Data-Driven Approach to Modeling Microfabricated Chemical Sensor Manufacturing

We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (μPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.

A Metabolomics Assay to Diagnose Citrus Huanglongbing Disease and to Aid in Assessment of Treatments to Prevent or Cure Infection

Citrus greening disease, or Huanglongbing (HLB), has devastated citrus crops globally in recent years. The causal bacterium, 'Candidatus Liberibacter asiaticus', presents a sampling issue for qPCR diagnostics and results in a high false negative rate. In this work, we compared six metabolomics assays to identify HLB-infected citrus trees from leaf tissue extracted from 30 control and 30 HLB-infected trees. A liquid chromatography-mass spectrometry-based assay was most accurate. A final partial least squares-discriminant analysis (PLS-DA) model was trained and validated on 690 leaf samples with corresponding qPCR measures from three citrus varieties (Rio Red grapefruit, Hamlin sweet orange, and Valencia sweet orange) from orchards in Florida and Texas. Trees were naturally infected with HLB transmitted by the insect vector Diaphorina citri. In a randomized validation set, the assay was 99.9% accurate to classify diseased from nondiseased samples. This model was applied to samples from trees receiving plant defense-inducer compounds or biological treatments to prevent or cure HLB infection. From two trials, HLB-related metabolite abundances and PLS-DA scores were tracked longitudinally and compared with those of control trees. We demonstrate how our assay can assess tree health and the efficacy of HLB treatments and conclude that no trialed treatment was efficacious.

Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis

Analysis of volatile organic compounds (VOCs) can be an effective strategy to inspect the quality of horticultural commodities and following their degradation. In this work, we report that VOCs emitted by walnuts can be studied using gas chromatography-differential mobility spectrometry (GC-DMS), and those GC-DMS data can be analyzed to predict the rancidity of walnuts, i.e., classify walnuts into grades of freshness. Walnut kernels were assigned a class n depending on their level of freshness as determined by a peroxide assay. VOC samples were analyzed using GC-DMS. From these VOC data, a partial least square regression (PLSR) model provided a freshness prediction value m , which corresponded to the rancid class n when m = n ± 0.5 . The PLSR model had an accuracy of 80% to predict walnut grade and demonstrated a minimal root mean squared error of 0.42 for the m response variables (representative of walnut grade) with the GC-DMS data. We also conducted gas chromatography-mass spectrometry (GC-MS) experiments to identify volatiles that emerged or were enhanced with more rancid walnuts. The findings of the GC-MS study of walnut VOCs align excellently with the GC-DMS study. Based on our results, we conclude that a GC-DMS device deployed with a pre-trained machine learning model can be a very effective device for classifying walnut grades in the industry.

Characterization of the microbiome and volatile compounds in anal gland secretions from domestic cats (Felis catus) using metagenomics and metabolomics

Many mammals rely on volatile organic chemical compounds (VOCs) produced by bacteria for their communication and behavior, though little is known about the exact molecular mechanisms or bacterial species that are responsible. We used metagenomic sequencing, mass-spectrometry based metabolomics, and culturing to profile the microbial and volatile chemical constituents of anal gland secretions in twenty-three domestic cats (Felis catus), in attempts to identify organisms potentially involved in host odor production. We found that the anal gland microbiome was dominated by bacteria in the genera Corynebacterium, Bacteroides, Proteus, Lactobacillus, and Streptococcus, and showed striking variation among individual cats. Microbiome profiles also varied with host age and obesity. Metabolites such as fatty-acids, ketones, aldehydes and alcohols were detected in glandular secretions. Overall, microbiome and metabolome profiles were modestly correlated (r = 0.17), indicating that a relationship exists between the bacteria in the gland and the metabolites produced in the gland. Functional analyses revealed the presence of genes predicted to code for enzymes involved in VOC metabolism such as dehydrogenases, reductases, and decarboxylases. From metagenomic data, we generated 85 high-quality metagenome assembled genomes (MAGs). Of importance were four MAGs classified as Corynebacterium frankenforstense, Proteus mirabilis, Lactobacillus johnsonii, and Bacteroides fragilis. They represent strong candidates for further investigation of the mechanisms of volatile synthesis and scent production in the mammalian anal gland.

