Volatile Fingerprints Research Articles

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids. This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted. We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

Effects of Variable-Temperature Roasting on the Flavor Compounds of Xinjiang Tannur-Roasted Mutton.

This study investigates the effect of variable-temperature roasting on the flavor compounds of Xinjiang tannur-roasted mutton. Gas chromatography coupled with ion mobility spectroscopy (GC-IMS) was used to compare and analyze the volatile components and flavor fingerprints of Xinjiang tannur-roasted mutton using variable-temperature electrically heated air roasting (VTR), constant-temperature electrically heated air roasting (EHAR), and constant-burning charcoal roasting (BCR) techniques. The changes in fatty acids and free amino acids in Xinjiang tannur-roasted mutton under different roasting conditions were compared. By using GC-IMS analysis, 11 flavor compounds, including 4-methyl-3-penten-2-one, isoamyl propionate, trans-2-heptenal, trans-2-heptenal, 2-hexanone, n-hexanol, 2-hexenal, 2-ethylfuran, and ethyl 2-methylbutanoate, were identified as characteristic volatile compounds in the temperature-controlled electrothermal roasting of Xinjiang tannur-roasted mutton using the following conditions: 0-4 min, 300 °C; 5-10 min, 220 °C; and 11-17 min, 130 °C (VTR3). Through principal component analysis, it was found that the substances with the highest positive correlation with PC1 and PC2 were n-hexanol and 3-methylbutanol. The sensory evaluation showed that VTR3 had high acceptability (p < 0.05) and a fat flavor (p < 0.05). There was no significant difference in the total fatty acid (TFA) content between the VTR3 and burning charcoal roast for 1-17 min at 300 °C (BCR3) (p > 0.05), but it was lower than that in the other experimental groups (p < 0.05). The lowest proportion of glutamic acid content in VTR3 was 22.44%, and the total free amino acid content in the electric thermostatic roasting for the 1-17 min, 300 °C (EHAR3) group (347.05 mg/100 g) was significantly higher than that in the other experimental groups (p < 0.05). By using Spearman correlation analysis, the roasting loss rate showed a highly significant negative correlation with essential amino acids (EAAs), non-essential amino acids (NEAAs), and total free amino acids (TAAs) (the correlation coefficients (r) were 0.82, 0.87, and 0.87, respectively) with p < 0.01. There was no correlation between changes in the free amino acid content and fatty acid content (p > 0.05). By using Differential scanning calorimetry (DSC) analysis, we also found that there was no significant difference in peak temperature (Tp) between the VTR3 and EHAR experimental groups (p > 0.05). Variable temperature electric heating can affect the flavor of lamb, and there are significant differences in the content of flavor precursors such as fatty acids and amino acids in Xinjiang tannur-roasted mutton.

Rapid headspace analysis of commercial spearmint and peppermint teas using volatile 'fingerprints' and an electronic nose.

Spearmint and peppermint teas are widely consumed around the world for their flavor and therapeutic properties. Dynamic headspace sampling (HS) coupled to gas chromatography/mass spectrometry (GC-MS) with principal component analysis (PCA) of 'fingerprint' volatile profiles were used to investigate 27 spearmint and peppermint teas. Additionally, comparisons between mint teas were undertaken with an electronic nose (enose). Twenty compounds, all previously known in the literature, were identified using HS-GC-MS. PCA found distinct differences between the fingerprint volatile profiles of spearmint, peppermint and spearmint/peppermint combination teas. HS-GC-MS analysis performed with an achiral column allowed faster processing time and yielded tighter clustering of PCA tea groups than the analysis which used a chiral column. Two spearmint outliers were detected. One showed a high degree of variation in volatile composition and a second wholly overlapped with the peppermint PCA grouping. Enose analysis separated all treatments with no overlaps. Characterizing the volatile fingerprints of mint teas is critical to quality control for this valuable agricultural product. The results of this study show that fingerprint volatile profiles and enose analysis of mint teas are distinctive and could be used to rapidly identify unknown samples. With specific volatile profiles identified for each tea, samples could be tested in the laboratory, or potentially on a farm or along the supply chain, to confirm the provenance and authenticity of mint food or beverage commodities. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Effect of Saccharomyces cerevisiae Strain TIA2019A Concentration as Starter Culture on Volatile Aroma Fingerprint of Cocoa Beans and Sensory Perception of End-chocolate

