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Detection of Volatile Organic Compounds in Complex Mixtures

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A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Applied Chemical Sensors".

Deadline for manuscript submissions: closed (10 December 2025) | Viewed by 21757

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Editors

Dr. Lauryn E. DeGreeff

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Guest Editor

Department of Chemistry and Biochemistry, Global Forensic and Justice Center, Florida International University, Miami, FL 33199, USA
Interests: VOC detection; VOC mixture characterization; canine detection

Dr. Kevin Johnson

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Guest Editor

Chemistry Division, US Naval Research Laboratory, Washington, DC 20375, USA
Interests: chemometric analysis; multisensor system design; sensor array optimization and quality metrics

Special Issue Information

Dear Colleagues,

Natural olfactory systems have remarkable selectivity and sensitivity, allowing them to navigate chemical sensing tasks in a complex world. While many have tried to replicate animal olfaction for the detection of volatile organic compounds (VOCs), it is immensely challenging to replicate such selectivity. In particular, chemosensor-based systems can often exhibit disappointing performances as they move from initial testing in simplified laboratory environments to sensing tasks occurring in a more complex chemical world due to unanticipated interferences and environmental conditions.

This Special Issue of Chemosensors, entitled “Detection of Volatile Organic Compounds in Complex Mixtures”, seeks contributions that contemplate the analysis of complex mixtures or detection in a complex background. Authors are invited to submit papers that describe detection efforts in complex real-world environments or methods of analysis, detection and characterization of complex VOC mixtures. Papers may cover topics such as the use of sensor arrays, determination of target analytes from complex mixtures, novel sensing and analytical instrumentation for complex environments, and bio-, biomimetic and animal chemical sensing in real-world or complex environments.

Dr. Lauryn E. DeGreeff
Dr. Kevin Johnson
Guest Editors

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Keywords

volatile organic compounds
complex mixtures
sensor arrays
machine olfaction
sensor validation
novel analytical instrumentation
novel sensing materials

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Published Papers (7 papers)

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Research

Jump to: Review

16 pages, 3130 KB

Open AccessArticle

Fast and Non-Invasive Electronic Nose Devices for Screening Out COVID-19 Virus Infection Based on Exhaled Breath VOC Detection

by Woosuck Shin, Toshio Itoh, Yoshitake Masuda, Takehiro Kitawaki and Makoto Sawano

Chemosensors 2026, 14(1), 1; https://doi.org/10.3390/chemosensors14010001 - 19 Dec 2025

Viewed by 1396

Abstract

Current gene-based PCR diagnostics involving reverse-transcription polymerase chain reaction (RT-PCR) require at least several hours, expensive tools, and complicated sample collection methods to obtain results. A test for detecting volatile organic compounds (VOCs) in exhaled breath is advantageous as a simple, non-invasive, and fast screening method. In this study, a VOC detection system of array sensors was applied for the classification of breath control and COVID-19 virus infection. The ability to classify VOCs in the breath with COVID-19 virus infection has been studied with two metal-oxide (MOX) gas sensor arrays, commercially available sensors, and in-house sensors. The dataset of gas response signals from the array-type semiconductive gas sensors of the VOC detection system was analyzed using machine learning; principal component analysis (PCA) was used as a dimensionality-reduction method, and random forest (RF) and a convolutional neural network (CNN) were used as classification methods for the VOC concentration patterns in each breath. For the RF model, the accuracy results for the classification by two gas sensor arrays was 0.917 and this was improved by CO₂ calibration to 0.967, and the feature importance analysis revealed the importance of specific gas sensors. For the CNN, an input layer of a transformed gray-scale image with the shape of 12 data points × 8 sensors was used, and its accuracy reached 100% within a relatively small number of epochs, demonstrating a short training time, which is beneficial for breath detectors or e-nose devices. Full article

(This article belongs to the Special Issue Detection of Volatile Organic Compounds in Complex Mixtures)

