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Separations
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State of the Art and Challenges in the Analysis of Volatile...
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State of the Art and Challenges in the Analysis of Volatile Organic Compounds

A special issue of Separations (ISSN 2297-8739). This special issue belongs to the section "Chromatographic Separations".

Deadline for manuscript submissions: 10 November 2025 | Viewed by 1091

Special Issue Editors

Prof. Dr. Jérôme Vial

E-Mail Website
Guest Editor
Unité Mixte de Recherche Chimie Biologie Innovation (UMR CBI), Laboratoire des Sciences Analytiques, Bioanalytiques et Miniaturisation, Ecole Supérieure de Physique et de Chimie Industrielle de la ville de Paris (ESPCI Paris), PSL Research University, 10 rue Vauquelin, 75005 Paris, France
Interests: gas chromatography-mass dpectrometry; comprehensive bidimensionnal GCxGC; GCxGC MS; thermodesorption; chemometrics; renewable gases; pyrolisis oils; scent sampling; volatolomic
Dr. Sylvain Antoniotti

E-Mail Website
Guest Editor
CNRS, Institut de Chimie de Nice, Université Cote d’Azur. Parc Valrose, 06108 Nice CEDEX 2, France
Interests: GC-MS; natural complex substances; flavour and fragrance; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The analysis of Volatile Organic Compounds (VOCs) is pivotal in numerous fields. Without claiming to be exhaustive, we can mention perfumery and cosmetics, air quality control, odour assessment, volatolomics, renewable energy with biogazes or non-fossile fuels, food quality, and non-invasive medical diagnostics.

Key techniques include one-dimensional (GC) and two-dimensional gas chromatography (GCxGC), often coupled with mass spectrometry (MS). Sampling and injection methods are critical for accurate VOC analysis. Liquid injection, headspace techniques, and solid-phase micro-extraction (SPME) and its variants are commonly employed to capture these compounds. High-resolution mass spectrometry (HRMS) offers additionnal insights in identifying and quantifying VOCs.

The data analysis and visualisation of results for complex sets are integral parts of VOC analysis, where chemometric tools are used to process and interpret the complex data generated. These tools facilitate pattern recognition and component identification, leading to the improved discimination of complex samples and the potential indentification of specific markers.

Therefore, this Special Issue aims to consolidate and disseminate knowledge in the field. We invite you to contribute a research article, communication, or review related to VOCs analysis and its applications.

Prof. Dr. Jérôme Vial
Dr. Sylvain Antoniotti
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. Separations 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 2600 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

  • GC-MS
  • GCxGC
  • headpsace techniques
  • chemometrics
  • volatolomic
  • scent sampling
  • aroma
  • thermodeorption

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Published Papers (2 papers)

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18 pages, 1972 KB  
Article
Characterization of Pyrolysis Oils Using a Combination of GC×GC/TOFMS and GC/HRMS Analysis: The Impact of Data Processing Parameters
by Xiangdong Chen, Carlos Rincon, Benoît Gadenne, José Dugay, Michel Sablier and Jérôme Vial
Separations 2025, 12(9), 239; https://doi.org/10.3390/separations12090239 - 4 Sep 2025
Viewed by 413
Abstract
Human population growth and increasing transportation demands have led to rising global tire consumption and associated waste. In response, various material and energy recovery strategies, such as pyrolysis, have been developed to produce high-value-added products such as pyrolysis oils, which can be reused [...] Read more.
Human population growth and increasing transportation demands have led to rising global tire consumption and associated waste. In response, various material and energy recovery strategies, such as pyrolysis, have been developed to produce high-value-added products such as pyrolysis oils, which can be reused as materials or fuels. However, these oils often contain heteroatom-containing compounds (e.g., nitrogen, oxygen, sulfur) that can hinder their valorization and must therefore be identified and removed. To characterize heteroatomic compounds present in distillation fractions of pyrolysis oils, GC×GC/TOFMS and GC/HRMS were employed. For non-target analysis, data processing parameters were optimized using a Central Composite Design (CCD). The most influential parameters for GC×GC/TOFMS were the minimum number of mass-to-charge ratio (m/z) signals kept in the deconvoluted spectra (minimum stick count) and peak signal-to-noise ratio (S/N), while for GC/HRMS, optimization focused on the m/z S/N threshold, peak S/N, and total ion current (TIC). Under optimal conditions, 129 and 92 heteroatomic compounds were identified via GC×GC/TOFMS and GC/HRMS, respectively, within a single distillation fraction, with 57 compounds identified using both techniques. Notably, GC×GC/TOFMS exclusively identified 72 compounds, while there were only 5 unique to GC/HRMS. These results highlight the effectiveness of GC×GC/TOFMS in characterizing heteroatomic compounds in complex mixtures, while also underlining the complementary value of GC/HRMS. Full article
(This article belongs to the Special Issue State of the Art and Challenges in the Analysis of Volatile Organic Compounds)
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11 pages, 1070 KB  
Article
Directed Message-Passing Neural Networks for Gas Chromatography
by Daniel Struk, Rizky Ilhamsyah, Jean-Marie D. Dimandja and Peter J. Hesketh
Separations 2025, 12(8), 200; https://doi.org/10.3390/separations12080200 - 30 Jul 2025
Viewed by 430
Abstract
In this paper, the directed message-passing neural network architecture is used to predict several quantities of interest in gas chromatography: retention times, Clarke-Glew 3-point thermodynamic parameters for simulation, and retention indices. The retention index model was trained with 48,803 training samples and reached [...] Read more.
In this paper, the directed message-passing neural network architecture is used to predict several quantities of interest in gas chromatography: retention times, Clarke-Glew 3-point thermodynamic parameters for simulation, and retention indices. The retention index model was trained with 48,803 training samples and reached 1.9–2.6% accuracy, whereas the thermodynamic parameters and retention time were trained by using 230 training data samples yielding 17% accuracy. Furthermore, the accuracy as a function of the number of training samples is investigated, showing the necessity of large, accurate datasets for training deep learning-based models. Lastly, several uses of such a model for the identification of compounds and the optimization of GC parameters are discussed. Full article
(This article belongs to the Special Issue State of the Art and Challenges in the Analysis of Volatile Organic Compounds)
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