Effect of Saccharomyces cerevisiae Strain TIA2019A Concentration as Starter Culture on Volatile Aroma Fingerprint of Cocoa Beans and Sensory Perception of End-chocolate

Insight into flavor difference of cherry (Prunus avium L.) grown in facility environment and outdoors through metabolomics and correlation analysis

The flavor profiles of cherries cultivated in greenhouse and those grown in open fields show significant variations, however, the underlying flavor-contributing factors remain unidentified. Hence, a joint investigation with widely targeted metabolomics analysis, volatile fingerprint analysis, and descriptive sensory analysis for the Russia 8 and Tieton cherry cultivars was conducted using UPLC-MS/MS and GC × GC-TOFMS to clarify the flavor differences of open-air and greenhouse-grown cherries. The study found that open-air cultivation could lead to the accumulation of non-volatile flavor substances and prompted appearance of higher acidity, astringency, plum-like flavor, and fresh herb notes; most of differential metabolites were significantly positively correlated with astringency, plum-like flavor and bitterness. Through correlation analysis and path analysis, potential flavor components and key important pathways contributing to flavor disparities were provided, and light intensity, soil moisture content, temperature and humidity were inferred as the main factors affecting the flavor profiles of open-air and greenhouse-grown cherries.

Characterization of the flavor profile of UHT milk during shelf-life via volatile metabolomics fingerprinting combined with chemometrics

Ultra-high temperature (UHT) milk is popular among consumers. However, its flavor and texture change in its shelf life. Flavor is highly determinative for the success of dairy products and for consumers’ willingness to buy. It is important to milk producers to ensure the optimal flavor of their products in the shelf life. In order to be able to control and predict the flavor quality of UHT milk during the shelf life, this study compared the variations in sensory quality, volatile aroma release and backbone flavor factors and developed a discriminant model to assess flavor quality based on flavouromics data of five competing milk sample during storage. Using partial least squares discriminant analysis (PLS-DA) with Electronic-nose (E-nose) data excellent classification sensitivity and specificity were achieved compared to models based on gas chromatography-mass spectrometry (GC–MS) data. The PLS-DA model using E-nose data exhibited a 100% correct classification rate for the storage period, and a 92% correct rate based on the eight variable importance in the projection (VIP) elements screened for volatile components from different groups. The discriminative model developed herein based on E-nose combined with chemometrics demonstrated advantages such as speed, efficiency, and environmental friendliness. This method shows promise as a precise tool for analyzing aroma changes in UHT milk during its shelf life, and provide support for controlling the flavor substances and milk product development.

A novel headspace solid-phase microextraction arrow method employing comprehensive two-dimensional gas chromatography–mass spectrometry combined with chemometric tools for the investigation of wine aging

BackgroundOmics is used as an analytical tool to investigate wine authenticity issues. Aging authentication ensures that the wine has undergone the necessary maturation and developed its desired organoleptic characteristics. Considering that aged wines constitute valuable commodities, the development of advanced omics techniques that guarantee aging authenticity and prevent fraud is essential. ResultsΑ solid phase microextraction Arrow method combined with comprehensive two-dimensional gas chromatography-mass spectrometry was developed to identify volatiles in red wines and investigate how aging affects their volatile fingerprint. The method was optimized by examining the critical parameters that affect the solid phase microextraction Arrow extraction (stirring rate, extraction time) process. Under optimized conditions, extraction took place within 45 min under stirring at 1000 rpm. In all, 24 monovarietal red wine samples belonging to the Xinomavro variety from Naoussa (Imathia regional unit of Macedonia, Greece) produced during four different vintage years (1998, 2005, 2008 and 2015) were analyzed. Overall, 237 volatile compounds were tentatively identified and were treated with chemometric tools. Four major groups, one for each vintage year were revealed using the Hierarchical Clustering Analysis. The first two Principal Components of Principal Component Analysis explained 86.1% of the total variance, showing appropriate grouping of the wine samples produced in the same crop year. A two-way orthogonal partial least square – discriminant analysis model was developed and successfully classified all the samples to the proper class according to the vintage age, establishing 17 volatile markers as the most important features responsible for the classification, with an explained total variance of 88.5%. The developed prediction model was validated and the analyzed samples were classified with 100% accuracy according to the vintage age, based on their volatile fingerprint. SignificanceThe developed methodology in combination with chemometric techniques allows to trace back and confirm the vintage year, and is proposed as a novel authenticity tool which opens completely new and hitherto unexplored possibilities for wine authenticity testing and confirmation.