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14 pages, 1123 KB

Open AccessArticle

Portable MOS Electronic Nose Screening of Virgin Olive Oils with HS-SPME-GC–MS Corroboration: Classification and Estimation of Sunflower-Oil Adulteration

by Ramiro Sánchez, Fernando Díaz and Lina Melo

Chemosensors 2025, 13(10), 374; https://doi.org/10.3390/chemosensors13100374 - 21 Oct 2025

Cited by 5 | Viewed by 1631

Abstract

Extra virgin olive oil (EVOO) can degrade during production or storage to virgin olive oil (VOO) or lampante olive oil (LOO). Fraud can also occur during commercialisation through the adulteration of EVOO (Ad-EVOO) with cheaper sunflower oil (SFO). Therefore, rapid screening techniques for quality control are needed. We evaluated an electronic nose (EN) with chemometrics—linear discriminant analysis (LDA), artificial neural-network discriminant analysis (ANN-DA), and partial least-squares regression (PLS)—in two scenarios: (i) classification into four classes (EVOO, VOO, LOO, and Ad-EVOO adulterated with 25% w/w SFO); and (ii) Ad-EVOO series containing 5–40% w/w SFO. Classes were corroborated by HS-SPME-GC-MS, with elevated (E)-2-hexenal and 3-hexen-1-ol in EVOO and increases in nonanal, ethyl acetate, and 2-propanol in deteriorated oils. Using the EN, LDA separated the classes, and ANN-DA achieved 90% accuracy under cross-validation, with the greatest confusion between VOO and LOO. In adulteration, discrimination emerged from 20% SFO, and PLS estimated %Ad-EVOO with R²pred = 0.972 (RMSEC/RMSEP = 8.059/5.627). In conclusion, the EN provides objective, rapid, and non-destructive screening that supports sensory panels and chromatographic analyses during reception and storage in industrial settings. Full article

(This article belongs to the Special Issue Detection of Volatile Organic Compounds in Complex Mixtures)

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18 pages, 1503 KB

Open AccessArticle

Investigation of Distinct Odor Profiles of Blood over Time Using Chemometrics and Detection Canine Response

by Fantasia Whaley, Valerie Albizu, Jordi Cruz, Rushali Dargan and Lauryn DeGreeff

Chemosensors 2025, 13(9), 349; https://doi.org/10.3390/chemosensors13090349 - 11 Sep 2025

Cited by 2 | Viewed by 6066

Abstract

The detection of blood by human remains detection (HRD) canines and blood detection dogs (BDDs) is crucial to both search and rescue (SAR) and crime scene investigation. They can be used to find both missing persons and to detect otherwise undetectable blood evidence at crime scenes. An added level of difficulty with training occurs as blood volatile organic compounds (VOCs) are drastically affected by time. Previous studies have shown this, with a focus on a longer timescale (weeks/months). Little data exists on the changes in the first 48 h, the most crucial time in SAR, something this study aims to rectify. Data was collected using headspace solid-phase microextraction/gas chromatography–mass spectrometry, which was then analyzed using chemometrics and confirmed with canine trials. The results of the laboratory analysis indicated that there were multiple, distinct odor profiles between the 1 h and 2-week time windows—namely, the fresh, intermediate, and aged stages of decomposition. The noted changes in the odor profiles were validated with HRD canine trials. Canines had difficulty detecting the fresh blood (1–2 h old) and had the greatest detection rate for the aged blood (34–36 h old). Both the chemical analysis and canine behavior data displayed a clear change in the odor profile within the first 48 h. This information will assist SAR, HRD, and BBD training to ensure they train on all distinct odor profiles. Full article

(This article belongs to the Special Issue Detection of Volatile Organic Compounds in Complex Mixtures)

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14 pages, 681 KB

Open AccessArticle

Breathprint-Based Endotyping of COPD and Bronchiectasis COPD Overlap Using Electronic Nose Technology: A Prospective Observational Study

by Vitaliano Nicola Quaranta, Mariafrancesca Grimaldi, Silvano Dragonieri, Alessio Marinelli, Andrea Portacci, Maria Rosaria Vulpi and Giovanna Elisiana Carpagnano

Chemosensors 2025, 13(8), 311; https://doi.org/10.3390/chemosensors13080311 - 16 Aug 2025