Portable chemical detection platform for on-site monitoring of odorant levels in natural gas

The adequate odorization of natural gas is critical to identify gas leaks and to reduce accidents. To ensure odorization, natural gas utility companies collect samples to be processed at core facilities or a trained human technician smells a diluted natural gas sample. In this work, we report a detection platform that addresses the lack of mobile solutions capable of providing quantitative analysis of mercaptans, a class of compounds used to odorize natural gas. Detailed description of the platform hardware and software components is provided. Designed to be portable, the platform hardware facilitates extraction of mercaptans from natural gas, separation of individual mercaptan species, and quantification of odorant concentration, with results reported at point-of-sampling. The software was developed to accommodate skilled users as well as minimally trained operators. Detection and quantification of six commonly used mercaptan compounds (ethyl mercaptan, dimethyl sulfide, n-propylmercaptan, isopropyl mercaptan, tert‑butyl mercaptan, and tetrahydrothiophene) at typical odorizing concentrations of 0.1-5 ppm was performed using the device. We demonstrate the potential of this technology to ensure natural gas odorizing concentrations throughout distribution systems.

Oxylipin concentration shift in exhaled breath condensate (EBC) of SARS-CoV-2 infected patients

Infection of airway epithelial cells with severe acute respiratory coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and lung injury with hypoxia. It is challenging to sample the lower airways non-invasively and the capability to identify a highly representative specimen that can be collected in a non-invasive way would provide opportunities to investigate metabolomic consequences of COVID-19 disease. In the present study, we performed a targeted metabolomic approach using liquid chromatography coupled with high resolution chromatography (LC-MS) on exhaled breath condensate (EBC) collected from hospitalized COVID-19 patients (COVID+) and negative controls, both non-hospitalized and hospitalized for other reasons (COVID-). We were able to noninvasively identify and quantify inflammatory oxylipin shifts and dysregulation that may ultimately be used to monitor COVID-19 disease progression or severity and response to therapy. We also expected EBC-based biochemical oxylipin changes associated with COVID-19 host response to infection. The results indicated ten targeted oxylipins showing significative differences between SAR-CoV-2 infected EBC samples and negative control subjects. These compounds were prostaglandins A2 and D2, LXA4, 5-HETE, 12-HETE, 15-HETE, 5-HEPE, 9-HODE, 13-oxoODE and 19(20)-EpDPA, which are associated with specific pathways (i.e. P450, COX, 15-LOX) related to inflammatory and oxidative stress processes. Moreover, all these compounds were up-regulated by COVID+, meaning their concentrations were higher in subjects with SAR-CoV-2 infection. Given that many COVID-19 symptoms are inflammatory in nature, this is interesting insight into the pathophysiology of the disease. Breath monitoring of these and other EBC metabolites presents an interesting opportunity to monitor key indicators of disease progression and severity.

Exhaled breath condensate profiles of U.S. Navy divers following prolonged hyperbaric oxygen (HBO) and nitrogen-oxygen (Nitrox) chamber exposures

Prolonged exposure to hyperbaric hyperoxia can lead to pulmonary oxygen toxicity (PO2tox). PO2tox is a mission limiting factor for special operations forces divers using closed-circuit rebreathing apparatus and a potential side effect for patients undergoing hyperbaric oxygen (HBO) treatment. In this study, we aim to determine if there is a specific breath profile of compounds in exhaled breath condensate (EBC) that is indicative of the early stages of pulmonary hyperoxic stress/PO2tox. Using a double-blind, randomized 'sham' controlled, cross-over design 14 U.S. Navy trained diver volunteers breathed two different gas mixtures at an ambient pressure of 2 ATA (33 fsw, 10 msw) for 6.5 h. One test gas consisted of 100% O2(HBO) and the other was a gas mixture containing 30.6% O2with the balance N2(Nitrox). The high O2stress dive (HBO) and low O2stress dive (Nitrox) were separated by at least seven days and were conducted dry and at rest inside a hyperbaric chamber. EBC samples were taken immediately before and after each dive and subsequently underwent a targeted and untargeted metabolomics analysis using liquid chromatography coupled to mass spectrometry (LC-MS). Following the HBO dive, 10 out of 14 subjects reported symptoms of the early stages of PO2tox and one subject terminated the dive early due to severe symptoms of PO2tox. No symptoms of PO2tox were reported following the nitrox dive. A partial least-squares discriminant analysis of the normalized (relative to pre-dive) untargeted data gave good classification abilities between the HBO and nitrox EBC with an AUC of 0.99 (±2%) and sensitivity and specificity of 0.93 (±10%) and 0.94 (±10%), respectively. The resulting classifications identified specific biomarkers that included human metabolites and lipids and their derivatives from different metabolic pathways that may explain metabolomic changes resulting from prolonged HBO exposure.