Uncovering the Counterfeit: A study of whiskey authenticity through volatile organic compound fingerprinting, aroma and color sensory analysis

This study presents a method employing gas chromatography coupled with mass spectrometry and headspace solid-phase microextraction (HS-SPME-GC–MS), supplemented with chemometrics (Soft independent modelling of class analogies - SIMCA), to analyze volatile organic compound (VOCs) profiles in suspect whiskey samples. Furthermore, a sensory analysis of aroma and color was conducted with a panel of 52 non-trained volunteers to evaluate their ability to discriminate and preference for counterfeit whiskeys. The HS-SPME-GC–MS method successfully distinguished 41 seized samples from authentic beverages. Interestingly, sensory analysis revealed that panelists could differentiate between counterfeit and authentic samples with a reference standard but did not consistently show a preference for aroma. In some cases, there was even a preference for the color of counterfeit whiskeys. The findings suggest that sensorial tests alone may not effectively distinguish counterfeit from authentic whiskeys, especially for non-expert consumers, highlighting the need for analytical instrumentation methods in fraud detection.

Comparative analysis of volatile fingerprints between different geographical origins and ornamental cultivars of white standard Chrysanthemum (Dendranthema grandiflorum)

BackgroundWhite standard Dendranthema grandiflorum (D. grandiflorum) is widely used for ornamental purposes. The volatile organic components (VOCs) of D. grandiflorum vary depending on the geographical origin and cultivar, which influence the market value. Here, we applied headspace extraction coupled with gas chromatography–mass spectrometry (HS–GC–MS) for the first time to determine the different origins (Korea, China, Vietnam) of D. grandiflorum in three genotypes (Baekgang, Jinba, Iwa-no-hakusen) based on volatile fingerprints and to assess the correlation among the metabolites identified in Korean and non-Korean D. grandiflorum.ResultsA total of 41 VOCs, mainly monoterpenoids, sesquiterpenoids, alcohols, and fatty acids, were identified. Principal component analysis showed that instead of geographical origin, genotype influences D. grandiflorum fingerprints. Cultivar discrimination was mainly affected by the metabolites associated with the alpha-terpinyl cation pathway. The orthogonal partial least squares discriminant analysis model achieved 100% and 93.3% accuracies in the calibration and validation sets, respectively. The results of volcano plots and clustering analysis, sesquiterpenoids were significantly more abundant in non-Korean than in Korean, whereas monoterpenoids were abundant in Korean samples.Conclusions41 makers confirmed robustness maintenance over two years. These findings can be useful for reliably identifying the geographical origin of D. grandiflorum and providing a comprehensive understanding of VOCs in D. grandiflorum cultivars grown in different countries of origins.Graphical

