Analysis Of Volatile Organic Compounds Research Articles

Investigation of sorptive interactions between volatile organic compounds and supramolecules at dynamic oscillation using bulk acoustic wave resonator virtual sensor arrays

Supramolecules are considered as promising materials for volatile organic compounds (VOCs) sensing applications. The proper understanding of the sorption process taking place in host-guest interactions is critical in improving the pattern recognition of supramolecules-based sensing arrays. Here, we report a novel approach to investigate the dynamic host-guest recognition process by employing a bulk acoustic wave (BAW) resonator capable of producing multiple oscillation amplitudes and simultaneously recording multiple responses to VOCs. Self-assembled monolayers (SAMs) of β-cyclodextrin (β-CD) were modified on four BAW sensors to demonstrate the gas-surface interactions regarding oscillation amplitude and SAM length. Based on the method, a virtual sensor array (VSA) type electronic nose (e-nose) can be realized by pattern recognition of multiple responses at different oscillation amplitudes of a single sensor. VOCs analysis was realized respectively by using principal component analysis (PCA) for individual VOC identification and linear discriminant analysis (LDA) for VOCs mixtures classification.

GIS-Based Analysis of Volatile Organic Compounds in Bucheon, Korea, Using Mobile Laboratory and Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry Methods.

Recently, volatile organic compounds (VOCs) have been shown to act as precursors of secondary organic particles that react with ultraviolet rays in the atmosphere and contribute to photochemical smog, global warming, odor, and human health risks, highlighting the importance of VOC management. In this study, we measured VOC concentrations in various contexts including industrial and residential areas of Bucheon, Korea, through mobile laboratory and proton-transfer-reaction time-of-flight mass spectrometry methods to determine winter VOC concentrations and visualized the data based on spatial information. Regional characteristics, temperature/humidity, atmospheric conditions, wind speed, traffic volume, etc., during the measurement period of the study site were comprehensively reviewed. For this purpose, global information system (GIS)-based air quality data and various environmental variables were comprehensively reviewed to assess spatial and temporal concentrations in three dimensions rather than in tables and graphs. Among VOCs, the levels of toluene, methanol, and n + i-butene were relatively high, with average concentrations of 48.3 ± 67.2, 34.4 ± 102.7, and 32.6 ± 57.7 ppb, respectively, at the end of the working day. The highest concentrations occurred near the Ojeong Industrial Complex. Mobile pollution sources are also a major driver of VOCs, highlighting the necessity of comprehensively reviewing traffic variables such as road level, estimated traffic volume, and average speed when identifying hotspots of air pollution. GIS-based visualization analysis techniques will improve the efficiency of air quality management.

Forensic significance of VOCs profiling in decayed ante- and post-mortem injuries by GC×GC-TOF/MS.

Accurately identifying and differentiating the types of injuries in decomposed corpses is a major challenge in forensic identification. Forensic investigations involving decomposed cadavers pose challenges in determining the cause of death. Traditional methods often lack conclusive evidence. However, the implementation of advanced analytical techniques, such as comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOF/MS), shows promise in overcoming these limitations, but the potential in this area remains limited. Therefore, this study aims to bridge this gap by exploring the potential of GC × GC-TOF/MS in the analysis of volatile organic compounds (VOCs) changes within decaying ante- and post-mortem injuries.The research emphasizes the forensic significance of VOCs changes in decomposed cadavers. We used GC × GC-TOF/MS analysis to identify the specific volatile compounds in putrefied corpse tissue samples from mice. The GC × GC-TOF/MS analysis results showed that under winter conditions, PC1 explained 57.16% of the variance, and PC2 explained 25.23% of the variance; while under summer conditions, PC1 explained 71.89% of the variance, and PC2 explained 24.49% of the variance. This demonstrates the potential of GC × GC-TOF/MS in identifying specific VOCs present in tissue samples that can serve as potential biomarkers for distinguishing between antemortem and postmortem injury. GC × GC-TOF/MS analysis revealed distinct VOC patterns in both conditions. Comprehensive use of GC × GC-TOF/MS analysis enhances accuracy in identifying and characterizing ante- and post-mortem injuries in decomposed cadavers. This study can significantly contribute to the field of forensic medicine and improve the accuracy of forensic investigations.

