Identification Of Volatile Organic Compounds Research Articles

The application of PTR-MS and non-targeted analysis to characterize VOCs emitted from a plastic recycling facility fire.

On April 11th, 2023, the My Way Trading (MWT) recycling facility in Richmond, Indiana caught fire, mandating the evacuation of local residents and necessitating the U.S. Environmental Protection Agency (EPA) to conduct air monitoring. The EPA detected elevated levels of plastic combustion-related air pollutants, including hydrogen cyanide and benzene. We aimed to identify these and other volatile organic compounds (VOCs) present as well as to identify the potential hazard of each compound for various human health effects. To identify the VOCs, we conducted air monitoring at sites within and bordering the evacuation zone using proton transfer reaction mass spectrometry (PTR-MS) and non-targeted analysis (NTA). To facilitate risk assessment of the emitted VOCs, we used the EPA Hazard Comparison Dashboard. We identified 46 VOCs, within and outside the evacuation zone, with average detection levels above local background levels measured in Middletown, OH. Levels of hydrogen cyanide and 4 other VOCs were at least 1.8-fold higher near the incidence site in comparison to background levels and displayed unique temporal and spatial patterns. The 46 VOCs identified had the highest hazardous potential for eye and skin irritation, with approximately 45% and 39%, respectively, of the VOCs classified as high and very high hazards for these endpoints. Notably, all detected VOC levels were below the hazard thresholds set for single VOC exposures; however, hazard thresholds for exposure to VOC mixtures are currently unclear. This study serves as a proof-of-concept that PTR-MS coupled with NTA can facilitate rapid identification and hazard assessment of VOCs emitted following anthropogenic disasters. Furthermore, it demonstrates that this approach may augment future disaster responses to quantify additional VOCs present in complex combustion mixtures.

Discrimination and screening of volatile metabolites in atractylodis rhizoma from different varieties using headspace solid-phase microextraction-gas chromatography-mass spectrometry and headspace gas chromatography-ion mobility spectrometry, and ultra-fast gas chromatography electronic nose

Atractylodis rhizoma is a common bulk medicinal material with multiple species. Although different varieties of atractylodis rhizoma exhibit variations in their chemical constituents and pharmacological activities, they have not been adequately distinguished due to their similar morphological features. Hence, the purpose of this research is to analyze and characterize the volatile organic compounds (VOCs) in samples of atractylodis rhizoma using multiple techniques and to identify the key differential VOCs among different varieties of atractylodis rhizoma for effective discrimination. The identification of VOCs was carried out using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC–MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), resulting in the identification of 60 and 53 VOCs, respectively. The orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to screen potential biomarkers and based on the variable importance in projection (VIP ≥ 1.2), 24 VOCs were identified as critical differential compounds. Random forest (RF), K-nearest neighbor (KNN) and back propagation neural network based on genetic algorithm (GA-BPNN) models based on potential volatile markers realized the greater than 90 % discriminant accuracies, which indicates that the obtained key differential VOCs are reliable. At the same time, the aroma characteristics of atractylodis rhizoma were also analyzed by ultra-fast gas chromatography electronic nose (Ultra-fast GC E-nose). This study indicated that the integration of HS-SPME-GC–MS, HS-GC-IMS and ultra-fast GC E-nose with chemometrics can comprehensively reflect the differences of VOCs in atractylodis rhizoma samples from different varieties, which will be a prospective tool for variety discrimination of atractylodis rhizoma.

Integration of nontarget analysis with machine learning modeling for prioritization of odorous volatile organic compounds in surface water

Assessing the odor risk caused by volatile organic compounds (VOCs) in water has been a big challenge for water quality evaluation due to the abundance of odorants in water and the inherent difficulty in obtaining the corresponding odor sensory attributes. Here, a novel odor risk assessment approach has been established, incorporating nontarget screening for odorous VOC identification and machine learning (ML) modeling for odor threshold prediction. Twenty-nine odorous VOCs were identified using two-dimensional gas chromatography-time of flight mass spectrometry from four surface water sampling sites. These identified odorants primarily fell into the categories of ketones and ethers, and originated mainly from biological production. To obtain the odor threshold of these odorants, we trained an ML model for odor threshold prediction, which displayed good performance with accuracy of 79%. Further, an odor threshold-based prioritization approach was developed to rank the identified odorants. 2-Methylisoborneol and nonanal were identified as the main odorants contributing to water odor issues at the four sampling sites. This study provides an accessible method for accurate and quick determination of key odorants in source water, aiding in odor control and improved water quality management. Environmental ImplicationWater odor episodes have been persistent and significant issues worldwide, posing severe challenges to water treatment plants. Unpleasant odors in aquatic environments are predominantly caused by the occurrence of a wide range of volatile organic chemicals (VOCs). Given the vast number of newly-detected VOCs, experimental identification of the key odorants becomes difficult, making water odor issues complex to control. Herein, we propose a novel approach integrating nontarget analysis with machine learning models to accurate and quick determine the key odorants in waterbodies. We use the approach to analyze four samples with odor issues in Changsha, and prioritized the potential odorants.

