Search Results (219)

Volatile organic compounds (VOCs) are critical indicators of the metabolic status of whole-plant maize silage (WPMS). However, the impact of inoculating various strains of fermentation agents on VOC changes has not been systematically explored. This study aimed to determine how inoculation with Lactiplantibacillus plantarum and Lentilactobacillus buchneri modulates the VOC profile and odor of WPMS after 90 days. VOCs were extracted by headspace solid-phase microextraction and analyzed by gas chromatography-mass spectrometry (HS-SPME-GC-MS). Key VOCs were screened using the variable importance in projection (VIP) and substantiated by relative odor activity values (rOAV) and odor descriptions. A total of 82 compounds were identified, including 22 esters, 19 alcohols, 3 acids, 9 aldehydes, 2 ethers, 6 hydrocarbons, 4 ketones, 10 phenols, and 8 terpenoids. L. plantarum enhanced green/fruity odors while strain L. buchneri significantly reduced undesirable phenolic and aldehydic compounds. Six key VOCs influencing the odor of WPMS were selected: 4-ethyl-2-methoxyphenol and benzaldehyde, which contribute smoky, bacon, and bitter almond aromas, and (E)-3-hexen-1-ol, benzyl alcohol, (E, E)-2,4-heptadienal and methyl salicylate, which impart green, fruity, and nutty aromas. These findings highlight the effects and contributions of various strain additives on VOCs in WPMS, providing new theoretical insights for regulating the flavor profile of WPMS. Full article

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. Full article

This study aimed to systematically characterize the volatile organic compound (VOC) profiles of safflower (Carthamus tinctorius L.) from eight major production regions, providing a scientific basis for quality evaluation and geographical traceability. VOC profiling was conducted using gas chromatography–ion mobility spectrometry (GC–IMS), and regional differences were assessed through multivariate statistical analyses, including Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS–DA), Euclidean distance, and hierarchical clustering. Key differential compounds were identified by variable importance in projection (VIP) and relative odor activity value (ROAV) analyses, with aldehydes and esters emerging as the primary contributors to the discrimination of samples across regions. VOC fingerprints of safflower were further established, and a combined VIP–ROAV strategy was proposed for the screening of characteristic compounds. These findings provide a reliable reference for safflower quality control and offer practical guidance for its geographical authentication in the food industry. Full article

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy associated with occupational or environmental exposure to asbestos. Effective management of MPM remains challenging due to its prolonged latency period and the typically late onset of clinical symptoms. Accordingly, there is an increasing demand for the implementation of reliable, non-invasive, and data-driven diagnostic strategies within large-scale screening programs. In this context, the chemical profiling of volatile organic compounds (VOCs) in exhaled breath has recently gained recognition as a promising and non-invasive approach for the early detection of cancer, including MPM. Therefore, in this cross-sectional observational study, an overall number of 125 individuals, including 64 MPM patients and 61 healthy controls (HC), were enrolled. End-tidal breath fraction (EXP) was collected directly onto two-bed adsorbent cartridges by an automated sampling system and analyzed by thermal desorption–gas chromatography–mass spectrometry (TD-GC/MS). A machine learning approach based on a random forest (RF) algorithm and trained using a 10-fold cross-validation framework was applied to experimental data, yielding remarkable results (AUC = 86%). Fifteen VOCs reflecting key metabolic alterations characteristic of MPM pathophysiology were found to be able to discriminate between MPM and HC. Moreover, twenty breath samples from asymptomatic former asbestos-exposed (AEx) and eight MPM patients during follow-up (FUMPM) were exploratively analyzed, processed, and tested as blinded samples by the validated statistical method. Good agreement was found between model output and clinical information obtained by CT. These findings underscore the potential of breath VOC analysis as a non-invasive diagnostic approach for MPM and support its feasibility for longitudinal patient and at-risk subjects monitoring. Full article

This work compares reactive (castor) and non-reactive (rapeseed, sunflower, linseed, and used cooking) oils, each at a dosage of 10 wt%, when incorporated into an in-house two-component polyurethane (PU) parquet adhesive. A commercial market adhesive was tested only as an external benchmark and was not modified. Mechanical properties were evaluated according to EN ISO 17178, inorganic leaching according to EN 12457-4, and volatile organics were screened by headspace GC–MS (not comparable to ISO 16000-9 chamber protocols). All in-house formulations met the EN ISO 17178 acceptance limits. The sunflower oil variant showed the highest shear strength, whereas rapeseed and castor oils provided stable tensile performance. HS-GC-MS did not yield high-confidence VOC identifications; therefore, no regulatory emission claim is made. The formulation with used cooking oil exhibited the largest variability and elevated leaching of Zn and Sb, underscoring the need for feedstock quality control. At 10 wt% loading, standard-compliant performance was obtained with both reactive and non-reactive oils, suggesting that physical modification can be sufficient, while castor oil may additionally react. In contrast to derivatized oils reported elsewhere, our approach employs unmodified oils, thereby avoiding extra reaction steps—such as epoxidation, hydroxylation, and transesterification—that typically increase the carbon footprint, while still meeting relevant standards. Full VOC chamber testing and LCA are beyond the scope of this study. Full article