Cited by 2 | Viewed by 1678

Abstract

Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with multiple clinical and inflammatory phenotypes. The coexistence of bronchiectasis, known as bronchiectasis–COPD overlap (BCO), identifies a subgroup with increased morbidity and mortality. Non-invasive breath analysis using electronic noses (e-noses) has shown promise in identifying disease-specific volatile organic compound (VOC) patterns (“breathprints”). Our aim was to evaluate the ability of an e-nose to differentiate between COPD and BCO patients, and to assess its utility in detecting inflammatory endotypes (neutrophilic vs. eosinophilic). In a monocentric, prospective, real-life study, 98 patients were enrolled over nine months. Forty-two patients had radiologically confirmed BCO, while fifty-six had COPD without bronchiectasis. Exhaled breath samples were analyzed using the Cyranose 320 e-nose. Principal component analysis (PCA) and discriminant analysis were used to identify group-specific breathprints and inflammatory profiles. PCA revealed significant breathprint differences between BCO and COPD (p = 0.021). Discriminant analysis yielded an overall accuracy of 69.6% (AUC 0.768, p = 0.037). The highest classification performance (76.8%) was achieved when distinguishing eosinophilic COPD from neutrophilic BCO. These findings suggest distinct inflammatory profiles that may be captured non-invasively. E-nose technology holds potential for the non-invasive endotyping of COPD, especially in identifying neutrophilic BCO as a unique inflammatory entity. Breathomics may support early, personalized treatment strategies. Full article

(This article belongs to the Special Issue Detection of Volatile Organic Compounds in Complex Mixtures)

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11 pages, 1227 KB

Open AccessArticle

Sampling and Comparison of Extraction Techniques Coupled with Gas Chromatography–Mass Spectrometry (GC-MS) for the Analysis of Substrates Exposed to Explosives

by Himanshi Upadhyaya, Alexis J. Hecker and John V. Goodpaster

Chemosensors 2024, 12(12), 251; https://doi.org/10.3390/chemosensors12120251 - 29 Nov 2024

Cited by 4 | Viewed by 3425

Abstract

Explosive-detecting canines (EDCs) show high sensitivity in detecting explosives that they are trained to detect. The ability of canines to detect explosive residues to the parts per trillion level can sometimes result in nuisance alerts. These nuisance alerts can occur when various materials (i.e., substrates) are exposed to volatile organic compounds (VOCs) present in explosive mixtures, leading to contamination—the unintended absorption or adsorption of VOCs by the substrate. Chemical constituents such as taggant, plasticizer, and residual solvent in explosives are often composed of VOCs that canines are trained on to detect explosives. Composition C-4 (C4) is a common explosive that EDCs are trained to detect and hence is this study’s focus. Common VOCs of interest emitted from C4 include 2,3-dimethyl-2,3-dinitrobutane (DMNB), 2-ethyl-1 hexanol (2E1H), and cyclohexanone. In this study, we developed a protocol for comparing different substrates such as cotton, cardboard, wood, sheet metal, and glass that were exposed to volatiles from C4. 1-bromooctane (1-BO) was used as a single-odor compound to compare the complex odor originating from C4. Triplicates of substrates such as cotton, wood, cardboard, sheet metal, and glass were exposed to 1 g of C4 in a paint can for one week and the substrates were then extracted using various extraction methods such as liquid injection, direct SPME, and headspace analysis coupled with gas chromatography–mass spectrometry. An extraction time study was performed to determine the optimal extraction time for SPME analysis, and it was found to be 20 min. Comparison of extraction methods revealed that SPME surpassed other techniques as DMNB was found on all substrates using SPME. It was observed that porous substrates such as wood and cardboard have a higher retention capacity for volatiles in comparison to non-porous substrates such as sheet metal and glass. Finally, swabbing was evaluated as a sampling technique for the substrates of interest and the extracts were analyzed using the total vaporization–solid phase microextraction (TV-SPME) technique. No volatiles associated with C4 were identified on conducting a GC-MS analysis, suggesting that swabbing is not an ideal technique for analysis of substrates exposed to C4. Full article