Identification of volatile organic compound of oil palm plants infected with Ganoderma sp.

The success of controlling basal stem rot (BSR) disease caused by Ganoderma sp. is mostly determined by the early detection of the fungi. Hence, when the signs of infection begin to appear, plants are generally difficult to rescue since the pathogen infection has spread widely to all parts of the plant. Therefore, early detection through the application of biosensors for Ganoderma sp. infection is absolutely necessary. This study aim was to identify biomarker compounds of Ganoderma sp. infection in oil palm plants with GC-MS for volatile compounds, and LC-MS for non-volatile compounds. The results showed that Ganoderma sp. mycelium produced pyrimidinamine compounds. Meanwhile in early infected nursery plants, were found benzo[h]quinoline, hexaoxa-7,9,11-trisilaheptad, tris-(trimethyl-silyl ester and methyl-tris(trimethyl-siloxy)-silane). Whereas in Bekri, Rejosari, and Adolina plantation, the compound methyl-tris(trimethyl-siloxy)-silane was also found in healthy mature plants. GC-MS test results showed that for early, moderate and severe plants produced several benzene derivative compounds such as ethylbenzene, xylene, and benzaldehyde. These compounds were assumed to be resulted from the breakdown of the lignin structure which build plant cell walls, and have potency to be used as marker compounds for early infected Ganoderma sp. detection. The result of the produced gas quantification concluded that in the produced oil palm plants released less CO2 compared to healthy plants. On the other hand, the NH3 produced was higher than the healthy plants. Meanwhile, two non-volatile compounds were found that they were only produced by infected trunk and root tissue, namely pseudobrucine and picrasidine.

Effects of psychological stress on the emission of volatile organic compounds from the skin

Thirty-five women were included in a clinical study to characterize the volatile organic compounds (VOCs) emitted by the skin during exposure to psychological stress. An original silicon-based polymeric phase was used for VOC sampling on the forehead before and after stress induction. Cognitive stress was induced using specialized software that included a chronometer for semantic and arithmetic tasks. Assessment of stress was monitored using a State-trait anxiety inventory questionnaire, analysis of participants’ verbal expressions and clinical measurements. Identification and relative quantification of VOCs were performed by gas chromatography-mass spectrometry. Stress induction was validated by a significant increase in state-anxiety as indicated by the questionnaire, modifications in electrodermal activity measurements and the expression of stress verbatims. In parallel, a sebum production increase and a skin pH decrease were observed. A total of 198 VOCs with different potential sources were identified. They were categorized in 5 groups: probable cosmetic composition, VOCs produced by the body or its microbiota, environmental origin, and dietary intake. In our qualitative statistical approach, three VOCs were found to be correlated with stress induction and 14 compounds showed significance in the paired Wilcoxon test. Fatty-acyls derived from lipids were predominantly identified as well as ethylbenzenes.

