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

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 5447

Special Issue Editors

Dr. María José Aliaño-González

E-Mail Website
Guest Editor
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

E-Mail Website
Guest Editor
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (5 papers)

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Research

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14 pages, 1756 KB  
Article
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 391
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 [...] Read more.
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  
Article
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 427
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 [...] Read more.
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  
Article
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
Viewed by 530
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 [...] Read more.
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  
Article
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
Viewed by 700
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 [...] Read more.
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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34 pages, 2021 KB  
Review
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
Viewed by 3274
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 [...] Read more.
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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