Search Results (1,065)

Background/Objectives: Breast cancer is the most common malignancy among women, and early detection is critical for improving outcomes. The Breast Imaging Reporting and Data System (BI-RADS) standardizes reporting, but the BI-RADS 4 category presents a major challenge, with malignancy risk ranging from 2% to 95%. Consequently, most women in this category undergo biopsies that ultimately prove unnecessary. This study evaluated whether exhaled breath analysis could distinguish malignant from benign findings in BI-RADS 4 patients. Methods: Participants referred to the McGill University Health Centre Breast Center with BI-RADS 3–5 findings provided multiple breath specimens. Breathprints were captured using an electronic nose (eNose) powered breathalyzer, and diagnoses were confirmed by imaging and pathology. An autoencoder-based model fused the breath data with BI-RADS scores to predict malignancy. Model performance was assessed using repeated cross-validation with ensemble voting, prioritizing sensitivity to minimize false negatives. Results: The breath specimens of eighty-five participants, including sixty-eight patients with biopsy-confirmed benign lesions and seventeen patients with biopsy-confirmed breast cancer within the BI-RADS 4 cohort were analyzed. The model achieved a mean sensitivity of 88%, specificity of 75%, and a negative predictive value (NPV) of 97%. Results were consistent across BI-RADS 4 subcategories, with particularly strong sensitivity in higher-risk groups. Conclusions: This proof-of-concept study shows that exhaled breath analysis can reliably differentiate malignant from benign findings in BI-RADS 4 patients. With its high negative predictive value, this approach may serve as a non-invasive rule-out tool to reduce unnecessary biopsies, lessen patient burden, and improve diagnostic decision-making. Larger, multi-center studies are warranted. Full article

Salinity stress severely limits crop productivity worldwide. While plant growth-promoting rhizobacteria (PGPR) are known to alleviate abiotic stress, the specific mechanisms mediated by their volatile organic compounds (VOCs) remain largely elusive. In this study, an in vitro split-plate system was used to investigate the effects of VOCs emitted by Bacillus velezensis SQR9 on Arabidopsis thaliana seedlings under salt stress. Exposure to SQR9 VOCs significantly enhanced Arabidopsis salt tolerance, evidenced by increased biomass and root growth. Mechanistically, SQR9 VOCs mitigated salt-induced damage by increasing chlorophyll content, modulating osmolytes, and reducing malondialdehyde (MDA) levels. SQR9 VOCs alleviated oxidative stress by decreasing ROS (H₂O₂, O₂⁻) accumulation and enhancing antioxidant enzyme (SOD, CAT, POD) activities. Furthermore, SQR9 VOCs maintained ion homeostasis by significantly reducing leaf Na⁺ accumulation, maintaining a high K⁺/Na⁺ ratio, and upregulating key ion transporter genes. Analysis of the headspace from SQR9 cultured on MSgg medium identified 2,3-butanediol (2,3-BD) as a major active VOC. Exogenous application of 2,3-BD successfully mimicked the growth-promoting and salt-tolerance-enhancing effects of SQR9. Our findings demonstrate that SQR9 VOCs, particularly 2,3-BD, systemically prime Arabidopsis for salt tolerance by co-activating the antioxidant defense system and the SOS ion homeostasis pathway. Full article

This study aimed to evaluate the feasibility of producing vinegar from organic third-category apples, peaches, and clementines on a laboratory scale. Two-step fermentation with Saccharomyces cerevisiae and Gluconobacter oxydans was applied, monitoring production of ethanol and acetic acid and microbial dynamics. Fruit vinegars were subjected to analyses of sensory traits, color, volatile organic compounds (VOCs), and antioxidant activity. Comparable ethanol yields across substrates were obtained, ensuring consistent acetous fermentation and achieving acetic acid concentrations of 5.0–5.6%. Dynamics of yeasts and acetic acid bacteria reflected the production of and subsequent decrease in ethanol. Overall, fermentation proceeded a bit faster in peach juice. Overall, the fruit vinegars, particularly those from peaches and clementines, exhibited darker and more saturated tones. The values of colorimetric indexes fell within the range reported for vinegars. Sensory analysis highlighted large differences among the vinegars. Notwithstanding the highest scores of color, aroma intensity, and floral aroma received by the peach vinegar (PV), it received the lowest acceptability. Clementine vinegar (CV) was especially appreciated. Multivariate analysis based on the VOC profile showed that apple vinegar (AV) was quite similar to the commercial one, whereas PV and CV were well distinguished from it. CV showed the highest antioxidant activity followed by PV. Full article