Exhaled breath analyses for bronchial thermoplasty in severe asthma patients

BackgroundBronchial thermoplasty (BT) is a bronchoscopic treatment for severe asthma. Although multiple trials have demonstrated clinical improvement after BT, optimal patient selection remains a challenge and the mechanism of action is incompletely understood.The aim of this study was to examine whether exhaled breath analysis can contribute to discriminate between BT-responders and non-responders at baseline and to explore pathophysiological insights of BT. MethodsExhaled breath was collected from patients at baseline and six months post-BT. Patients were defined as responders or non-responders based on a half point increase in asthma quality of life questionnaire scores. Gas chromatography-mass spectrometry was used for volatile organic compounds (VOCs) detection and analyses. Analytical workflow consisted of: 1) detection of VOCs that differentiate between responders and non-responders and those that differ between baseline and six months post-BT, 2) identification of VOCs of interest and 3) explore correlations between clinical biomarkers and VOCs. ResultsData was available from 14 patients. Nonanal, 2-ethylhexanol and 3-thujol showed a significant difference in intensity between responders and non-responders at baseline (p = 0.04, p = 0.01 and p = 0.03, respectively). After BT, no difference was found in the compound intensity of these VOCs. A negative correlation was observed between nonanal and IgE and BALF eosinophils (r = −0.68, p < 0.01 and r = −0.61, p = 0.02 respectively) and 3-thujol with BALF neutrophils (r = −0.54, p = 0.04). ConclusionsThis explorative study identified discriminative VOCs in exhaled breath between BT responders and non-responders at baseline. Additionally, correlations were found between VOC's and inflammatory BALF cells. Once validated, these findings encourage research in breath analysis as a non-invasive easy to apply technique for identifying airway inflammatory profiles and eligibility for BT or immunotherapies in severe asthma.

Hybrid plasmonic aerogel with tunable hierarchical pores for size-selective multiplexed detection of VOCs with ultrahigh sensitivity

Sensitive and rapid identification of volatile organic compounds (VOCs) at ppm level with complex composition is vital in various fields ranging from respiratory diagnosis to environmental safety. Herein, we demonstrate a SERS gas sensor with size-selective and multiplexed identification capabilities for VOCs by executing the pre-enrichment strategy. In particular, the macro-mesoporous structure of graphene aerogel and micropores of metal-organic frameworks (MOFs) significantly improved the enrichment capacity (1.68 mmol/g for toluene) of various VOCs near the plasmonic hotspots. On the other hand, molecular MOFs-based filters with different pore sizes could be realized by adjusting the ligands to exclude undesired interfering molecules in various detection environments. Combining these merits, graphene/AuNPs@ZIF-8 aerogel gas sensor exhibited outstanding label-free sensitivity (up to 0.1 ppm toluene) and high stability (RSD=14.8%, after 45 days storage at room temperature for 10 cycles) and allowed simultaneous identification of multiple VOCs in a single SERS measurement with high accuracy (error < 7.2%). We visualize that this work will tackle the dilemma between sensitivity and detection efficiency of gas sensors and will inspire the design of next-generation SERS technology for selective and multiplexed detection of VOCs.

Identification of candidate genes associated with host-seeking behavior in the parasitoid wasp Diachasmimorpha longicaudata

BackgroundDiachasmimorpha longicaudata is a hymenopteran fruit fly endoparasitoid. Females of this species find their hosts for oviposition by using complex sensorial mechanisms in response to physical and chemical stimuli associated with the host and host habitat. Ecological and behavioral aspects related to host-seeking behavior for oviposition have been extensively studied in D. longicaudata, including the identification of volatile organic compounds acting as attractants to females. In this sense, molecular mechanisms of chemoreception have been explored in this species, including a preliminary characterization of odorant-binding proteins (OBPs), chemosensory proteins (CSPs) and odorant receptors (ORs), among other proteins. Functional assays on OBP and CSP have been conducted as a first approach to identify molecular mechanisms associated with the female host-seeking behavior for oviposition. The aims of the present study were to identify the D. longicaudata sensory gene repertoire expressed in the antenna of sexually mature and mated individuals of both sexes, and subsequently, characterize transcripts differentially expressed in the antennae of females to identify candidate genes associated with the female host-seeking behavior for oviposition.ResultsA total of 33,745 predicted protein-coding sequences were obtained from a de novo antennal transcriptome assembly. Ten sensory-related gene families were annotated as follows: 222 ORs, 44 ionotropic receptors (IRs), 25 gustatory receptors (GRs), 9 CSPs, 13 OBPs, 2 ammonium transporters (AMTs), 8 pickpocket (PPKs) receptors, 16 transient receptor potential (TRP) channels, 12 CD36/SNMPs and 3 Niemann-Pick type C2 like proteins (NPC2-like). The differential expression analysis revealed 237 and 151 transcripts up- and downregulated, respectively, between the female and male antennae. Ninety-seven differentially expressed transcripts corresponded to sensory-related genes including 88 transcripts being upregulated (87 ORs and one TRP) and nine downregulated (six ORs, two CSPs and one OBP) in females compared to males.ConclusionsThe sensory gene repertoire of D. longicaudata was similar to that of other taxonomically related parasitoid wasps. We identified a high number of ORs upregulated in the female antenna. These results may indicate that this gene family has a central role in the chemoreception of sexually mature females during the search for hosts and host habitats for reproductive purposes.