Characteristics and Source Analysis of Volatile Organic Compounds in Typical Ozone Pollution Processes During Summer in Jinan, China

Based on a typical ozone （O3） pollution process in Jinan City from June 16 to 26, 2021, the variation characteristics of O3 and its precursor volatile organic compounds （VOCs） during different pollution periods （clean period （CP）, pollution rise period （PRP）, heavy pollution period （HPP）, and pollution decline period （PDP）） in the urban area were analyzed. Both positive matrix factorization （PMF） and an observation-based model （OBM） were used to identify the main sources of VOCs, O3 production mechanisms, and sensitive species. The results showed that the average value of ρ（O3-8h） during the HPP period in the urban area was （246.67±11.24） μg·m-3, and ρ（O3-1h） had a peak value of 300 μg·m-3. The volume fractions of VOCs and NO2 concentration were affected by the decrease in planetary boundary layer and wind speed, which were 76.99%-145.36% and 127.78%-141.18% higher than those in the other three periods, respectively, and were the main reasons for the aggravation of O3 pollution. Alkanes, oxygenated volatile organic compounds （OVOCs）, and halogenated hydrocarbons accounted for 43.81%, 20.98%, and 17.43% of VOCs in urban areas, respectively. All of them showed significant growth during the HPP period, with acetone, propane, and ethane being the top three species by volume in each stage and isopentane showing the highest growth during the HPP period. Alkene, alkanes, and aromatic hydrocarbons accounted for 40.19%, 28.06%, and 21.92% of the ozone generation potential （OFP）. 1-butene, toluene, isopentane, and isoprene were the species with higher OFP. Isoprene had the highest OFP during the PRP phase, and 1-butene had the highest OFP during the HPP phase. The volume fraction of isopentane significantly increased OFP. The correlation coefficient between VOCs and CO preliminarily indicated that motor vehicle exhaust and oil and gas volatilization were the main sources of VOCs during the HPP period. Further use of PMF revealed that solvent use sources, combustion sources, motor vehicle exhaust+oil and gas volatilization sources, industrial emission sources, and plant sources were important sources of VOCs in urban areas. The contribution of motor vehicle exhaust+oil and gas volatilization sources in the HPP period to VOCs was 3.09-14.72 times higher than that in other periods. The contribution of solvent use sources to VOCs was approximately 2.50 times higher than that in the CP and PRP periods. The main sources of VOCs volume fraction increase were motor vehicle exhaust, oil and gas volatilization sources, and solvent use sources. Potential sources and concentration weight analysis found that VOCs were also affected by the transmission of VOCs to Binzhou and Dongying in the northeast direction. The OBM results indicated that the main pathway of O3 formation in urban areas was the reaction of peroxide hydroxyl radicals （HO2·） and methyl peroxide radicals （CH3O2·） with NO, and the net ozone generation rate during the HPP phase [P（O3）net] was 24×10-9 h-1. Based on the sensitivity experiment results, the alkene components of 1-butene, propylene, cis-2-butene, and ethylene were the dominant species for O3 production.

A New Methodology for High Spatiotemporal Resolution Measurements of Air Volatile Organic Compounds: From Sampling to Data Deconvolution.

Monitoring of volatile organic compounds (VOCs) in air is crucial for understanding their atmospheric impacts and advancing their emission reduction plans. This study presents an innovative integrated methodology suitable for achieving semireal-time high spatiotemporal resolution three-dimensional measurements of VOCs from ground to hundreds of meters above ground. The methodology integrates an active AirCore sampler, custom-designed for deployment from unmanned aerial vehicles (UAV), a proton-transfer-reaction mass spectrometry (PTR-MS) for sample analysis, and a data deconvolution algorithm for improved time resolution for measurements of multiple VOCs in air. The application of the deconvolution technique significantly improves the signal strength of data from PTR-MS analysis of AirCore samples and enhances their temporal resolution by 4 to 8 times to 4-11 s. A case study demonstrates that the methodology can achieve sample collection and analysis of VOCs within 45 min, resulting in >120-360 spatially resolved data points for each VOC measured and achieving a horizontal resolution of 20-55 m at a UAV flight speed of 5 m/s and a vertical resolution of 5 m. This methodology presents new possibilities for acquiring 3-dimensional spatial distributions of VOC concentrations, effectively tackling the longstanding challenge of characterizing three-dimensional VOC distributions in the lowest portion of the atmospheric boundary layer.

