Peak assignment in multi-capillary column–ion mobility spectrometry using comparative studies with gas chromatography–mass spectrometry for VOC analysis

Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry

Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.

Patterns of volatile organic compounds in excrements of preterm neonates

BACKGROUND

As neonates are susceptible for many diseases, establishing noninvasive diagnostic methods is desirable. We hypothesized that volatile organic compounds (VOCs) could be successfully measured in diaper samples.

METHODS

We performed a feasibility study to investigate whether ambient air-independent headspace measurements of the VOC profiles of diapers from premature infants can be conducted using ion mobility spectrometer coupled with multi-capillary columns (B & S Analytik GmbH).

RESULTS

We analysed 39 diapers filled with stool (n = 10) or urine (n = 20) respectively, using empty diapers as a control (n = 9). A total of 158 different VOCs were identified, and we classified the content of the diapers (urine or stool) according to their VOC profiles with a significance level of p < 0.05.

CONCLUSIONS

We have developed a novel method to study headspace VOC profiles of biosamples using ion mobility spectrometry coupled with multi-capillary columns. Using this method, we have characterized the VOC profiles of stool and urine of preterm neonates. Future studies are warranted to characterize specific VOC profiles in infections and other diseases of the preterm neonate, thus establishing quick and noninvasive diagnostics in the routine care of the highly vulnerable preterm and term neonates.

Simultaneous measurement of inhaled air and exhaled breath by double multicapillary column ion-mobility spectrometry, a new method for breath analysis: results of a feasibility study

The high sensitivity of the methods applied in breath analysis entails a high risk of detecting analytes that do not derive from endogenous production. Consequentially, it appears useful to have knowledge about the composition of inhaled air and to include alveolar gradients into interpretation.

The current study aimed to standardise sampling procedures in breath analysis, especially with multicapillary column ion-mobility spectrometry (MCC-IMS), by applying a simultaneous registration of inhaled air and exhaled breath.

A “double MCC-IMS” device, which for the first time allows simultaneous analysis of inhaled air and exhaled breath, was developed and tested in 18 healthy individuals. For this, two BreathDiscovery instruments were coupled with each other.

Measurements of inhaled air and exhaled breath in 18 healthy individuals (mean age 46±10.9 years; nine men, nine women) identified 35 different volatile organic compounds (VOCs) for further analysis. Not all of these had positive alveolar gradients and could be regarded as endogenous VOCs: 16 VOCs had a positive alveolar gradient in mean; 19 VOCs a negative one. 12 VOCs were positive in >12 of the healthy subjects.

For the first time in our understanding, a method is described that enables simultaneous measurement of inhaled air and exhaled breath. This facilitates the calculation of alveolar gradients and selection of endogenous VOCs for exhaled breath analysis. Only a part of VOCs in exhaled breath are truly endogenous VOCs. The observation of different and varying polarities of the alveolar gradients needs further analysis.

Simultaneous analysis of inhaled air and exhaled breath by a newly invented double MCC-IMS device shows that exhaled breath contains confounding exogeneous analytes and only a smaller number of truly endogenous VOCs, which can be used for further analysishttps://bit.ly/3HGVzV5

Enhanced Selectivity in Volatile Organic Compound Gas Sensors Based on ReS2-FETs under Light-Assisted and Gate-Bias Tunable Operation

Using a single-device two-dimensional (2D) rhenium disulfide (ReS₂) field-effect transistor (FET) with enhanced gas species selectivity by light illumination, we reported a selective and sensitive detection of volatile organic compound (VOC) gases. 2D materials have the advantage of a high surface-area-to-volume ratio for high sensitivity to molecules attached to the surface and tunable carrier concentration through field-effect control from the back-gate of the channel, while keeping the top surface open to the air for chemical sensing. In addition to these advantages, ReS₂ has a direct band gap also in multilayer cases, which sets it apart from other transition-metal dichalcogenides (TMDCs). We take advantage of the effective response of ReS₂ to light illumination to improve the selectivity and gas-sensing efficiency of a ReS₂-FET device. We found that light illumination modulates the drain current response in a ReS₂-FET to adsorbed molecules, and the sensing activity differs depending on the gas species used, such as acetone, ethanol, and methanol. Furthermore, wavelength and carrier density rely on certain variations in light-modulated sensing behaviors for each chemical. The device will distinguish the gas concentration in a mixture of VOCs using the differences induced by light illumination, enhancing the selectivity of the sensor device. Our results shed new light on the sensing technologies for realizing a large-scale sensor network in the Internet-of-Things era.