Hydrophobic MOF/PDMS-Based QCM Sensors for VOCs Identification and Quantitative Detection in High-Humidity Environments.

Metal-organic frameworks (MOFs) have great potential in quartz crystal microbalance (QCM) platforms for volatile organic compound (VOCs) detection and recognition due to their unique properties. However, the MOFs' hydrophilicity degrades performance in high-humidity environments, limiting reliable VOC sensing in complex environments. Herein, we propose a novel VOC virtual sensor array (VSA) using a single QCM sensor with an adsorption layer composed of MIL-101(Cr) MOF and polydimethylsiloxane (PDMS), realizing stable sensing and accurate identification for different VOCs under various relative humidity (RH) conditions. The hydrophobic PDMS layer improves the moisture resistance of the sensor to 4 and 14 times in terms of shifts in resonant frequency and scattering parameters, respectively. In addition, performance is maintained over 2 days of water treatment, demonstrating superior water resistance. The highest sensitivity of 2.68 mdB ppm-1 is achieved for isopropanol detection, with the lowest limit of detection of 20.06 ppm for acetone. Combining resonant signals and lumped parameters, the proposed VSA technique effectively discriminates four VOCs (ethanol, 2-propanol, acetone, and acetonitrile) with a high accuracy of 95.3% under both 60% and 90% RH backgrounds. The studies provide a promising solution for reliable low-concentration VOC detection using QCM sensors in high-humidity environments such as underground spaces.

Visual Recognition of Volatile Organic Compounds by Photonic Nose Integrated with Multiple Metal-Organic Frameworks.

The photonic nose inspired by the olfactory system is an integrated detection platform constructed by multiple sensing units as channels. However, in the detection of volatile organic compounds (VOCs), the sensing results that cannot be directly readable and the poor ability to distinguish analytes with similar chemical properties are the main challenges faced by this sensor. Here, 8 metal-organic frameworks (MOF)-based photonic crystals are used as the basic sensing units to construct a photonic nose detection platform. The microscopic adsorption of VOCs by MOFs enables the photonic crystals (PCs) to produce macroscopic structural color output, and further makes the photonic nose have specific color fingerprints for different VOCs, the response time of all PCs to VOCs can be within 1s. Through the color fingerprint, the visual identification of VOCs produced by 5 common solvent vapors is realized, and 9 VOCs with similar chemical properties are further distinguished. In addition, the application potential of the photonic nose in the actual environment is verified by identifying different contents of benzene in the paint. It is envisaged that the MOF-based photonic nose has great reference value for the development of intelligent and multi-component synergistic functional gas sensors.

Intelligent electrochemical sensors for precise identification of volatile organic compounds enabled by neural network analysis

Intelligent electrochemical sensors for precise identification of volatile organic compounds enabled by neural network analysis

Tuning phosphorene and MoS2 2D materials for detecting volatile organic compounds associated with respiratory diseases.

Efficient identification of volatile organic compounds (VOCs) is essential for the rapid diagnostication of respiratory diseases. By detecting specific biomarkers associated with different pathologies one may distinguish between tuberculosis, nosocomial pneumonia, Aspergillus fumigatus, influenza and SARS-CoV-2 virus infections. Phosphorene and MoS2 are potential candidates from the class of 2D graphene-like materials, which can be used as active layers for sensing elements. However, as the target molecules poorly adhere to the pristine layers, binding centers are created by introducing substitutional impurities. The adsorbed VOCs induce modifications in the electrical properties of the customized active layers. For each biomarker and a sequence of substitutional impurities, a pattern of conductivities is obtained, which enables the detection of an unknown test specimen. Exploring multiple biosensor configurations we find an optimal design yielding a considerable selectivity for the five biomarker compounds.