Application of fluorescent carbon quantum dots functional modified carboxymethyl cellulose film in toluene gas specific detection

With the increasing consumption of organic solvents in chemical and pharmaceutical industries, the environment pollution of volatile organic compounds (VOCs) has become an urgent problem. Therefore, the rapid-visual detection method is of great significance in the analysis of VOCs. Based on the fluorescence quenching/enhancement mechanism of carbon quantum dots (CQDs), with the help of carboxymethyl cellulose membrane with porous and large specific surface area structure, a series of green CQDs@carboxymethyl cellulose composite film (CQDs@CMC composite film) was prepared in this study. In the typical-targeted pollutants (toluene) detection application, a fluorescence spectroscopy method was established which could achieve the high sensitivity and strong specificity detection. The mainly conclusions were as follows: The fluorescence spectrometric detection method for toluene: A kind of hydrophobic Lmi/Bet CQDs@CMC composite film was prepared and characterized with imidazole/betaine CQDs and porous carboxymethyl cellulose composite film as raw materials. The toluene detection performance was studied, and the recognition mechanism was explored. The results showed that toluene enhanced the fluorescence of Lmi/Bet CQDs@CMC composite film. The fluorescence intensity of composite films was proportional to toluene concentration when the toluene concentration ranged from 200 to 2200 mg/L. The detection limit of toluene was 1.169 mg/L, which provides a theoretical basis for the detection of toluene by fluorescence spectrometry.

Multi-task deep learning model for quantitative volatile organic compounds analysis by feature fusion of electronic nose sensing

In exploring pattern recognition for electronic noses via deep neural networks, traditional networks encounter key challenges, such as low training efficiency, and neglect of spatial-temporal attributes of gas sensor response sequences. In this study, an unmanned gas-sensing test system is used to generate a large dataset to ensure robust model training. The Gramian angular field-Markov transition field is utilized to convert time sequences into images. Using advanced image processing tools, the images are then subsequently compressed with data augmentation. This method fully preserves temporal and spatial features within the sequences, thus enhancing model performance. Furthermore, the proposed multi-task learning (MTL) framework competently performs simultaneous classification and regression tasks. The primary component of the MTL network, majorly constituted of convolutional neural networks, emphasizes the spatial features of the sequences. The integration of a long short-term memory layer ensures the preservation of temporal feature analysis of the input data, thereby enhancing predictive performance. When images are compressed to only 3.9 % of the original data, substantial information can still be preserved. Subsequently, the model trained by such compressed images attains an accuracy of 95.31 % and an R2 score of 0.9510 for classification and regression tasks, respectively. Our work reveals the remarkable potential of integrating temporal and spatial features in pattern recognition, promoting the potency of multi-tasking deep learning networks in electronic nose technology.

Detection of huanglongbing infection in citrus using compositional analysis of volatile organic compounds