Volatile organic compounds (VOCs) emissions from petroleum product storage pose not only a significant environmental concern but also a potential threat to occupational health. This study investigates geospatial analysis of VOCs on an industrial platform in Romania, utilizing a combination of portable field detectors and geostatistical modeling techniques. For more than 10 months, we conducted measurements at 41 georeferenced sampling points across three operational zones, using FID/PID instruments calibrated and validated in accordance with national standards. To evaluate dispersion conditions, meteorological data were simultaneously collected. VOC concentrations were measured under varying meteorological scenarios and analyzed using the Empirical Bayesian Kriging (EBK) method in ArcGIS Pro 3.1.0. Maximum concentrations reached up to 229.46 mg/m³ in central tank areas, with some point samples exceeding this level. Peripheral zones generally showed values below 65 mg/m³, although concentrations above 100 mg/m³ were still observed at 10% of the monitoring sites. The results indicate apparent spatial clustering of elevated VOC levels, particularly under low wind speed and high humidity. Our study highlights the relevance of continuous monitoring and site-specific mitigation strategies in high-risk industrial settings in Romania. Full article

Background/Objectives: Volatile organic compounds (VOCs) are emerging as non-invasive biomarkers of metabolic and disease-related processes, yet their reliable detection from complex biological matrices such as urine remains analytically challenging. This study aimed to establish a robust, non-targeted headspace solid-phase microextraction gas chromatography–mass spectrometry (HS–SPME GC–MS) workflow optimized for very small-volume urinary samples. Methods: We systematically evaluated the effects of pH adjustment and NaCl addition on VOC extraction efficiency using a 75 µm CAR/PDMS fiber and a sample volume of only 0.75 mL. Method performance was further assessed using concentration-dependent experiments with representative VOC standards and by application to real human urine samples analyzed in technical triplicates. Results: Acidification to pH 3 markedly improved extraction performance, increasing both total signal intensity and the number of detectable VOCs, whereas alkaline conditions and additional NaCl produced only minor effects. Representative VOC standards showed compound-specific linear dynamic ranges with minimal carry-over within the relevant analytical range. Application to real urine samples confirmed high analytical reproducibility, with triplicates clustering tightly in principal component analysis and most metabolites exhibiting relative standard deviations below 25%. Conclusions: The optimized HS–SPME GC–MS method enables comprehensive, non-targeted urinary VOC profiling from limited sample volumes. This workflow provides a robust analytical foundation for exploratory volatilomics studies under sample-limited conditions and supports subsequent targeted method refinement once specific compounds or chemical classes have been prioritized. Full article

The fiberboard industry remains heavily reliant on synthetic, formaldehyde-based adhesives, which, despite their cost-effectiveness and strong bonding performance, present significant environmental and human health concerns due to volatile organic compound (VOC) emissions. In response to growing sustainability imperatives and regulatory pressures, the development of non-toxic, renewable, and high-performance bio-based adhesives has emerged as a critical research frontier. This review, conducted through both narrative and systematic approaches, synthesizes current advances in green adhesive technologies with emphasis on lignin, tannin, starch, protein, and hybrid formulations, alongside innovative synthetic alternatives designed to eliminate formaldehyde. The Evidence for Policy and Practice Information and Coordinating Centre (EPPI) framework was applied to ensure a rigorous, transparent, and reproducible methodology, encompassing the identification of research questions, systematic searching, keywording, mapping, data extraction, and in-depth analysis. Results reveal that while bio-based adhesives are increasingly capable of approaching or matching the mechanical strength and durability of urea–formaldehyde adhesives, challenges persist in terms of water resistance, scalability, cost, and process compatibility. Hybrid systems and novel crosslinking strategies demonstrate particular promise in overcoming these limitations, paving the way toward industrial viability. The review also identifies critical research gaps, including the need for standardized testing protocols, techno-economic analysis, and life cycle assessment to ensure the sustainable implementation of these solutions. By integrating environmental, economic, and technological perspectives, this work highlights the transformative potential of green adhesives in transitioning the fiberboard sector toward a low-toxicity, carbon-conscious future. It provides a roadmap for research, policy, and industrial innovation. Full article