Active sampling of volatile chemicals for non-invasive classification of chicken eggs by sex early in incubation

According to industry estimates, approximately 7 billion day-old male chicks are disposed of annually worldwide because they are not of use to the layer industry. A practical process to identify the sex of the egg early in incubation without penetrating the egg would improve animal welfare, reduce food waste and mitigate environmental impact. We implemented a moderate vacuum pressure system through commercial egg-handling suction cups to collect volatile organic compounds (VOCs). Three separate experiments were set up to determine optimal conditions to collect eggs VOCs to discriminate male from female embryos. Optimal extraction time (2 min), storage conditions (short period of incubation during egg storage (SPIDES) at days 8-10 of incubation), and sampling temperature (37.5°C) were determined. Our VOC-based method could correctly differentiate male from female embryos with more than 80% accuracy. These specifications are compatible with the design of specialized automation equipment capable of high-throughput, in-ovo sexing based on chemical sensor microchips.

Characterization of the microbiome and volatile compounds in anal gland secretions from domestic cats (Felis catus) using metagenomics and metabolomics

Animals rely on volatile chemical compounds for their communication and behavior. Many of these compounds are sequestered in endocrine and exocrine glands and are synthesized by anaerobic microbes. While the volatile organic compound (VOC) or microbiome composition of glandular secretions has been investigated in several mammalian species, few have linked specific bacterial taxa to the production of volatiles or to specific microbial gene pathways. Here, we use metagenomic sequencing, mass-spectrometry based metabolomics, and culturing to profile the microbial and volatile chemical constituents of anal gland secretions in twenty-three domestic cats (Felis catus), in attempts to identify organisms potentially involved in host odor production. We found that the anal gland microbiome was dominated by bacteria in the genera Corynebacterium, Bacteroides, Proteus, Lactobacillus, and Streptococcus, and showed striking variation among individual cats. Microbiome profiles also varied with host age and obesity. Metabolites such as fatty-acids, ketones, aldehydes and alcohols were detected in glandular secretions. Overall, microbiome and metabolome profiles were modestly correlated (r=0.17), indicating that a relationship exists between the bacteria in the gland and the metabolites produced in the gland. Functional analyses revealed the presence of genes predicted to code for enzymes involved in VOC metabolism such as dehydrogenases, reductases, and decarboxylases. From metagenomic data, we generated 85 high-quality metagenome assembled genomes (MAGs). Of these, four were inferred to have high relative abundance in metagenome profiles and had close relatives that were recovered as cultured isolates. These four MAGs were classified as Corynebacterium frankenforstense, Proteus mirabilis, Lactobacillus johnsonii, and Bacteroides fragilis. They represent strong candidates for further investigation of the mechanisms of volatile synthesis and scent production in the mammalian anal gland.

Controlled air exchange rate method to evaluate reduction of volatile organic compounds by indoor air cleaners

Air cleaning technologies are needed to reduce indoor concentrations and exposure to volatile organic compounds (VOCs). Currently, air cleaning technologies lack an accepted test standard to evaluate their VOC removal performance. A protocol to evaluate the VOC removal performance of air cleaning devices was developed and piloted with two devices. This method injects a VOC mixture and carbon dioxide into a test chamber, supplies outdoor air at a standard building ventilation rate, periodically measures the VOC concentrations in the chamber using solid phase microextraction-gas chromatography-mass spectrometry over a 3-h decay period, and compares the decay rate of VOCs to carbon dioxide to measure the VOC removal air cleaning performance. The method was demonstrated with both a hydroxyl radical generator and an activated carbon air cleaner. It was shown that the activated carbon air cleaner device tested had a clean air delivery rate an order of magnitude greater than the hydroxyl radical generator device (72.10 vs 6.32 m3/h).