Applying HS-SPME-GC-MS combined with PTR-TOF-MS to analyze the volatile compounds in coffee husks of Coffea arabica with different primary processing treatments in Yunnan

Primary processing treatments are crucial to the flavor profile of coffee beans, which create the flavor precursors basis in coffee husks and diffuse into the beans, with satisfying outcomes. In this study, eleven coffee husks of Coffea arabica with different primary processing treatments (sun exposure, washing and honey treatment) from three latitudes of Yunnan were investigated by the combination of HS-SPME-GC-MS and PTR-TOF-MS. A total of 114 volatile organic compounds (VOCs) and 77 masses were identified. The characteristic compounds octanoic acid (S1) and methyl linoleate (S11) in sun-exposed, phenethyl alcohol (F4), methyl salicylate (F7), 3,5-dimethylbenzaldehyde (F9) and phytone (T9) in washed and n-hexadecanoic acid (S9) of honey-treated husks were recognized by the ions at m/z 33.032, m/z 68.049, m/z 83.049, m/z 67.054, m/z 57.034, m/z 41.030 and m/z 49.010, respectively. This study demonstrated the feasibility and complementarity of HS-SPME-GC-MS and PTR-TOF-MS in the rapid identification of VOCs in coffee husks. Meanwhile, the antioxidant capacities of the 11 husks were evaluated in vitro, and the sun-exposed samples exhibited most promising potency, in which methyl salicylate (F7), methyl linoleate (S11), methyl linolenate (S13), ethyl linoleate (S14), ethyl linolenate (S16), linalool (T1), citronellol (T2) and caffeine (J3) contributed mainly to the antioxidant capacities.

Optimization and validation for identification of volatile organic com-pounds released from Trogoderma variabile Ballion using headspace solid-phase microextraction and GC-FID/MS

Volatile organic compounds (VOC) emitted by Trogoderma variabile at different life stages (larvae, female and male) can help us to understand the chemical signals that are released by the beetle which can serve as biomarkers for diagnostic purpose. There are several factors that effect on the optimization of VOC extraction including, temperature, jar size, number of insects, duration of extraction, and gas chromatography (GC) conditions. This study used headspace solid phase microextraction (HS-SPME) fibre coupled with flame ionization detection (FID) and gas chromatography with mass spectrometry (GC-MS) to determine the optimal method for accurate, rapid and cost-effective extraction and identification of VOC from different life stages of T. variabile. The HS-SPME technique and the analytical conditions with GC and GCMS were optimized and validated for the determination of VOCs released from T. variabile. Selection of the number of insects was based on the height and the number of peaks. Results showed that 15 and 20 larvae and adults respectively gave the best number of peaks. Sixteen hours were optimized as the best extraction time for larvae and adults to get maximum number of emitted VOCs. The number of compound released from T.variabile different stages was 7, 9 and 11 for male, female and larvae respectively. Furthermore, some compounds were identified in all stages such as oxime-, methoxy-phenyl, 2(3H)-furanone, 5-ethyldihydro- and nonanal. Also, most of the compounds were significantly different in peak area between different life stages.

Volatile blooms of Aleurodicus rugioperculatus Martin infested coconut and banana leaves attracting parasitoid Encarsia guadeloupae Viggiani

Invasive whitefly species in coconut plantations have become a matter of concern in the last half a decade as they cause direct and indirect infestation on the palms. The Rugose spiralling whitefly (RSW), Aleurodicus rugioperculatus Martin infestation was high on Cocos nucifera L. (coconut) followed by Dypsis lutescens (H. Wendl.) (Butterfly palm) and Annona squamosa L. (custard apple). The coconut varieties Malayan Yellow Dwarf and Chowghat Orange Dwarf were observed with higher infestation index while that of West coast tall was the lowest. Higher RSW parasitization levels were observed on Musa paradisiaca L. (banana) and Canna indica L. (Indian shot), with 85.96 and 71.59% parasitization, respectively. Identification of volatile organic compounds (VOCs) between healthy and RSW-infested coconut plants revealed the emission of 56 VOCs from the healthy coconut plant and 47 VOCs from RSW infested plant. 22 VOCs were common in both samples, and 25 VOCs were unique to RSW-infested coconut plants. The presence of 42 VOCs was identified from the headspace extracts of RSW-infested bananas. Differences in the VOCs emitted from RSW-infested banana and coconut plants revealed higher emission of terpenoids like β-Caryophyllene, (E, Z)-2,6-dimethyl-2,4,6-Octatriene, Humulene, α-Pinene, Farnesane, α-Copaene and β-cis-Ocimene from RSW infested banana plants that proved to be more attractive to the parasitoid. Identifying specific blends of volatile compounds influencing Encarsia guadeloupae Viggiani could help to augment the parasitotic for RSW management in coconut plantations.