Full Workflows for the Analysis of Gas Chromatography-Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

Strategy for the identification of flavor compounds in e-liquids by correlating the analysis of GCxIMS and GC-MS

This work presents a strategy to correlate the results from gas chromatography coupled ion mobility spectrometry (GCxIMS) and mass spectrometry (GC-MS) to enable a simpler and cheaper analysis of flavor compounds in e-liquids. The use of the retention index for GCxIMS measurements was validated for its application to correlate results with GC-MS data. The easy detection of the GCxIMS for substances at concentrations as low as 1 μg/L can therefore be combined with the identification power of the MS. The use of the MS' mass signals and wide-spread availability of mass spectra libraries reduces the effort necessary to choose the correct reference standards for the identification of unknown substances. Between both detectors, correlating of the retention time indices was achieved for ± 1%. 2-Alkanones were used as an alternative reference point for the IMS and the well-established alkanes for the MS. The application on flavor compounds in e-liquids shows equal or better results than those presented for more complex, hardware-based correlations like line splitting. Additionally, the inverted reduced mobility combined with the retention index of a non-polar column enables simple extrapolation for the confirmation of expected substances as well as the use in a transferable database. For the first time, this comprehensive application allows an extensive, simplified, and cheap identification of flavor compounds in e-liquids by GCxIMS and GC-MS.

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fast-response gas-monitoring systems. However, the conventional plasma discharge system is bulky, operates at a high temperature, and inappropriate for volatile organic compounds (VOCs) concentration detection. Therefore, we report a machine learning (ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer, which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment. Based on the charge accumulation mechanism, a multi-switched manipulation triboelectric nanogenerator (SM-TENG) can provide a direct current (DC) bias at the order of a few hundred, which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs, and their mixtures, with a special tip-plate electrode configuration. Aiming to tackle the grand challenge in the detection of multiple VOCs, the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms, which significantly enhance the detection ability of the SM-TENG based VOC analyzer, showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.

Geographical origin identification of two salmonid species via flavor compound analysis using headspace-gas chromatography-ion mobility spectrometry combined with electronic nose and tongue

The flavor of salmonids is affected by species and origin. Sources of salmonid fish fillets are complex and difficult to identify and label fraud occasionally occurs in the market. In this study, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), electronic nose, electronic tongue and amino acid detection technologies were used to analyze flavor compounds in two salmonid species from different geographical origins. Fingerprints of volatile compounds of salmonid were constructed using HS-GC-IMS technology. Free amino acid (FAA) content differed in salmonids from different geographical origins. Regarding salmonid odor, HS-GC-IMS analysis results were basically consistent with those of the electronic nose. Regarding taste, the conclusions drawn from the electronic tongue were consistent with the amino acid test results. Therefore, our results demonstrate that flavor compounds can be used to distinguish salmonids from different geographical origins, providing a new dimension to food safety and authenticity. Furthermore, HS-GC-IMS, electronic nose and tongue can be used as tools in the market to identify food fraud.

Determination of volatile organic compounds by HS-GC-IMS to detect different stages of Aspergillus flavus infection in Xiang Ling walnut

The aim of this study was to evaluate the performance of volatile organic compounds (VOCs) for evolution monitoring and early detection of Aspergillus flavus (A. flavus) contamination in walnuts. We successfully applied headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) to evaluate walnut VOC changes caused by A. flavus contamination. A total of 48 VOCs were identified in walnuts contaminated with A. flavus. After identification of VOCs, a heat map and principal component analysis (PCA) highlighted ethyl acetate-D, 3-methyl-2-butanol, and cyclohexanone as potential biomarkers specific to A. flavus contamination in walnuts. These results provided valid targets for the development of sensors to evaluate the early mold contamination in stored walnuts.