AbstractHuanglongbing (HLB) is caused by “Candidatus Liberibacter asiaticus” and is spread by citrus psyllids. It is a highly damaging and infectious disease of citrus for which there is no reliable treatment. Timely detection and removal of diseased trees is an effective strategy to control HLB. Various citrus cultivars infected with HLB exhibit distinct symptoms, posing a challenge to generalize HLB detection methods across multiple cultivars. Volatile organic compounds (VOCs) in HLB‐infected leaves from cultivars grown in different regions were comprehensively analysed by gas chromatography–mass spectrometry (GC‐MS) and gas chromatography‐ion mobility spectrometry (GC‐IMS), aiming to establish a new generalized HLB detection method applicable to a range of citrus cultivars. The changes in VOCs were analysed in leaves of four citrus cultivars after infection with HLB. The results showed that there was a similar response to HLB infection in different citrus cultivars, and this response was reflected in both the concentration and type of VOCs. By combining GC‐MS and GC‐IMS with orthogonal partial least squares discriminant analysis (OPLS‐DA) model, the accurate identification of the HLB infection status of different citrus cultivars was achieved, with the prediction indices reaching 0.994 for GC‐MS and 0.972 for GC‐IMS. In addition, 27 compounds were identified that were significantly different between citrus cultivars affected by HLB and healthy plants. This study provides valuable insights into the changes in VOCs in citrus cultivars after HLB infection and lays the theoretical foundation for VOC‐based HLB detection strategies.

Characterization of the volatile flavor profiles of black garlic using nanomaterial-based colorimetric sensor array, HS-SPME-GC/MS coupled with chemometrics strategies

This work investigated the feasibility of applying headspace solid phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC/MS) combining olfactory visualization for flavor characterization of black garlic. Volatile organic compounds (VOCs) analysis was performed to select important differential VOCs during black garlic processing. A multi-channels nanocomposite CSA assembled with two porous metal-organic frameworks was then developed to characterize flavor profiles changes during black garlic processing, and garlic samples during processing could be divided into five clusters, consistent with VOCs analysis. Artificial neural network (ANN) model outperformed other pattern recognition methods in discriminating processing stages. Furthermore, SVR model for odor sensory scores with the correlation coefficient for prediction set of 0.8919 exhibited a better performance than PLS model, indicating a preferable prediction ability for odor quality. This work demonstrated that the nanocomposite CSA combining appropriate chemometrics can offer an effective tool for objectively and rapidly characterizing flavor quality of black garlic or other food matrixes.

Electronic Nose and GC-MS Analysis to Detect Mango Twig Tip Dieback in Mango (Mangifera indica) and Panama Disease (TR4) in Banana (Musa acuminata)

Volatile organic compounds (VOCs), as a biological element released from plants, have been correlated with disease status. Although analysis of VOCs using GC-MS is a routine procedure, it has limitations, including being time-consuming, laboratory-based, and requiring specialist training. Electronic nose devices (E-nose) provide a portable and rapid alternative. This is the first pilot study exploring three types of commercially available E-nose to assess how accurately they could detect mango twig tip dieback and Panama disease in bananas. The devices were initially trained and validated on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated using inbuilt data analysis methods, mainly by principal component analysis (PCA) and cross-validation. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3/MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-noses are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS. The study was conducted in controlled laboratory conditions, as E-noses are highly sensitive to surrounding volatiles.

Volatile organic compounds: A promising new frontier for cancer screening

The late onset of cancer symptoms can cause a significant delay in diagnosis, impacting patients’ prognosis and quality of life, thus prompting a need for alternative screening and detection methods. Neoplastic processes cause distinct and immediate changes to the body’s metabolism, creating unique patterns in the volatile organic compounds (VOCs) produced and released through exhaled breath. For this reason, VOC profiles have emerged as diagnostic indicators for several types of malignancies, facilitating early cancer detection. Both non-invasive and accessible, the analysis of breath VOCs for cancer screening and detection has gained recognition as a new frontier in cancer diagnostics. Using exhaled breath instead of gold-standard cancer detection and screening tools that are traditionally invasive and uncomfortable for the patient could be revolutionary in improving patient compliance. Further, compared to the gold-standard tools, breath testing is relatively inexpensive, and the method of analysis, storage, and transporting the samples is simplified. Several studies have demonstrated the accuracy of VOC analysis in detecting various types of cancer, including breast cancer, colon cancer, prostate cancer, gastric cancer, and melanoma. This article summarizes the evidence supporting VOC analysis for cancer screening and detection. It reviews the clinical utility, current limitations, and necessity for standardization across all VOC screening tools to ensure the standardization and reliability of measurements. The evidence supporting breath tests to detect cancer accurately is strong, demonstrating that VOC sampling improves patient outcomes and decreases the global burden of malignant conditions by detecting cancer earlier.