Grain aging during storage leads to quality deterioration and significant economic losses. Traditional analytical approaches are often labor-intensive, slow, and inadequate for modern intelligent grain storage management. This review summarizes recent advances in the intelligent discrimination of grain aging using volatile organic compound (VOC) fingerprints combined with machine learning (ML) techniques. It first outlines the biochemical mechanisms underlying grain aging and identifies VOCs as early and sensitive biomarkers for timely determination. The review then examines VOC determination methodologies, with a focus on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), for constructing volatile fingerprinting profiles, and discusses related method standardization. A central theme is the application of ML algorithms, including Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN)) for feature extraction and pattern recognition in high-dimensional datasets, enabling effective discrimination of aging stages, spoilage types, and grain varieties. Despite these advances, key challenges remain, such as limited model generalizability, the lack of large-scale multi-source databases, and insufficient validation under real storage conditions. Finally, future directions are proposed that emphasize methodological standardization, algorithmic innovation, and system-level integration to support intelligent, non-destructive, real-time grain quality monitoring. This emerging framework provides a promising powerful pathway for enhancing global food security. Full article

This study aimed to analyze the volatile organic compounds (VOCs) for different rice aroma types using sensory evaluation, headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS), and gas chromatography-ion mobility spectrometry (GC-IMS) techniques, and to explore the material basis for the flavor differences. Based on the sensory evaluation results, rice aroma was categorized into three types, distinguished by their unique aroma compounds. Type A was characterized by a prominent sweet, popcorn aroma, type B by a more prominent cereal and starchy flavor, and type C by a more complex aroma. Untargeted metabolomics analysis using HS-SPME-GC-MS identified and characterized 74 volatile compounds. A comparison of A versus B versus C revealed 8 key aroma compounds, primarily alkanes, aldehydes, ketones, alcohols, and heterocyclic compounds. (E)-2-Octenal, 6-Undecanone, 2-Acetyl-1h-pyrrole, and P-menthan-1-ol in type A gave it a better sweet aroma, Dodecane, 2,6,10-trimethyl-, 1-Octen-3-one, and 2-Methyldecane in type B gave it a better starchy and cereal flavor. 2-Acetyl-1h-pyrrole, Heptacosane, and 1-Propanol in type C contributed to a complex aroma. GC-IMS analysis showed that the fingerprints of rice with different aroma types were significantly different. The VOCs of aroma type A contained (+)-limonene, 2-methylpyrazine, 2-pentanone, ethyl butanoate, n-pentanal, styrene, 1-butanol, 3-methyl-, acetate, 1-hexanal, 1-pentanol, and 2-heptanone, which gave it a better sweet aroma. The VOCs of aroma type C contained 1-octen-3-ol, 2,6-dimethyl pyrazine, 2-acetylpyridine, and ethyl hexanoate, which gave it a better complex aroma. Full article