Combining with volatilomic profiling and chemometrics to explore the volatile characteristics in five different dried Zanthoxylum bungeanum maxim

Owing to the short picking period of the fresh Zanthoxylum bungeanum, the postharvest drying has become an essential operation before the storage and transportation of Z. bungeanum. To explore the effects of drying methods on volatile characteristics, the volatilomic profiling of five different dried Z. bungeanum was investigated by E-nose, HS-SPME-GC/MS, GC-IMS in combination with chemometrics. The results indicated that W1W, W2W and W5S sensors within E-nose analysis showed the strongest responses in both fresh and dried Z. bungeanum. According to the identification of volatile organic compounds (VOCs), terpenes, esters and alcohols played the major roles in the volatile formation of the fresh and dried Z. bungeanum. The samples derived from hot air drying showed the relatively similar features with the fresh sample based on the relative abundances of these major VOCs. According to the results of multiple factor analysis (MFA), GC-IMS showed the strongest ability in distinguishing the fresh and different dried samples. Compared with the high levels of terpenes in fresh group, the significant increasement of terpene alcohols and terpene esters from the degradation and transformation of bound terpenoids was the main characteristics of all dried Z. bungeanum. Using the GC-IMS datasets, a weighted correlation network analysis (WCNA) model was constructed to clarify the VOC characteristics in all detetected samples. Thereinto, 6 significantly correlated modules were identified in fresh and five different dried samples. Additionally, a total of 23 hub VOCs can be recognized as the potential biomarkers for better distinguishing the fresh and five different dried Z. bungeanum.

Gas Chromatography-Mass Spectrometry Profiling of Volatile Metabolites Produced by Some Bacillus spp. and Evaluation of Their Antibacterial and Antibiotic Activities.

Bacillus species produce different classes of antimicrobial and antioxidant substances: peptides or proteins with different structural compositions and molecular masses and a broad range of volatile organic compounds (VOCs), some of which may serve as biomarkers for microorganism identification. The aim of this study is the identification of biologically active compounds synthesized by five Bacillus species using gas chromatography coupled to mass spectrometry (GC-MS). The current study profoundly enhances the knowledge of antibacterial and antioxidant metabolites ensuring the unambiguous identification of VOCs produced by some Bacillus species, which were isolated from vegetable samples of potato, carrot, and tomato. Phylogenetic and biochemical studies were used to identify the bacterial isolates after culturing. Phylogenetic analysis proved that five bacterial isolates BSS12, BSS13, BSS16, BSS21, and BSS25 showed 99% nucleotide sequence similarities with Bacillus safensis AS-08, Bacillus cereus WAB2133, Bacillus acidiproducens NiuFun, Bacillus toyonesis FORT 102, and Bacillus thuringiensis F3, respectively. The crude extract was prepared from bacterial isolates to assess the antibiotic resistance potency and the antimicrobial potential against various targeted multidrug-resistant strains, including yeast strains such as Candida albicans, Candida krusei, and bacterial strains of Enterococcus hirae, Escherichia coli, Klebsiella aerogenes, Klebsiella pneumoniae, Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus group B, Streptococcus mutans, Shigella sonnei, Salmonella enteritidis, Serratia marcescens, Pseudomonas aeruginosa, and Proteus vulgaris. GC-MS analysis of bacterial strains found that VOCs from Bacillus species come in a variety of chemical forms, such as ketones, alcohols, terpenoids, alkenes, etc. Overall, 69 volatile organic compounds were identified from five Bacillus species, and all five were found to share different chemical classes of volatile organic components, which have a variety of pharmacological applications. However, eight antibacterial compounds with different concentrations were commonly found in all five species: acetoin, acetic acid, butanoic acid, 2-methyl-, oxime-, methoxy-phenyl, phenol, 1,2-benzenedicarboxylic acid, bis(2-methylpropyl) ester, nonanoic acid, and hexadecanoic acid, methyl. The present study has demonstrated that bacterial isolates BSS25, BSS21, and BSS16 display potent inhibitory effects against Candida albicans, while BSS25, BSS21, and BSS13 exhibit the ability to restrain the growth and activity of Candida krusei. Notably, BSS25 and BSS21 are the only isolates that demonstrate substantial inhibitory activity against Klebsiella aerogenes. This disparity in inhibitory effects could be attributed to the higher concentrations of acetoin in BSS25 and BSS21, whereas BSS16 and BSS13 have relatively elevated levels of butanoic acid, 2-methyl-. Certainly, the presence of acetoin and butanoic acid, 2-methyl-, contributes to the enhanced antibacterial potential of these bacterial strains, in conjunction with other organic volatile compounds and peptides, among other factors. The biology and physiology of Bacillus can be better understood using these results, which can also be used to create novel biotechnological procedures and applications. Moreover, because of its exceptional ability to synthesize and produce a variety of different antibacterial compounds, Bacillus species can serve as natural and universal carriers for antibiotic compounds in the form of probiotic cultures and strains to fight different pathogens, including mycobacteria.