Electronic nose: a tool to verify the PDO declaration of Portuguese olive oils

The volatile and olfactory profiles of three Portuguese olive oils with protected designations of origin (PDO) were studied: “Azeite do Alentejo Interior”, “Azeites da Beira Interior”, and “Azeite de Trás-os-Montes”. Seven classes of volatiles were identified, with aldehydes, followed by hydrocarbons and alcohols, the most prevalent (5.63, 2.92, and 2.79 mg/kg olive oil, respectively). The “Azeites da Beira Interior” oils exhibited the highest amount of volatiles (18.2 ± 4.6 mg/kg olive oil) compared to the oils from the other two PDOs. Ten positive olfactory sensations were detected, and a significant effect of the PDO on the intensities of fruity, apple, cabbage, tomato, dry and fresh herbs was observed. Specifically, “Azeite do Alentejo Interior” PDO oils were characterized as fruity-ripe, while “Azeite de Trás-os-Montes” PDO oils were labelled as fruity-green. Conversely, “Azeites da Beira Interior” PDO oils encompassed both fruity-ripe and fruity-green oils. Unique volatile and olfactory fingerprints were established for each PDO, allowing the linear discrimination of the oils according to the PDO, with a predictive sensitivity of 98.0 ± 4.2% (repeated K-fold-CV). Furthermore, a lab-made electronic nose successfully discriminated the studied oils based on the PDO, with a predictive accuracy of 99.7 ± 2.0% (repeated K-fold-CV). This device also allowed predicting the concentrations of the three main volatile classes found in the oils through multiple linear regression models (R2 ≥ 0.923 ± 0.101 and RMSE ≤ 1.32 ± 0.72 mg/kg oil; repeated K-fold-CV). These findings underscore the potential of the electronic nose as a reliable traceability tool to authenticate the PDO declaration of Portuguese olive oils, and broaden its use beyond non-PDO oils from a specific geographical area to encompass a national scale.Graphical abstract

Screening tools combined with multivariate data analysis to predict or confirm virgin olive oil classification by the Panel test

AbstractA particular aspect of quality control of virgin olive oil (VOO) is the mandatory application, together with chemical and instrumental determinations, of a standardized and official method for sensory assessment. The latter, known as Panel test, is carried out by trained assessors and contributes to the classification of VOOs into three commercial categories (extra virgin, virgin, and lampante). One drawback of this method is related to the large number of samples to be analyzed, compared to the work capacity of a sensory panel, especially during the selection for purchase by companies that blend and market virgin oils and the quality control conducted by the authorities to verify the declared commercial category. For this reason, it is helpful to develop and validate robust and rapid screening methods, based on volatile fingerprints, to preclassify each sample into one of the three commercial categories. Considering the strict relation between volatile compounds and the main sensory attributes (fruity and defects), a gas‐chromatographic volatile fingerprint can be the right choice. In this paper, the comparison of two emerging techniques, namely, headspace‐gas chromatography‐ion mobility spectrometry (HS‐GC‐IMS) and flash‐gas chromatography (FGC), applied on a sample set of 49 VOOs, using calibrations previously built with a larger number of samples, is presented. The number of correctly classified samples, with respect to the commercial category determined by the Panel test, was satisfactory and comparable (92% for HS‐GC‐IMS, and 94% for FGC), confirming the effectiveness of both methods and the robustness of the predictive models.Practical Applications: The demand for rapid screening tools to reduce the number of samples to be assessed by the Panel test has increased in recent years. The validation of robust models and their joint adoption by companies that market VOOs as well as official control bodies could reduce nonconformities and increase the batches of VOO being controlled, thus better protecting the consumer. Therefore, it is desirable to have different tools available to analyze volatile compounds, together with the associated calibration models, along with detailed instructions for their application, to have different alternatives that suit the equipment of individual laboratories.

Analysis and application of volatile metabolic profiles of Escherichia coli: a preliminary GC-IMS-based study.

Nosocomial infections caused by Escherichia coli (E. coli) may pose serious risks to patients, and early identification of pathogenic bacteria and drug sensitivity results can improve patient prognosis. In this study, we clarified the composition and relative content of volatile organic compounds (VOCs) generated by E. coli in tryptic soy broth (TSB) using gas chromatography-ion mobility spectrometry (GC-IMS). We explored whether imipenem (IPM) could be utilized to differentiate between carbapenem-sensitive E. coli (CSEC) and carbapenem-resistant E. coli (CREC). The results revealed that 36 VOCs (alcohols, aldehydes, acids, esters, ketones, pyrazines, heterocyclic compounds, and unknown compounds) were detected using GC-IMS. Besides, the results indicated that changes in the relative content of VOCs as well as changes in the signal intensity of fingerprints were able to assess the growth state of bacteria during bacterial growth and help identify E. coli. Lastly, under selective pressure of IPM, volatile fingerprints of E. coli could be employed as a model to distinguish CSEC from CREC strains.

Volatile Fingerprint and Differences in Volatile Compounds of Different Foxtail Millet (Setaria italica Beauv.) Varieties.