This study investigates the feasibility of incorporating chemically recycled polyol (glycolysate), derived from semi-rigid polyurethane waste from the building industry, into rigid PUF formulations intended for thermal insulation applications. Glycolysis was performed using a diethylene glycol–glycerol mixture (4:1) at 185 °C in the presence of a dibutyltin dilaurate (DBTDL) catalyst. The resulting glycolysate was characterized by a hydroxyl number of 590 mg KOH/g. Foams containing 5–50% recycled polyol were prepared and described in terms of foaming kinetics, cellular structure, thermal conductivity, apparent density, mechanical performance, dimensional stability, flammability, and volatile organic compound (VOC) emissions. The incorporation of glycolysate accelerated the foaming process, with the gel time reduced from 44 s to 16 s in the sample containing 40% recycled polyol, enabling a reduction in catalyst content. The substitution of up to 40% virgin polyol with recycled polyol maintained a high closed-cell content (up to 87.7%), low thermal conductivity (λ₁₀ = 26.3 mW/(m·K)), and dimensional stability below 1%. Additionally, compressive strength improvements of up to 30% were observed compared to the reference foam (294 kPa versus 208 kPa for the reference sample). Flammability testing confirmed compliance with the B2 classification (DIN 4102), while preliminary qualitative VOC screening indicated no formation of additional harmful volatile compounds in glycolysate-containing samples compared to the reference. The results demonstrate that glycolysate can be effectively utilized in high-performance insulation materials, contributing to improved resource efficiency and a reduced carbon footprint. Full article

Root rot, mainly caused by Fusarium oxysporum, is one of the most destructive diseases and leads to significant economic loss of Astragalus membranaceus. To develop an effective strategy for the management of this serious disease, a bacterial strain 2-12 was screened from A. membranaceus rhizosphere soil and identified as Bacillus paralicheniformis based on the phylogenetic analyses of gyrase subunit B gene (gyrB) and RNA polymerase gene (rpoB) sequences. Interestingly, the volatile organic compounds (VOCs) produced by B. paralicheniformis 2-12 exhibited potent antifungal activities against F. oxysporum, as well as fifteen other plant pathogens. Under scanning electron microscopy observation, hyphae treated with the VOCs exhibited abnormal variation such as distortion, twist, and vesiculation, leading to distinctive protoplasm shrinkage. After treatment with B. paralicheniformis 2-12 VOCs, the lesion diameter and disease incidence both reduced significantly compared to control (p < 0.05), thus demonstrating prominent biological efficiency. Moreover, B. paralicheniformis 2-12 VOCs were composed of 17 VOCs, including 9 alkanes, 3 alcohols, 3 acids and esters, 1 aromatic compound, and 1 alkyne compound. A total of 1945 DEGs, including 1001 up-regulated and 944 down-regulated genes, were screened via transcriptome analysis. These DEGs were mainly associated with membranes and membrane parts, amino acid metabolism, and lipid metabolism. The findings in this work strongly suggested that B. paralicheniformis 2-12 VOCs could be applied as a new candidate for the control of A. membranaceus root rot. Full article

Tomicus yunnanensis Kirkendall and Faccoli, a native bark beetle species and key pest of Pinus yunnanensis Franch. in southwestern China, relies on host-derived volatile organic compounds (VOCs) for host selection. To unravel these mechanisms, we collected VOCs from P. yunnanensis trunks across four infestation stages (healthy, early-infested, weakened, near-dead) using dynamic headspace sampling. Chemical profiling via gas chromatography–mass spectrometry (GC-MS) identified 51 terpenoids, with α-pinene as the most abundant component. VOC profiles differed markedly between healthy and early-infested trees, while gradual shifts in compound diversity and abundance occurred from the weakened to near-dead stages. Bioactive compounds were screened using gas chromatography–electroantennographic detection (GC-EAD) and a Y-tube olfactometer. Electrophysiological responses in T. yunnanensis were triggered by α-pinene, β-pinene, 3-carene, 2-thujene, and 4-allylanisole. Behavioral tests revealed that α-pinene, 3-carene, and 2-thujene acted as attractants, whereas β-pinene and 4-allylanisole functioned as repellents. These results indicate that infestation-induced VOC dynamics guide beetle behavior, with attractants likely promoting host colonization during early infestation and repellents signaling deteriorating host suitability in later stages. By mapping these chemical interactions, our study identifies potential plant-derived semiochemicals for targeted pest management. Integrating these compounds with pheromones could enhance the monitoring and control strategies for T. yunnanensis, offering ecologically sustainable solutions for pine ecosystems. Full article