Predominant SARS-CoV-2 variant impacts accuracy when screening for infection using exhaled breath vapor

BACKGROUND

New technologies with novel and ambitious approaches are being developed to diagnose or screen for SARS-CoV-2, including breath tests. The US FDA approved the first breath test for COVID-19 under emergency use authorization in April 2022. Most breath-based assays measure volatile metabolites exhaled by persons to identify a host response to infection. We hypothesized that the breathprint of COVID-19 fluctuated after Omicron became the primary variant of transmission over the Delta variant.

METHODS

We collected breath samples from 142 persons with and without a confirmed COVID-19 infection during the Delta and Omicron waves. Breath samples were analyzed by gas chromatography-mass spectrometry.

RESULTS

Here we show that based on 63 exhaled compounds, a general COVID-19 model had an accuracy of 0.73 ± 0.06, which improved to 0.82 ± 0.12 when modeling only the Delta wave, and 0.84 ± 0.06 for the Omicron wave. The specificity improved for the Delta and Omicron models (0.79 ± 0.21 and 0.74 ± 0.12, respectively) relative to the general model (0.61 ± 0.13).

CONCLUSIONS

We report that the volatile signature of COVID-19 in breath differs between the Delta-predominant and Omicron-predominant variant waves, and accuracies improve when samples from these waves are modeled separately rather than as one universal approach. Our findings have important implications for groups developing breath-based assays for COVID-19 and other respiratory pathogens, as the host response to infection may significantly differ depending on variants or subtypes.

Glass-to-Glass Fusion Bonding Quality and Strength Evaluation with Time, Applied Force, and Heat

A bonding process was developed for glass-to-glass fusion bonding using Borofloat 33 wafers, resulting in high bonding yield and high flexural strength. The Borofloat 33 wafers went through a two-step process with a pre-bond and high-temperature bond in a furnace. The pre-bond process included surface activation bonding using O₂ plasma and N₂ microwave (MW) radical activation, where the glass wafers were brought into contact in a vacuum environment in an EVG 501 Wafer Bonder. The optimal hold time in the EVG 501 Wafer bonder was investigated and concluded to be a 3 h hold time. The bonding parameters in the furnace were investigated for hold time, applied force, and high bonding temperature. It was concluded that the optimal parameters for glass-to-glass Borofloat 33 wafer bonding were at 550 °C with a hold time of 1 h with 550 N of applied force.

Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification

Differential mobility spectrometry (DMS)-based detectors are being widely studied to detect chemical warfare agents, explosives, chemicals, drugs and analyze volatile organic compounds (VOCs). The dispersion plots from DMS devices are complex to effectively analyze through visual inspection. In the current work, we adopted machine learning to differentiate pure chemicals and identify chemicals in a mixture. In particular, we observed the convolutional neural network algorithm exhibits excellent accuracy in differentiating chemicals in their pure forms while also identifying chemicals in a mixture. In addition, we propose and validate the magnitude-squared coherence (msc) between the DMS data of known chemical composition and that of an unknown sample can be sufficient to inspect the chemical composition of the unknown sample. We have shown that the msc-based chemical identification requires the least amount of experimental data as opposed to the machine learning approach.

A low cost, easy-to-assemble, open-source modular mobile sampler design for thermal desorption analysis of breath and environmental VOCs

Exhaled breath vapor contains hundreds of volatile organic compounds (VOCs), which are the byproducts of health and disease metabolism, and they have clinical and diagnostic potential. Simultaneous collection of breath VOCs and background environmental VOCs is important to ensure analyses eliminate exogenous compounds from clinical studies. We present a mobile sampling system to extract gaseous VOCs onto commercially available sorbent-packed thermal desorption tubes. The sampler can be connected to a number of commonly available disposable and reusable sampling bags, in the case of this study, a Tedlar bag containing a breath sample. Alternatively, the inlet can be left open to directly sample room or environmental air when obtaining a background VOC sample. The system contains a screen for the operator to input a desired sample volume. A needle valve allows the operator to control the sample flow rate, which operates with an accuracy of -1.52 ± 0.63% of the desired rate, and consistently generated that rate with 0.12 ± 0.06% error across repeated measures. A flow pump, flow sensor and microcontroller allow volumetric sampling, as opposed to timed sampling, with 0.06 ± 0.06% accuracy in the volume extracted. Four samplers were compared by sampling a standard chemical mixture, which resulted in 6.4 ± 4.7% error across all four replicate modular samplers to extract a given VOC. The samplers were deployed in a clinical setting to collect breath and background/environmental samples, including patients with active SARS-CoV-2 infections, and the device could easily move between rooms and can undergo required disinfection protocols to prevent transmission of pathogens on the case exterior. All components required for assembly are detailed and are made publicly available for non-commercial use, including the microcontroller software. We demonstrate the device collects volatile compounds, including use of chemical standards, and background and breath samples in real use conditions.