Examination of Volatile Signatures of Fusarium Bulb Rot in Garlic Using Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry and Solid-Phase Microextraction Gas Chromatography/Mass Spectrometry

Fusarium bulb rot, caused by Fusarium proliferatum, is a worldwide disease of garlic, both in the open field and during storage. Early diagnosis of the disease during storage is difficult due to the morphology of the bulbs and cloves. Volatile organic compounds (VOCs) are secondary metabolites produced by several microorganisms, including phytopathogenic fungi and bacteria. In recent years, the development of several techniques for the detection and characterization of VOCs has prompted their use, among others, as a diagnostic tool for the early and non-destructive analysis of many diseases of species of agricultural interest. In this paper, proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and solid-phase microextraction gas chromatography/mass spectrometry (SPME-GC/MS) were successfully utilized to characterize the volatolome of commercial garlic cloves, artificially and naturally infected with F. proliferatum, for the early discrimination between diseased and healthy ones. A partial least squares discriminant analysis (PLSDA) and a principal component analysis (PCA) allowed for the separation of infected and healthy cloves and the identification of specific VOCs produced by the fungus during the infection. The results obtained in this work could be utilized for the development of simpler, more economical, and more portable devices for the early detection of infected garlic bulbs during storage.

Identification of volatile organic compounds in purple and white soybean flowers by HS-SPME/GC-MS

This study aimed to establish a method for the extraction, enrichment, and identification of volatile organic compounds (VOCs) released by the flowers of purple (BRS 399) and white (DONMARIO 6563) soybean varieties. We tested the Static Headspace (HS) and Solid Phase Microextraction (SPME) methods using various fibre types: PDMS (Polydimethylsiloxane), PDMS/DVB (Divinylbenzene), and PDMS/DVB/CAR (Carboxen). We employed gas chromatography–mass spectrometry (GC–MS) to identify the VOCs. The SPME method with PDMS/DVB and PDMS/DVB/CAR fibres yielded the highest number of extracted compounds for both soybean cultivars. Notably, 67 compounds were detected in Glycine max. L for the first time. Using the developed method, we were able to detect 52 and 57 VOCs in the purple and white soybean varieties, respectively, including ketones, alcohols, aldehydes and benzenoids. In conclusion, the method we developed effectively identified VOCs in soybean flowers, thus enriching our understanding of the interactions between soybean flowers and their pollinators.

AI‐Driven Wearable Mask‐Inspired Self‐Healing Sensor Array for Detection and Identification of Volatile Organic Compounds

AbstractVolatile organic compounds (VOCs) sensor arrays have garnered considerable attention due to their potential to provide real‐time information for monitoring pollution levels and personal health associated concerning VOCs in the ambient environment. Here, an AI‐driven wearable mask‐inspired self‐healing sensor array (MISSA), created using a simplified single‐step stacking technique for detecting and identifying VOCs is presented. This wearable MISSA comprises three vertically placed breathable self‐healing gas sensors (BSGS) with linear response behavior, consistent repeatability, and reliable self‐healing abilities. For wearable and portable monitoring, the MISSA is combined with a flexible printed circuit board (FPCB) to produce a mobile‐compatible wireless system. Due to the distinct layers of MISSA, it creates exclusive code bars for four distinct VOCs over three concentration levels. This grants precise gas identification and concentration prognoses with excellent accuracy of 99.77% and 98.3%, respectively. The combination of MISSA with artificial intelligence (AI) suggests its potential as a successful wearable device for long‐term daily VOC monitoring and assessment for personal health monitoring scenarios.