This review aims to summarize possible methods for the detection of limonene in the gas phase at low to very low concentrations. Limonene has historically been of interest as a fragrance in cosmetics, the food industry, pharmaceutics, and the production of solvents. The development of analytical methods for limonene was initially driven by its use in relevant industries such as chemical, pharmaceutics, cosmetics, food, agriculture, and forestry. More recently, it has been recognized as a potent biomarker for human metabolic conditions, such as liver disease and certain cancers. The interest in improved limonene detection in exhaled human breath has increased, particularly from the medical field, which demands high reliability, very low detection limits in the parts per billion (ppb) and even parts per trillion (ppt) range, and excellent selectivity against other exhaled volatile organic compounds (VOC). In addition, the detection methods should be portable and affordable to facilitate potential mass screening. This review paper aims to explore all possible detection methods by evaluating their proven analytical capabilities for limonene or discussing their potential usefulness, benefits, and applicability for limonene detection. Full article

Early detection of cancer is crucial for improving patient outcomes. Traditional modalities such as mammography and low-dose computed tomography are effective but exhibit inherent limitations, including radiation exposure and accessibility challenges. This review explores innovative, non-invasive cancer screening methods, focusing on liquid biopsy and volatile organic compound (VOC)-based detection platforms. Liquid biopsy analyzes circulating tumor DNA and other biomarkers in bodily fluids, offering potential for early detection and monitoring of treatment response. VOC-based detection leverages unique metabolic signatures emitted by cancer cells, detectable in exhaled breath or other bodily emissions, providing a rapid and patient-friendly screening option. We provide a comprehensive overview of these advanced multi-cancer detection techniques to enhance diagnostic accuracy, accessibility, and patient adherence, and ultimately enhance survival rates and patient outcomes. Full article

Exhaled breath condensate (EBC) has gained attention as a diagnostic gateway for lung diseases, brain–gut microbiota dysbiosis, and biobanking. Due to its non-invasive and fast collection method, EBC collection is not under temporal or volume limitations. Nonetheless, conventional EBC screening methods are complex and require high operational costs and expertise. Thus, the advent of nanotechnology has introduced efforts for using nanosensors as EBC analyzers. Over the past decade, multiple EBC-based studies reported the successful use of functionalized nanosensors to trace oxidative stress, tissue damage, and respiratory diseases. The EBC signature includes biomarkers such as gases (H₂O₂ and VOCs), cations (polyamines), fatty acids, cytokines, and aldehydes, in addition to traces of drugs and antibiotics. A common feature of nanosensors is their ability to amplify signals and rapidly detect EBC analytes with high sensitivity and specificity. Based on the collected data, standardizing the collection protocol and read-out methods across laboratories is essential for optimal data comparability. Larger cohorts should be considered to ensure a reliable reproducibility of the reported outputs. Future research directions should employ EBC-based nanosensors to unravel the unexplored omics of lung diseases, especially those linked to the brain–gut microbiota that might influence airway immunity. Full article

Background: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Effective screening strategies for early diagnosis that could improve disease prognosis are lacking. Non-invasive breath analysis of volatile organic compounds (VOC) is a potential method for earlier LC detection. This study explores the association of VOC profiles with artificial intelligence (AI) to achieve a sensitive, specific, and fast method for LC detection. Patients and methods: Exhaled breath air samples were collected from 123 healthy individuals and 73 LC patients at two clinical sites. The enrolled patients had LC diagnosed with different stages. Breath samples were collected before undergoing any treatment, including surgery, and analyzed using gas chromatography coupled to ion-mobility spectrometry (GC-IMS). AI methods classified the overall chromatographic profiles. Results: GC-IMS is highly sensitive, yielding detailed chromatographic profiles. AI methods ranked the sets of exhaled breath profiles across both groups through training and validation steps, while qualitative information was deliberately not taking part nor influencing the results. The K-nearest neighbor (KNN) algorithm classified the groups with an accuracy of 90% (sensitivity = 87%, specificity = 92%). Narrowing the LC group to those only in early-stage IA, the accuracy was 90% (sensitivity = 90%, specificity = 93%). Conclusions: Evaluation of the global exhaled breath profiles using AI algorithms enabled LC detection and demonstrated that qualitative information may not be required, thus easing the frustration that many studies have experienced so far. The results show that this approach coupled with screening protocols may improve earlier detection of LC and hence its prognosis. Full article