The color and scent of flowers are vital ornamental attributes influenced by a complex interaction of metabolic and transcriptional mechanisms. Comparative analyses were performed to determine the molecular rationale for these features in Hydrangea arborescens, between the white-flowered variety ‘Annabelle’ (An) and its pink-flowered variant ‘Pink Annabelle’ (PA). Gas chromatography–mass spectrometry (GC–MS) detected 25 volatile organic compounds (VOCs) in ‘An’ and 21 in ‘PA’, with 18 chemicals common to both types. ‘An’ exhibited somewhat higher VOC diversity, whereas ‘PA’ emitted much bigger quantities of benzenoid and phenylpropanoid volatiles, including benzaldehyde, benzyl alcohol, and phenylethyl alcohol, resulting in a more pronounced floral scent. UPLC–MS/MS metabolomic analysis demonstrated obvious clustering of the two varieties and underscored the enrichment of phenylpropanoid biosynthesis pathways in ‘PA’. Transcriptomic analysis revealed 11,653 differentially expressed genes (DEGs), of which 7633 were elevated and linked to secondary metabolism. Key biosynthetic genes, including PAL, 4CL, CHS, DFR, and ANS, alongside transcription factors such as MYB—specifically TRINITY_DN5277_c0_g1, which is downregulated in ‘PA’ (homologous to AtMYB4, a negative regulator of flavonoid biosynthesis)—and TRINITY_DN23167_c0_g1, which is significantly upregulated in ‘PA’ (homologous to AtMYB90, a positive regulator of anthocyanin synthesis), as well as bHLH, ERF, and WRKY (notably TRINITY_DN25903_c0_g1, highly upregulated in ‘PA’ and homologous to AtWRKY75, associated with jasmonate pathway), demonstrating a coordinated activation of color and fragrance pathways. The integration of metabolomic and transcriptome data indicates that the pink-flowered ‘PA’ variety attains its superior coloring and aroma via the synchronized transcriptional regulation of the phenylpropanoid and flavonoid pathways. These findings offer novel molecular insights into the genetic and metabolic interplay of floral characteristics in Hydrangea. Full article

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

The flavor and aroma of kiwifruit are largely influenced by the concentration of Volatile Organic Compounds (VOCs). To analyze the volatile profiles and identify characteristic aroma compounds, this study utilized Gas Chromatography-Ion Mobility Spectrometry (GC-IMS) to analyze the aromatic compounds sourced from seven major production regions in China and New Zealand, covering red-, green-, and yellow-fleshed varieties. A total of 77 VOCs were identified, with esters, aldehydes, and ketones as the dominant classes. Significant regional and varietal differences were observed: red-fleshed kiwifruits from Yunnan exhibited high levels of 2-Vinyl-5-methylfuran, Ethyl formate, and 1-Penten-3-one; green-fleshed fruits from Shaanxi were rich in Limonene and Methyl hexanoate, and those from Yunnan were rich in 1-Propanol and 1-Hexanol; and yellow-fleshed fruits from Henan were characterized by Methyl salicylate and 3-Hydroxy-2-butanone. Orthogonal partial least squares discriminant analysis (OPLS-DA) successfully classified kiwifruits by origin and variety, confirming the stability and predictive power of the model (Q2Y > 0.97). This study also elucidated the key metabolic pathways—including lipid oxidation, amino acid degradation, and terpenoid metabolism—underlying the formation of these characteristic VOCs. These findings provide a theoretical foundation for the biochemical regulation of kiwifruit flavor and support the development of origin-tracing and quality-assessment tools based on VOC fingerprints. Full article

Background/Objectives: Fungi produce a diverse array of metabolites, including various volatile organic compounds (VOCs) with known physiological functions and other biological activities. These metabolites hold significant potential for medical and industrial applications. Within the fungal domain, Penicillium species represent a particularly important group. Methods: This study characterized the VOC profiles of four Penicillium expansum strains (R11, R19, R21, and R27) and one Penicillium polonicum strain (RS1) using the solid-phase microextraction–gas chromatography–mass spectrometry technique. Results: The analysis revealed that the only compound in common among the five strains of Penicillium was phenyl ethanol. The high toxicity of P. polonicum RS1 to Drosophila larvae correlated with its diverse and abundant alkene production. Specifically, alkenes constituted 31.28% of its total VOCs, followed by alcohols at 29.13%. GC-MS analyses detected 22, 17, 22, and 18 specific VOCs from R11, R19, R21, and R27, respectively. Overall, alkenes dominated the R11 profile (17.03%), alcohols were most abundant in R19 (28.82%), and R21 showed the highest combined release of alcohols (23.2%) and alkenes (11.7%), while R27 produced a moderate abundance of alcohols (9.16%) and alkenes (4.19%). Among the P. expansum strains, R11, R21, and R27 exhibited substantially higher toxicity than R19 strain in our previous assessment; these findings are consistent with their respective VOC profiles. Conclusions: The distinct VOC compositions across Penicillium strains significantly influence their biological characteristics and ecological functions. These findings provide a basis for follow-up research into the mechanisms of fungal volatile-mediated toxicity and support the development of biocontrol strategies. Full article