Aroma components in foxtail millet are one of the key factors in origin traceability and quality control, and they are associated with consumer acceptance and the corresponding processing suitability. However, the volatile differences based on the foxtail millet varieties have not been studied further. The present study was undertaken to develop the characteristic volatile fingerprint and analyze the differences in volatile compounds of 20 foxtail millet varieties by electronic nose (E-Nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS). A total of 43 volatile compounds were tentatively identified in foxtail millet samples, 34 and 18 by GC-IMS and GC-MS, respectively. Aldehydes, alcohols, and ketones were the major volatile compounds, and the hexanal content was the highest. The characteristic volatile fingerprint of foxtail millet was successfully constructed. A total of 39 common volatile compounds were found in all varieties. The content of hexanal, heptanal, 1-pentanol, acetophenone, 2-heptanone, and nonanal were explored to explain the aroma characteristics among the different varieties, and different varieties can be separated based on these components. The results demonstrate that the combination of E-Nose, GC-IMS, and GC-MS can be a fast and accurate method to identify the general aroma peculiarities of different foxtail millet varieties.

Volatile Fingerprint Mediates Yeast-to-Mycelial Conversion in Two Strains of Beauveria bassiana Exhibiting Varied Virulence.

Beauveria bassiana is a dimorphic and entomopathogenic fungus with different ecological roles in nature. In pathogenic fungi, yeast-to-mycelial conversion, which is controlled by environmental factors, is required for virulence. Here, we studied the effects of different stimuli on the morphology of two B. bassiana strains and compared the toxicities of culture filtrates. In addition, we explored the role of volatiles as quorum sensing-like signals during dimorphic transition. The killing assays in Caenorhabditis elegans (Nematoda: Rhabditidae) showed that strain AI2 isolated from a mycosed insect cadaver had higher toxicity than strain AS5 isolated from soil. Furthermore, AI2 showed earlier yeast-to-mycelial switching than AS5. However, an increase in inoculum size induced faster yeast-to-mycelium conversion in AS5 cells, suggesting a cell-density-dependent phenomenon. Gas chromatography-mass spectrometry (GC-MS) analyses showed that the fingerprint of the volatiles was strain-specific; however, during the morphological switching, an inverse relationship between the abundance of total terpenes and 3-methylbutanol was observed in both strains. Fungal exposure to 3-methylbutanol retarded the yeast-to-mycelium transition. Hence, this study provides evidence that volatile compounds are associated with critical events in the life cycle of B. bassiana.

Characterization of Italian honeys: integrating volatile and physico-chemical data

This article focuses on the comprehensive characterization of Italian honeys using various physico-chemical analyses and their volatile organic compounds (VOCs) fingerprint obtained through the PTR-ToF-MS technology. Honey characteristics, including pH, electrical conductivity, moisture content, hydroxymethylfurfural (HMF), and sugar content, were analyzed to assess their quality and origin. Honey samples from different flowers, including acacia, chestnut, citrus, linden, and multifloral, were collected and investigated. Furthermore, a few aged honeys were collected and analyzed and compared with the fresh ones. Physico-chemical analysis revealed that chestnut honey is characterized by high pH and EC values. Acacia honey has a higher fructose content, while aging appears to influence HMF levels, a vital indicator of honey quality, with aged samples exhibiting significant increases in HMF content. The VOC profiles have been found to vary among different honey types, suggesting that VOCs could be used as indicators of honey origin. Multivariate statistical analyses, such as partial least squares discriminant analysis (PLS-DA), have been applied to the VOCs data to differentiate honey types based on their volatile profiles. Acacia honey exhibited different physicochemical parameters but on the contrary, in the VOCs analysis, it displayed similarities with the linden honey due to their shared low emissions of volatile compounds. Citrus honey had similar chemical parameters to linden and multifloral honeys, but its distinctive VOCs emission allowed for a more accurate identification. In conclusion, the analysis performed with the PTR-ToF-MS was successful in obtaining specific volatile fingerprints of those samples and was effective for improving the characterization of honeys.