Formaldehyde is the most abundant carbonyl globally and the biggest driver of cancer risk in the United States among hazardous air pollutants. Ambient formaldehyde concentration measurements are generally sparse due to high measurement costs and limited measurement infrastructure. Recent studies have used low-cost air quality sensors to affordably improve spatial coverage and provide real-time measurements. Our previous research evaluated the laboratory performance of a low-cost electrochemical formaldehyde sensor (Sensirion SFA30) over formaldehyde concentrations ranging from 0 to 76 ppb. The sensors exhibited good linearity of response, a low limit of detection, and good accuracy in detecting formaldehyde. This study evaluated the cross-sensitivity of the SFA30 and the Gravity sensors (electrochemical formaldehyde sensors) over formaldehyde concentrations ranging from 0 to 326 ppb in a laboratory evaluation system, with broadband cavity-enhanced absorption spectroscopy used to obtain the reference measurements. We evaluated the sensors in a mixture of formaldehyde with five outdoor trace gases (CO, NO, NO₂, O₃, and isobutylene) and two indoor VOCs (methanol and isopropyl alcohol). The results suggest that the Gravity sensors may be useful for outdoor formaldehyde measurements when formaldehyde levels are well above background levels and that the SFA30 sensors may be useful screening tools for indoor environments, if properly calibrated. Full article

Single-cell protein feed (SCPF) is an important supplement to protein feed materials, but its authenticity is often affected by antibiotic mycelial dregs (AMD). Headspace-gas chromatography–ion mobility spectrometry (HS-GC-IMS), integrated with chemometrics, was utilized to differentiate nucleotide residue (NR), three AMDs, and adulterated samples with concentrations ranging from 0.1% to 20% (w/w). Orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component analysis (PCA) were applied to classify the adulterated samples. In addition, the feasibility of quantitative analysis of the AMDs content in adulterated SCPF based on partial least squares regression (PLSR) algorithm. In total, 88 volatile organic compounds (VOCs) were detected. The differences in VOCs between NR and AMD mainly came from aldehydes, alcohols, and esters. The OPLS-DA models effectively identified AMD in adulterated NR samples (Accuracy = 100%), demonstrating the HS-GC-IMS data’s good application potential for the SCPF adulteration. Nine VOCs, i.e., 2-ethyl-3-methylpyrazine, dihydro-5-methyl-2(3H)-furanone, 2-methylpropanol, (E,E)-2,4-heptadienal, linalool, 2,3,5-trimethylpyrazine, citronellol, acetoin, and 3-methylbutan-1-ol, were proposed as key markers for detecting NR adulterated with AMDs. The PLSR algorithm was further used to determine the AMD content in NR (R²_cal = 0.96, R²_cv = 0.94). This study validated HS-GC-IMS’s ability to analyze volatile organic compounds in feed and showcased its utility as a convenient, quick, and affordable tool for SCPF authenticity screening. Full article

Monitoring trace toluene exposure is critical for early-stage lung cancer screening via breath analysis, yet conventional chemiresistive sensors face fundamental limitations, including compromised selectivity in complex VOC matrices and humidity-induced signal drift, with prevailing p–n heterojunction architectures suffering from inherent charge recombination and environmental instability. Herein, we pioneer a 2D core–shell n–n heterojunction strategy through rational design of TiO₂@MoS₂ heterostructures, where vertically aligned MoS₂ nanosheets are epitaxially grown on 2D TiO₂ derived from graphene-templated synthesis, creating built-in electric fields at the heterojunction interface that dramatically enhance charge carrier separation efficiency. At 240 °C, the TiO₂@MoS₂ sensor exhibits a superior response (R_a/R_g = 9.8 to 10 ppm toluene), outperforming MoS₂ (R_a/R_g = 2.8). Additionally, the sensor demonstrates rapid response/recovery kinetics (9 s/16 s), a low detection limit (50 ppb), and excellent selectivity against interfering gases and moisture. The enhanced performance is attributed to unidirectional electron transfer (TiO₂ → MoS₂) without hole recombination losses, methyl-specific adsorption through TiO₂ oxygen vacancy alignment, and steric exclusion of non-target VOCs via size-selective MoS₂ interlayers. This work establishes a transformative paradigm in gas sensor design by leveraging n–n heterojunction physics and 2D core–shell synergy, overcoming long-standing limitations of conventional architectures. Full article