Limitations of GC-QTOF-MS Technique in Identification of Odorous Compounds from Wastewater: The Application of GC-IMS as Supplement for Odor Profiling

Odorous emissions from wastewater treatment plants (WWTPs) cause negative impacts on the surrounding areas and possible health risks on nearby residents. However, the efficient and reliable identification of WWTPs’ odorants is still challenging. In this study, odorous volatile organic compounds (VOCs) from domestic wastewater at different processing units were profiled and identified using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography quadrupole-time-of-flight mass spectrometry (GC-QTOF-MS). The GC-QTOF-MS results confirmed the odor contribution of sulfur organic compounds in wastewater before primary sedimentation and ruled out the significance of most of the hydrocarbons in wastewater odor. The problems in odorous compounds analysis using GC-QTOF-MS were discussed. GC-IMS was developed for visualized analysis on composition characteristics of odorants. Varied volatile compounds were detected by GC-IMS, mainly oxygen-containing VOCs including alcohols, fatty acids, aldehydes and ketones with low odor threshold values. The fingerprint plot of IMS spectra showed the variation in VOCs’ composition, indicating the changes of wastewater quality during treatment process. The GC-IMS technique may provide an efficient profiling method for the changes of inlet water and performance of treatment process at WWTPs.

BALSAM-An Interactive Online Platform for Breath Analysis, Visualization and Classification

The field of breath analysis lacks a fully automated analysis platform that enforces machine learning good practice and enables clinicians and clinical researchers to rapidly and reproducibly discover metabolite patterns in diseases. We present BALSAM-a comprehensive web-platform to simplify and automate this process, offering features for preprocessing, peak detection, feature extraction, visualization and pattern discovery. Our main focus is on data from multi-capillary-column ion-mobility-spectrometry. While not limited to breath data, BALSAM was developed to increase consistency and robustness in the data analysis process of breath samples, aiming to expand the array of low cost molecular diagnostics in clinics. Our platform is freely available as a web-service and in form of a publicly available docker container.

Specific and Highly Sensitive Detection of Ketone Compounds Based on p-Type MoTe2 under Ultraviolet Illumination

The selective and sensitive detection of chemical agents is demanded by a wide range of practical applications. In particular, sensing of volatile organic compounds (VOCs) at parts-per-billion level is critical for environmental monitoring, process control, and early diagnosis of human diseases. In this report, we demonstrate a specific and highly sensitive detection of ketone compounds using two-dimensional (2D) molybdenum ditelluride (MoTe₂). We investigated the effects of UV activation on the sensing performance to a variety of VOCs. It is found that the MoTe₂ field-effect transistor (FET) exhibits an opposite sensing response to ketone compounds before and after UV light activation, whereas the responses to other types of VOCs remain in the same direction regardless of the illumination. This unique behavior enables the discriminative detection of ketone molecules including acetone and pentanone from other VOCs in a gas mixture. The activation of UV light also results in a very high sensitivity and low detection limit toward acetone (∼0.2 ppm). Moreover, the MoTe₂ FET shows a stable sensing performance in a high humidity environment. The results demonstrate the potential of MoTe₂ as a promising candidate for high-performance acetone sensors in important applications such as human breath analysis. The scheme of light-tunable sensing can be applied to a broad range of sensing platforms based on 2D materials.

Detection of gas traces using semiconductor sensors, ion mobility spectrometry, and mass spectrometry

This article deals with the state-of-the-art instrumentation and application in the field of solid state gas sensorics, ion mobility spectrometry and mass-spectrometry-related research for the detection and measurements of low gas and vapor concentrations. The advantages and disadvantages of gas-analytical devices and systems are discussed, as well as the possibilities of its complex and/or complementary applications. Ion mobility spectrometry-mass spectrometry and subsequent techniques based on solid-state gas sensors are proposed for planned medical study.