Low-Cost Alternative for Online Analysis of Volatile Organic Compounds.

The use of online mass spectrometry for detecting volatile organic compounds (VOCs) has proven to be a powerful technique, allowing for real-time analysis of many chemical and biochemical processes. Unfortunately, online mass spectrometry has had limited application due to high instrument costs and limited availability. Here, we detail the design, construction, and performance characteristics of a custom ion-molecule reactor retrofitted to a commonly used single quadrupole mass spectrometer to operate as an online chemical ionization mass spectrometer (CIMS). This low-cost modified CIMS is capable of limits of detection below 10 parts per trillion for select VOCs including dimethyl sulfide, dimethylamine, and trimethylamine.

Chemical characterization of volatile organic compounds emitted by animal manure

Animal manure is considered a valuable organic fertilizer due to its important nutrient content enhancing soil fertility and plant growth in agriculture. Besides its beneficial role as fertilizer, animal manure represents a significant source of volatile organic compounds (VOCs), playing a significant role in atmospheric chemistry. Understanding the composition of VOCs Understanding VOCs from animal manure is crucial for assessing their environmental impact, as they can cause air pollution, odors, and harm to human health and ecosystems. Laboratory studies enhance field measurements by providing a precise inventory of manure emissions, addressing gaps in existing literature. Both approaches complement each other in advancing our understanding of manure emissions. In this context, we conducted an experimental study involving various animal manures (cow, horse, sheep, and goat) taken from a farm in Grignon (near Paris, France). We employed atmospheric simulation chambers within a controlled laboratory environment. The analysis of VOCs involved the combination of Proton Transfer Reaction-Quadrupole ion guide-Time-of-Flight Mass Spectrometry (PTR-QiTOF-MS) and Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS). Using PTR-QiTOF-MS, 368 compounds were detected and quantified within the manure samples. The complementary analysis by TD-GC-MS enhanced our identification of VOCs. Our findings revealed various chemical groups of VOCs, including oxygenated compounds (e.g., ethanol, cresol, acetaldehyde, etc.), nitrogenated compounds (ammonia, trimethylamine, etc.), sulfur compounds (methanethiol, dimethyl sulfide, etc.), aromatic compounds (phenols and indoles), terpenes (isoprene, D-limonene, etc.) and halogenated compounds. Cow manure exhibited the highest VOC emission fluxes, followed by goat, sheep, and horse manures. Notably, oxygenated VOCs were dominant contributors to total VOC emission fluxes in all samples. Statistical analysis highlighted the distinct nature of cow manure emissions, characterized by oxygenated compounds and nitrogenated compounds. In addition, goat manure was isolated from the other samples with high emissions of compounds having both oxygen and nitrogen atoms in their molecular formulas (e.g., CH3NO2). The experimental dataset obtained in this study provides an inventory reference for both VOCs and their emission fluxes in animal manures. Furthermore, it highlights odorant compounds and VOCs that serve as atmospheric aerosol precursor. Future studies can explore the effectiveness of various manure treatment methods to promote sustainable agriculture practices.

AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer.

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

Gas-chromatographic analyses of volatile organic compounds in essential oils extracted from Slovak juniper berries and needles (Juniperus communis L.)