Volatile fingerprints of beef cooking methods using sol–gel‐based solid‐phase microextraction (SPME) and direct analysis in real‐time mass spectrometry (DART‐MS)

RationaleThe aroma profile of food is a complex mixture of volatile compounds that constitutes a major component of the overall eating experience. The food service industry and chefs therefore constantly seek ways to investigate and thereby enhance the aroma profile. Oven cooking, sous vide and pan fry are three cooking methods of beef commonly practised by chefs. Near real‐time analysis of volatile compounds from these three cooking methods will provide insight into respective volatile fingerprints and help improve cooking techniques.MethodsVolatile compounds from three beef cooking methods were captured using an in‐house sol–gel based solid phase microextraction (SPME) method and analysed using direct analysis in real‐time mass spectrometry (DART‐MS). A volatile organic compound (VOC) standard was used to demonstrate successful implementation of the sol–gel coating technique. Volatile features discriminating the three cooking methods were shortlisted and statistically assessed by univariate and multivariate analyses.ResultsThe VOC standard was successfully adsorbed by the sol–gel method and detected by DART‐MS. Hierarchical cluster analysis clearly demarcated three beef cooking methods based on their volatile fingerprints. Out of 65 significant features differentiating the cooking methods, 50 were at highest concentrations from pan‐fry cooking only, followed by 14 with highest concentrations from oven cooking followed by pan frying. Sous vide followed by pan frying showed lowest concentrations of almost all volatile features.ConclusionsThe sol–gel‐based solid‐phase microextraction technique combined with DART‐MS was successful in differentiating beef cooking methods based on their volatile fingerprints. A workflow for rapid assessment of the volatile profile from beef cooking methods was established, providing a baseline to further explore volatile profiles from other key ingredients.

Investigating sensory-classified roasted arabica coffee with GC × GC-TOFMS and chemometrics to understand potato taste defect

The presence of flavor defects in coffee beans can negatively impact quality, the consumer experience, and commercial trade. Potato taste defect (PTD), a flavor defect specific to East African coffee, is often characterized by a musty, vegetable-like aroma. While previous work has correlated PTD with the presence of 2-isopropyl-3-methoxypyrazine (IPMP), additional changes in the volatile profile of these beans can further amplify the distinct odor of this defect. The aim of this work was to develop a volatile fingerprint of PTD in roasted arabica coffee using headspace solid-phase microextraction coupled to comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (HS-SPME-GC × GC-TOFMS) and chemometrics. Examination of the HS-SPME-GC × GC-TOFMS data with tile-based Fisher ratio (F-ratio) analysis discovered 359 analytes that differentiated clean coffee samples from those impacted by severe PTD (p-value < 0.01). It was determined that 327 of the identified analytes were more prevalent in the clean coffee samples while 32 analytes, including IPMP, exhibited higher signals in the impacted coffee samples. Principal components analysis (PCA) of the F-ratio results demonstrated that the coffee samples clustered based on the presence of PTD. Partial least squares (PLS) regression modeling further demonstrated that the compounds discovered by F-ratio analysis were correlated with PTD by accurately predicting the concentration of IPMP in the samples. Investigation of the compounds highly weighted in both the PCA and PLS loadings suggest that the presence of microorganisms on coffee beans after antestia bug damage could be a potential pathway for PTD. This damage results in an overall decrease of analytes that are known to have positive sensory contributions to coffee aroma. Collectively, the volatile fingerprint shown herein illustrates that PTD alters the biochemical process in coffee beans.

Fingerprint Analysis of Volatile Flavor Compounds in Crassostrea gigas of Different Ploidy and Gender under High-Temperature Incubation.