Inactivation of SARS-CoV-2 in clinical exhaled breath condensate samples for metabolomic analysis

Exhaled breath condensate (EBC) is routinely collected and analyzed in breath research. Because it contains aerosol droplets, EBC samples from SARS-CoV-2 infected individuals harbor the virus and pose the threat of infectious exposure. We report for the first time a safe and consistent method to fully inactivate SARS-CoV-2 in EBC samples and make EBC samples safe for processing and analysis. EBC samples containing infectious SARS-CoV-2 were treated with several concentrations of acetonitrile. The most commonly used 10% acetonitrile treatment for EBC processing failed to completely inactivate the virus in samples and viable virus was detected by the assay of SARS-CoV-2 infection of Vero E6 cells in a biosafety level 3 laboratory. Treatment with either 50% or 90% acetonitrile was effective to completely inactivate the virus, resulting in safe, non-infectious EBC samples that can be used for metabolomic analysis. Our study provides SARS-CoV-2 inactivation protocol for the collection and processing of EBC samples in the clinical setting and for advancing to metabolic assessments in health and disease.

Analysis of Volatile Profiles for Tracking Asymptomatic Infections of Phytophthora ramorum and Other Pathogens in Rhododendron

Phytophthora ramorum is an invasive, broad host range pathogen that causes ramorum blight and sudden oak death in forest landscapes of western North America. In commercial nurseries, asymptomatic infections of nursery stock by P. ramorum and other Phytophthora species create unacceptable risk and complicate inspection and certification programs designed to prevent introduction and spread of these pathogens. In this study, we continue development of a volatile organic compound (VOC)-based test for detecting asymptomatic infections of P. ramorum in Rhododendron sp. We confirmed detection of P. ramorum from volatiles collected from asymptomatic root-inoculated Rhododendron plants in a nursery setting, finding that the VOC profile of infected plants is detectably different from that of healthy plants, when measured from both ambient VOC emissions and VOCs extracted from leaf material. Predicting infection status was successful from ambient volatiles, which had a mean area under the curve (AUC) value of 0.71 ± 0.17, derived from corresponding receiver operating characteristic curves from an extreme gradient boosting discriminant analysis. This finding compares with that of extracted leaf volatiles, which resulted in a lower AUC value of 0.51 ± 0.21. In a growth chamber, we contrasted volatile profiles of asymptomatic Rhododendron plants having roots infected with one of three pathogens: P. ramorum, P. cactorum, and Rhizoctonia solani. Each pathogen induced unique and measurable changes, but generally the infections reduced volatile emissions until 17 weeks after inoculation, when emissions trended upward relative to those of mock-inoculated controls. Forty-five compounds had significant differences compared with mock-inoculated controls in at least one host-pathogen combination.

Exhaled breath biomarkers of influenza infection and influenza vaccination

Respiratory viral infections are considered a major public health threat, and breath metabolomics can provide new ways to detect and understand how specific viruses affect the human pulmonary system. In this pilot study, we characterized the metabolic composition of human breath for an early diagnosis and differentiation of influenza viral infection, as well as other types of upper respiratory viral infections. We first studied the non-specific effects of planned seasonal influenza vaccines on breath metabolites in healthy subjects after receiving the immunization. We then investigated changes in breath content from hospitalized patients with flu-like symptoms and confirmed upper respiratory viral infection. The exhaled breath was sampled using a custom-made breath condenser, and exhaled breath condensate (EBC) samples were analysed using liquid chromatography coupled to quadruplole-time-of-flight mass spectrometer (LC-qTOF). All metabolomic data was analysed using both targeted and untargeted approaches to detect specific known biomarkers from inflammatory and oxidative stress biomarkers, as well as new molecules associated with specific infections. We were able to find clear differences between breath samples collected before and after flu vaccine administration, together with potential biomarkers that are related to inflammatory processes and oxidative stress. Moreover, we were also able to discriminate samples from patients with flu-related symptoms that were diagnosed with confirmatory respiratory viral panels (RVP). RVP positive and negative differences were identified, as well as differences between specific viruses defined. These results provide very promising information for the further study of the effect of influenza A and other viruses in human systems by using a simple and non-invasive specimen like breath.