Juniper essential oils (EOs) are mainly extracted from berries; however, nowadays studies point to needles as a viable source of EOs. Total qualitative-quantitative profiles of individual volatile organic compounds (VOCs) of EOs extracted from berries, old and young needles, gathered from the same locality, were obtained by GC–MS and GC–FID analyses. Moreover, qualitative-quantitative differences were determined among particular EOs within two subsequent years. Generally, VOC profiles of EOs were mainly represented by monoterpenes, sesquiterpenes and their oxygenated derivatives. Depending on the harvest year, α-pinene (27.70–35.42 %) was the most abundant VOC in juniper berry EOs, followed by germacrene D, sabinene, myrcene, germacrene B and β-pinene. Overall, needle EOs were primarily characterised by sabinene, α-pinene, γ-terpinene and terpinen-4-ol, but with an intense dependence on the needle age and harvest year. β-Ylangene and α-cubebene were specific to berry EOs, whereas trans-2-hexenal was specific to needle EOs. Discriminant analysis achieved 100 % accuracy of correctly classified EOs according to the raw material and harvest year. α-Cubebene, germacrene B, γ-muurolene, one unknown compound and trans-2-hexenal were identified as the most significant for discrimination according to the source, while juniper camphort, cis-2-p-menthen-1-ol, terpinen-4-ol and β-elemene played dominant roles in discrimination according to the harvest year.

A compact breath breathalyzer for identifying the non-alcoholic fatty liver disease biomarker

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disease worldwide. Currently, its diagnosis relies primarily on imaging and histological examinations, which are invasive and prone to misdiagnosis in the early stage. To address these limitations, detection and analysis of volatile organic compounds (VOCs) in human breath can be a rapid and non-invasive screening method for NAFLD. In this study, a compact breath breathalyzer was developed, utilizing a miniaturized gas chromatography chip with the STM32 microcontroller as the main control chip to manage airflow, temperature, and receive terminal signals from the photoionization detector. In the experiment, a gas mixture comprising five VOCs (pentane, acetone, toluene, octane, and decane) was selected as the simulated typical disease biomarkers in human breath to investigate the breathalyzer's performance and optimize testing conditions for multi-polar and wide-boiling-range breath samples. Results show that the breathalyzer can detect low-boiling components (< 100°C) such as the isoprene and acetone, with a detection limit less than 50 ppb which are two commonly biomarkers of NAFLD. Furthermore, breath samples were collected from 35 non-diseased individuals, and NAFLD early-stage patient samples were simulated by increasing the isoprene concentration by 10 ppb. Convolutional neural network (CNN) were used to identify the VOC signatures in gas chromatograms with predictive accuracy of 85% for the classification model. Therefore, the compact breath breathalyzer has potential application in the rapid and early screening of NAFLD.

The autoxidation of polyether-polyurethane open cell soft foam: An analytical aging method to reproducibly determine VOC emissions caused by thermo-oxidative degradation

We present a new method for investigating the oxidation and emission behavior of air-permeable materials. Employing this method, a differentiated statement can be made about the extent to which critical volatile organic compounds (VOCs) such as formaldehyde, acetaldehyde, and acrolein are contained in the material as impurities or formed by thermo-oxidative degradation of the polymer matrix in the use phase. The parameters affecting methods of VOC analysis are reviewed and considered for the developed method. The molecular mechanisms of VOC formation are discussed. Toxicological implications of the reaction kinetics are put into context with international guidelines and threshold levels. This new method enables manufacturers of cellular materials not only to determine the oxidative stability of their products but also to optimize them specifically for higher durability. Environmental ImplicationCellular materials are ubiquitous in the technosphere. They play a crucial role in various microenvironments such as automotive interiors, building insulation, and cushioning. These materials are susceptible to oxidative breakdown, leading to the release of formaldehyde, acetaldehyde, and acrolein. The ecotoxicological profiles of these compounds necessitate monitoring and regulation. The absence of reproducible and reliable analytical methods restricts research and development aimed at risk assessment and mitigation. This work significantly enhances the toolbox for optimizing the oxidative stability of any open-cell cellular material and evaluating these materials in terms of their temperature-dependent oxidation and emission behavior.