In this study, diploid, triploid, and tetraploid Crassostrea gigas samples were subjected to gas chromatography and ion mobility (GC-IMS) to identify and analyze volatile compounds and flavor fingerprints under conditions of high-temperature incubation. The GC-IMS technology identified a total of 54 volatile components in C. gigas. The contents of 1-octen-3-ol, butyl pentanoate, p-methyl anisole, and 2-methyl-2-hepten-6-one in male oysters were significantly higher than in females, while the contents of phenylacetaldehyde, benzaldehyde, 2-ethyl-3-methylpyrazine, 2-ethylfuran, and 2,4-hexadienal in female oysters were significantly higher than in males. The contents of non-3-en-2-one-M and 1-pentanol in diploids were significantly higher than in triploids and tetraploids, while the content of 2,4-hexadienal in tetraploids was significantly higher than in diploids and tetraploids. The contents of ethyl acetate, ethyl-2-butenoate, and butanal in tetraploids were significantly higher than those in diploids and triploids. The results of a principal components analysis showed that different samples were relatively independently clustered, allowing the ability to distinguish different oyster samples. The chemical fingerprints of volatile compounds of C. gigas with different ploidy and gender under high-temperature incubation were established, and the volatile substance contours of C. gigas were visualized. The results provide a reference for distinguishing the ploidy and gender of C. gigas under conditions of high-temperature incubation.

Artificial Olfactory System for Distinguishing Oil-Contaminated Soils

Oil-contaminated soils are a major environmental problem for Kazakhstan. Oil spills or leaks lead to profound changes in the physical and agrochemical properties of the soil and the accumulation of hazardous substances. Whilst there are many remote sensing techniques and complex laboratory methods for oil spill detection, developing simple, reliable, and inexpensive tools for detecting the presence of pollutants in the soil is a relevant research task. The study aims to research the possibilities of an electronic nose combining a chemical sensor array with pattern recognition techniques to distinguish volatile organic compounds from several types of hydrocarbon soil pollutants. An electronic nose system was assembled in our laboratory. It includes eight gas metal oxide sensors, a humidity and temperature sensor, an analog-digital processing unit, and a data communication unit. We measured changes in the electrical conductivity of sensors in the presence of volatile organic compounds released from oil and petroleum products and samples of contaminated and uncontaminated soils. The list of experimental samples includes six types of soils corresponding to different soil zones of Kazakhstan, crude oil from three oil fields in Kazakhstan, and five types of locally produced fuel oil (including gasoline, kerosene, diesel fuel, engine oil, and used engine oil). We used principal component analysis to statistically process multidimensional sensor data, feature extraction, and collect the volatile fingerprint dataset. Pattern recognition using machine learning algorithms made it possible to classify digital fingerprints of samples with an average accuracy of about 92%. The study results show that electronic nose sensors are sensitive to soil hydrocarbon content. The proposed approach based on machine olfaction is a fast, accurate, and inexpensive method for detecting oil spills and leaks, and it can complement remote sensing methods based on computer vision.

Feasibility of using volatile urine fingerprints for the differentiation of sexually transmitted infections

Sexually transmitted infections (STIs) are a public health problem worldwide, and current diagnostic methods have certain limitations. In recent years, volatile organic compounds (VOCs) have been studied as an alternative diagnostic method. Due to this, this study aimed to detect, in vaginal swabs and urine samples, VOCs emitted by highly prevalent STIs-causing bacteria (Chlamydia trachomatis, Mycoplasma genitalium, and Neisseria gonorrhoeae) to identify potential biomarkers that allow the detection of these STIs. VOCs detected in urine samples showed a better differentiation of patients with STIs due to C. trachomatis from those not infected, with 2,6-dimethyl-4-heptanone as the volatile compound most related to the presence of this bacterium. Among the VOCs most related to M. genitalium in urine, 4-methyltetradecane and 2-methylpentadecane stood out, while 3,4,4-trimethyl-2-cyclohexen-1-one was the VOC most closely related to N. gonorrhoeae infection. Moreover, C12 alcohols were the main VOC family associated with positive samples in all three bacteria, which could indicate the presence of aldehyde reductases in their metabolism. In contrast, alcohols such as 3-methyl-1-heptanol and 1-octanol, as well as dimethyl esters, were more associated with negative samples and may be useful in ruling out an STI caused by one of these three bacteria. In short, the VOCs identified as potential biomarkers in patients with infection by C. trachomatis, M. genitalium, or N. gonorrhoeae could be used in the early diagnosis of these STIs, quickly interrupting the chain of transmission, especially interesting in asymptomatic patients.Key points• Sexually transmitted infections are a serious public health problem worldwide.• The study of VOCs in multiple infections is increasing in recent years.• The identification of volatile biomarkers could allow new diagnostic methods.Graphical abstract