wine aroma profiling. These tools integrate analytical and sensory data to predict aromatic characteristics and quality traits across diverse wine styles. Complementary techniques, including gas chromatography (GC), near-infrared (NIR) spectroscopy, and quantitative structure–odor relationship (QSOR) modeling, when integrated with multivariate statistical methods such as partial least squares regression (PLSR) and neural networks, have shown high predictive accuracy in assessing wine aroma and quality. Such approaches facilitate real-time monitoring, strengthen quality control, and support informed decision-making in enology. However, aligning instrumental outputs with human sensory perception remains a challenge, highlighting the need for further refinement of hybrid models. This review highlights the emerging role of predictive modeling and sensor-based technologies in advancing wine aroma evaluation and quality management. Full article

(This article belongs to the Special Issue GC, MS and GC-MS Analytical Methods: Opportunities and Challenges (Fourth Edition))

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