Human milk is the benchmark for formulating infant formula, the latter serving as a substitute when breastfeeding is not possible. Nevertheless, the lipid composition and structure of commercially available infant formulas still differ from those of human milk fat. Accordingly, this paper employs a computational–experimental framework to optimize formulations of prepared lipid (PF). The quality of the optimized product was further validated by analyzing volatile organic compounds (VOCs), color, lipid oxidation indicators, and oxidative stability. The results show that a total of 43 fatty acids (FA) were detected in the base oil, and palmitic acid, oleic acid, and linoleic acid are the main types of FA. Through computer simulation, 6 of PF were obtained, which are superior to commercial products (SP) in the similarity score of the parsimonious model, and PF1 has the highest score (84.15). Multivariate statistical analysis indicates that PF may be more suitable for use in infant formula milk powder due to its lipid composition. Gas chromatography-ion mobility spectrometry was used to study the VOCs content of PF and SP, and a total of 35 VOCs were identified. It was found that alcohols and ketones accounted for the highest proportion in PF, while Nitriles, Aldehydes, and Esters were the most abundant in SP. In the comparison of the basic physical and chemical indices between PF and SP, the peroxide value and p-anisidine value of PF are lower, and the overall oxidation stability is stronger than that of SP. This study provides a reference for the preparation and multi-dimensional evaluation of human milk fat substitutes. Full article

Traditional herbal prescriptions composed of multiple botanicals remain central to ethnopharmacological practice; however, their chemical basis and classification remain poorly understood. Non-volatile compound analyses of herbal medicines are well established, but comparative studies focusing on volatile organic compounds (VOCs) across multi-herbal prescriptions are scarce. To enhance the chemical understanding of traditional formulations and clarify prescription-level characteristics, this study applied headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME–GC–MS) to characterize VOC-based chemical signatures in 30 prescriptions composed of 76 herbal ingredients. Multivariate analyses such as principal component analysis, partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA) enabled systematic differentiation of various prescriptions and identified 25 discriminant VOCs, 9 of which were common among multiple therapeutic categories. These shared compounds, such as 5-hydroxymethylfurfural (5-HMF) and 4H-pyran-4-one derivatives, reflect recurrent chemical patterns associated with broad-spectrum applications, whereas category-specific volatiles (including isopsoralen, senkyunolide, and fenipentol) delineated therapeutic boundaries, even among prescriptions with overlapping botanicals. Capturing both shared and distinct volatile signatures clarified ambiguous boundaries between categories such as cold, inflammation, or diabetes versus kidney disorder prescriptions, thereby linking chemical patterns with ethnopharmacological indications. Together, these findings highlight VOC profiling as a valuable diagnostic and interpretive tool that bridges traditional categorization systems with modern chemical analysis, offering a robust framework for future pharmacological and mechanistic investigations. Such an approach not only substantiates traditional categorization but also provides a practical basis for quality control and pharmacological evaluation of multi-herbal formulations. Full article

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, excellent flexibility, and tunable chemical properties. This review comprehensively summarized recent advancements in polymer-based gas sensors for the detection of disease biomarkers in exhaled breath. The gas-sensing mechanism of polymers, along with novel gas-sensitive materials such as conductive polymers, polymer composites, and functionalized polymers was examined in detail. Moreover, key applications in diagnosing diseases, including asthma, chronic kidney disease, lung cancer, and diabetes, were highlighted through detecting specific biomarkers. Furthermore, current challenges related to sensor selectivity, stability, and interference from environmental humidity were discussed, and potential solutions were proposed. Future perspectives were offered on the development of next-generation polymer-based sensors, including the integration of machine learning for data analysis and the design of electronic-nose (e-nose) sensor arrays. Full article