Chemiresistive and Gravimetric Dual-Mode Gas Sensor toward Target Recognition and Differentiation

We demonstrate a dual-mode gas sensor for simultaneous and independent acquisition of electrical and mechanical signals from the same gas adsorption event. The device integrates a graphene field-effect transistor (FET) with a piezoelectric resonator in a seamless manner by leveraging multiple structural and functional synergies. Dual signals resulting from independent physical processes, i.e., mass attachment and charge transfer can reflect intrinsic properties of gas molecules and potentially enable target recognition and quantification at the same time. Fabrication of the device is based on standard Integrated Circuit (IC) foundry processes and fully compatible with system-on-a-chip (SoC) integration to achieve extremely small form factors. In addition, the ability of simultaneous measurements of mass adsorption and charge transfer guides us to a more precise understanding of the interactions between graphene and various gas molecules. Besides its practical functions, the device serves as an effective tool to quantitatively investigate the physical processes and sensing mechanisms for a large library of sensing materials and target analytes.

Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles

Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1].

Determination of volatile compounds by GC-IMS to assign the quality of virgin olive oil

The characterisation of different olive oil categories (extra virgin, virgin and lampante) using Ion Mobility Spectrometry (IMS) was improved by replacing the multicapillary column (MCC) with a capillary column (CC). The data obtained with MCC-IMS and CC-IMS were evaluated, studying both the global and the specific information obtained after the analysis of the volatile fraction of olive oils. A better differentiation of the oil categories was obtained employing CC vs MCC, since the classification percentage obtained with the CC-IMS was 92% as opposed to 87% obtained with MCC-IMS; although in productivity analytical terms, MCC offer a faster analysis than GC. The specific information obtained was also used to build a database, with a view to facilitating the characterization of specific attributes of olive oils. A total of 26 volatile metabolites (aldehydes, ketones, alcohols and esters) were identified. Finally, as revealed by an ANOVA test, some volatiles differed markedly in content among the different categories of oil. The data obtained confirms the potential of IMS as a reliable analytical screening technique, which can be used to assign the correct category to an olive oil sample.

Review on ion mobility spectrometry. Part 2: hyphenated methods and effects of experimental parameters

Ion Mobility Spectrometry (IMS) is a widely used and 'well-known' technique of ion separation in the gaseous phase based on the differences of ion mobilities under an electric field. This technique has received increased interest over the last several decades as evidenced by the pace and advances of new IMS devices available. In this review we explore the hyphenated techniques that are used with IMS, specifically mass spectrometry as an identification approach and a multi-capillary column as a pre-separation approach. Also, we will pay special attention to the key figures of merit of the ion mobility spectrum and how data sets are treated, and the influences of the experimental parameters on both conventional drift time IMS (DTIMS) and miniaturized IMS also known as high Field Asymmetric IMS (FAIMS) in the planar configuration. The present review article is preceded by a companion review article which details the current instrumentation and contains the sections that configure both conventional DTIMS and FAIMS devices. These reviews will give the reader an insightful view of the main characteristics and aspects of the IMS technique.

Exhaled volatile organic compounds discriminate patients with chronic obstructive pulmonary disease from healthy subjects