(This article belongs to the Special Issue Detection of Volatile Organic Compounds in Complex Mixtures)

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Review

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41 pages, 9730 KB

Open AccessReview

In-Vehicle Gas Sensing and Monitoring Using Electronic Noses Based on Metal Oxide Semiconductor MEMS Sensor Arrays: A Critical Review

by Xu Lin, Ruiqin Tan, Wenfeng Shen, Dawu Lv and Weijie Song

Chemosensors 2026, 14(1), 16; https://doi.org/10.3390/chemosensors14010016 - 4 Jan 2026

Cited by 1 | Viewed by 3011

Abstract

Volatile organic compounds (VOCs) released from automotive interior materials and exchanged with external air seriously compromise cabin air quality and pose health risks to occupants. Electronic noses (E-noses) based on metal oxide semiconductor (MOS) micro-electro-mechanical system (MEMS) sensor arrays provide an efficient, real-time solution for in-vehicle gas monitoring. This review examines the use of SnO₂-, ZnO-, and TiO₂-based MEMS sensor arrays for this purpose. The sensing mechanisms, performance characteristics, and current limitations of these core materials are critically analyzed. Key MEMS fabrication techniques, including magnetron sputtering, chemical vapor deposition, and atomic layer deposition, are presented. Commonly employed pattern recognition algorithms—principal component analysis (PCA), support vector machines (SVM), and artificial neural networks (ANN)—are evaluated in terms of principle and effectiveness. Recent advances in low-power, portable E-nose systems for detecting formaldehyde, benzene, toluene, and other target analytes inside vehicles are highlighted. Future directions, including circuit–algorithm co-optimization, enhanced portability, and neuromorphic computing integration, are discussed. MOS MEMS E-noses effectively overcome the drawbacks of conventional analytical methods and are poised for widespread adoption in automotive air-quality management. Full article

(This article belongs to the Special Issue Detection of Volatile Organic Compounds in Complex Mixtures)

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28 pages, 2438 KB

Open AccessReview

MOF-Derived Catalytic Interfaces for Low-Temperature Chemiresistive VOC Sensing in Complex Backgrounds

by Lu Zhang, Shichao Zhao, Jiangwei Zhu and Li Fu

Chemosensors 2025, 13(11), 386; https://doi.org/10.3390/chemosensors13110386 - 3 Nov 2025

Cited by 5 | Viewed by 2728

Abstract

The detection of volatile organic compounds (VOCs) at low operating temperatures is critical for public health, environmental monitoring, and industrial safety, yet it remains a significant challenge for conventional sensor technologies. Metal-organic frameworks (MOFs) have emerged as highly versatile precursors for creating advanced sensing materials. This review critically examines the transformation of MOFs into functional catalytic interfaces for low-temperature chemiresistive VOC sensing. We survey the key synthetic strategies, with a focus on controlled pyrolysis, that enable the conversion of insulating MOF precursors into semiconducting derivatives with tailored porosity, morphology, and catalytically active sites. This review establishes the crucial synthesis-structure-performance relationships that govern sensing behavior, analyzing how factors like calcination temperature and precursor composition dictate the final material’s properties. We delve into the underlying chemiresistive sensing mechanisms, supported by evidence from advanced characterization techniques such as in situ DRIFTS and density functional theory (DFT) calculations, which elucidate the role of oxygen vacancies and heterojunctions in enhancing low-temperature catalytic activity. A central focus is placed on the persistent challenges of achieving high selectivity and robust performance in complex, real-world environments. We critically evaluate and compare strategies to mitigate interference from confounding gases and ambient humidity, including intrinsic material design and extrinsic system-level solutions like sensor arrays coupled with machine learning. Finally, this review synthesizes the current state of the art, identifies key bottlenecks related to stability and scalability, and provides a forward-looking perspective on emerging frontiers, including novel device architectures and computational co-design, to guide the future development of practical MOF-derived VOC sensors. Full article

(This article belongs to the Special Issue Detection of Volatile Organic Compounds in Complex Mixtures)

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