Analysis of VOCs in Lueyang Black Chicken Breast Meat during the Steaming Process with GC-IMS and Stoichiometry

Steamed chicken breast meat attracts people for its unique flavor and nutritional benefits. In this study, the sensory evaluation of Lueyang black chicken breast meat during steaming was first performed, and their volatile organic compounds (VOCs) were further analyzed by gas chromatography-ion mobility spectroscopy (GC-IMS) combined with stoichiometry. The sensory results demonstrated that the Lueyang black chicken breast meat steamed for 15–30 min was more acceptable in taste, flavor, and chewiness. A total of 60 volatile flavor signal peaks were obtained, and 46 VOCs were recognized from qualitative analysis, containing 24 aldehydes (51.19–72.57%), 8 ketones (10.15–16.97%), 9 alcohols (7.98-13.16%), 2 furans (2.24–10.85%), 2 esters (0.54–1.56%), and 1 ether (0.05–2.47%). A stable and reliable prediction model was set up by orthogonal partial least squares-discriminant analysis (OPLS-DA), and 18 characteristic VOCs (including 10 aldehydes, 3 alcohols, 3 ketones, 1 furan, and 1 ether) were picked out through variable importance in the projection (VIP&gt;1.0 and p&lt;0.05). Principal component analysis (PCA) results indicated that the cumulative contribution ratio was 92% with PC1 68.7% and PC2 23.3%, respectively, indicating that these characteristic VOCs could well discriminate the steaming time of Lueyang black chicken breast meat. Heatmap clustering analysis also demonstrated a similar distinguishing effect. These results could provide references for the research, development, and quality control of ready-to-eat steamed products for Lueyang black chicken breast meat in the future.

Impact of breath sample collection method and length of storage of breath samples in Tedlar bags on the level of selected volatiles assessed using gas chromatography-ion mobility spectrometry (GC-IMS)

The analysis of volatile organic compounds (VOCs) in exhaled air has attracted the interest of the scientific community because it provides the possibility of monitoring physiological and metabolic processes and non-invasive diagnostics of various diseases. However, this method remains underused in clinical practice as well as in research because of the lack of standardized procedures for the collection, storage and transport of breath samples, which would guarantee good reproducibility and comparability of results. The method of sampling, as well as the storage time of the breath samples in the polymer bags used for sample storage and transport, affect the composition and concentration of VOCs present in the breath samples. The aim of our study was to compare breath samples obtained using two methods with fully disposable equipment: a Haldane sampling tube intended for direct breath collection and breath samples exhaled into a transparent Tedlar bag. The second task was to monitor the stability of selected compounds of real breath samples stored in a Tedlar bag for 6 h. Gas chromatography coupled with ion mobility spectrometry (GC-IMS) implemented in the BreathSpec® device was used to analyse exhaled breath. Our results showed a significant difference in the signal intensity of some volatiles when taking a breath sample with a Haldane tube and a Tedlar bag. Due to its endogenous origin, acetone levels were significantly higher when the Haldane tube sampler was used while elevated levels of 2-propanol and unidentified VOC (designated as VOC 3) in the Tedlar bag samples likely originated from contamination of the Tedlar bags. The VOC stability study revealed compound-specific signal intensity changes of the selected VOCs with storage time in the Tedlar bags, with some volatiles showing increasing signal intensity during storage in Tedlar bags. This limits the use of Tedlar bags only for very limited time and carefully selected purpose. Our results highlight the importance of careful design and implementation of experiments and clinical protocols to obtain relevant and reliable results.

Rapid and Visual Favor Analysis Using Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) in Meat Products: Research Progress and Future Trends

The flavor profile of meat and its processed products is highly volatile and subject to significant changes during storage, processing, and transportation. It is therefore crucial to monitor the flavor of meat to evaluate sensory quality and protect consumer health and safety. Gas chromatography-ion mobility spectrometry (GC-IMS) has become increasingly popular due to its advantages of being nondestructive, rapid, and capable of trace detection. The analysis of volatile organic compounds (VOCs) using this technique is continuously applied in the assessment of food freshness, origin traceability, and adulteration detection. GC-IMS has emerged as a promising tool for accurate monitoring and characterization of VOCs in food, particularly in the field of meat flavor analysis. Its applications include meat product authentication, adulteration detection, processing and storage-related flavor changes, and freshness monitoring. This paper provides a comprehensive overview of the working principle of GC-IMS and its applications in meat flavor analysis, while exploring future trends and potential limitations associated with the technique.