Chronic obstructive pulmonary disease (COPD) is a chronic airway inflammatory disease characterized by incompletely reversible airway obstruction. This clinically heterogeneous group of patients is characterized by different phenotypes. Spirometry and clinical parameters, such as severity of dyspnea and exacerbation frequency, are used to diagnose and assess the severity of COPD. The purpose of this study was to investigate whether volatile organic compounds (VOCs) could be detected in the exhaled breath of patients with COPD and whether these VOCs could distinguish COPD patients from healthy subjects. Moreover, we aimed to investigate whether VOCs could be used as biomarkers for classifying patients into different subgroups of the disease. Ion mobility spectrometry was used to detect VOCs in the exhaled breath of COPD patients. One hundred and thirty-seven peaks were found to have a statistically significant difference between the COPD group and the combined healthy smokers and nonsmoker group. Six of these VOCs were found to correctly discriminate COPD patients from healthy controls with an accuracy of 70%. Only 15 peaks were found to be statistically different between healthy smokers and healthy nonsmokers. Furthermore, by determining the cutoff levels for each VOC peak, it was possible to classify the COPD patients into breathprint subgroups. Forced expiratory volume in 1 second, body mass index, and C-reactive protein seem to play a role in the discrepancies observed in the different breathprint subgroups.

Exhaled breath analysis for lung cancer detection using ion mobility spectrometry

BACKGROUND

Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique.

METHODS

The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation.

RESULTS

A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%.

CONCLUSION

In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.

Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.

Evaluation of fast volatile analysis for detection of Botrytis cinerea infections in strawberry

Grey mold (Botrytis cinerea) is one of the major phytopathogens causing serious losses during strawberry postharvest and storage. B. cinerea-host interaction affect emissions of volatile compounds during infection resulting in a characteristic earthy, mushroom odor. Therefore, the objective of this study was to evaluate two analytical techniques based on fast volatile analysis on their performance for monitoring evolution and early detection of B. cinerea infections in strawberry. In a first experiment headspace multi-capillary column-ion mobility spectrometry (HS MCC-IMS) has been successfully used to evaluate development of strawberry aroma during shelflife. In a second experiment the same technique has been used to detect the degree of B. cinerea infection through changes in the volatile profile. Additionally, these samples were analyzed with headspace solid-phase-microextraction fast GC-MS (HS SPME fast GC-MS). Both HS MCC-IMS and HS SPME fast GC-MS could determine the changes in volatile composition as a function of the degree of B. cinerea infection as determined by an enzyme-linked immunosorbent assay (ELISA) and could be used to follow the evolution of infection. According to the ELISA data, some fruit were infected even without any symptoms and volatiles produced by the fungus may be overshadowed by the fruit volatiles. Therefore, both analytical techniques could not be used for early detection of B. cinerea infections. After identification of the volatile compounds and multivariate data analysis, potential biomarkers specific for B. cinerea were highlighted, being 3-methylbutanal, cis-4-decenal, 2-methyl-1-butanol, 2-methyl-1-propanol, 1-octen-3-one and 1-octen-3-ol.

Ion mobility spectrometry for microbial volatile organic compounds: a new identification tool for human pathogenic bacteria

Presently, 2 to 4 days elapse between sampling at infection suspicion and result of microbial diagnostics. This delay for the identification of pathogens causes quite often a late and/or inappropriate initiation of therapy for patients suffering from infections. Bad outcome and high hospitalization costs are the consequences of these currently existing limited pathogen identification possibilities. For this reason, we aimed to apply the innovative method multi-capillary column–ion mobility spectrometry (MCC-IMS) for a fast identification of human pathogenic bacteria by determination of their characteristic volatile metabolomes. We determined volatile organic compound (VOC) patterns in headspace of 15 human pathogenic bacteria, which were grown for 24 h on Columbia blood agar plates. Besides MCC-IMS determination, we also used thermal desorption–gas chromatography–mass spectrometry measurements to confirm and evaluate obtained MCC-IMS data and if possible to assign volatile compounds to unknown MCC-IMS signals. Up to 21 specific signals have been determined by MCC-IMS for Proteus mirabilis possessing the most VOCs of all investigated strains. Of particular importance is the result that all investigated strains showed different VOC patterns by MCC-IMS using positive and negative ion mode for every single strain. Thus, the discrimination of investigated bacteria is possible by detection of their volatile organic compounds in the chosen experimental setup with the fast and cost-effective method MCC-IMS. In a hospital routine, this method could enable the identification of pathogens already after 24 h with the consequence that a specific therapy could be initiated significantly earlier.