Environmental sampling of volatile organic compounds during the 2018 Camp Fire in Northern California

Trace analysis of volatile organic compounds (VOCs) during wildfires is imperative for environmental and health risk assessment. The use of gas sampling devices mounted on unmanned aerial vehicles (UAVs) to chemically sample air during wildfires is of great interest because these devices move freely about their environment, allowing for more representative air samples and the ability to sample areas dangerous or unreachable by humans. This work presents chemical data from air samples obtained in Davis, CA during the most destructive wildfire in California's history - the 2018 Camp Fire - as well as the deployment of our sampling device during a controlled experimental fire while fixed to a UAV. The sampling mechanism was an in-house manufactured micro-gas preconcentrator (µPC) embedded onto a compact battery-operated sampler that was returned to the laboratory for chemical analysis. Compounds commonly observed in wildfires were detected during the Camp Fire using gas chromatography mass spectrometry (GC-MS), including BTEX (benzene, toluene, ethylbenzene, m+p-xylene, and o-xylene), benzaldehyde, 1,4-dichlorobenzene, naphthalene, 1,2,3-trimethylbenzene and 1-ethyl-3-methylbenzene. Concentrations of BTEX were calculated and we observed that benzene and toluene were highest with average concentrations of 4.7 and 15.1 µg/m³, respectively. Numerous fire-related compounds including BTEX and aldehydes such as octanal and nonanal were detected upon experimental fire ignition, even at a much smaller sampling time compared to samples taken during the Camp Fire. Analysis of the air samples taken both stationary during the Camp Fire and mobile during an experimental fire show the successful operation of our sampler in a fire environment.

Predicting influenza and rhinovirus infections in airway cells utilizing volatile emissions

BACKGROUND

Respiratory viral infections are common and potentially devastating to patients with underlying lung disease. Diagnosing viral infections often requires invasive sampling, and interpretation often requires specialized laboratory equipment. Here, we test the hypothesis that a breath test could diagnose influenza and rhinovirus infections using an in vitro model of the human airway.

METHODS

Cultured primary human tracheobronchial epithelial cells were infected with either Influenza A H1N1 or Rhinovirus 1B and compared with healthy control cells. Headspace volatile metabolite measurements of cell cultures were made at 12 h timepoints post-infection using a thermal desorption-gas chromatography-mass spectrometry method.

RESULTS

Based on 54 compounds, statistical models distinguished VOC profiles of influenza- and rhinovirus-infected cells from healthy counterparts. Area under the curve values were 0.94 for influenza, 0.90 for rhinovirus, and 0.75 for controls. A regression analysis predicted how many hours prior cells became infected with a root mean square error of 6.35 h for influenza- and 3.32 h for rhinovirus-infected cells.

CONCLUSIONS

Volatile biomarkers released by bronchial epithelial cells could not only be used to diagnose whether cells were infected, but also the timing of infection. Our model supports the hypothesis that a breath test could serve to diagnose viral infections.

An environmental air sampler to evaluate personal exposure to volatile organic compounds

A micro fabricated chip-based wearable air sampler was used to monitor the personnel exposure of volatile chemical concentrations in microenvironments. Six teenagers participated in this study and 14 volatile organic compounds (VOCs) including naphthalene, 3-decen-1-ol, hexanal, nonanal, methyl salicylate and limonene gave the highest abundance during routine daily activity. VOC exposure associated with daily activities and the location showed strong agreements with two of the participant's results. One of these subjects had the highest exposure to methyl salicylate that was supported by the use of a topical analgesic balm containing this compound. Environmental based air quality monitoring followed by the personnel exposure studies provided additional evidence associated to the main locations where the participants traveled. Toluene concentrations observed at a gas station were exceptionally high, with the highest amount observed at 1213.1 ng m-3. One subject had the highest exposure to toluene and the GPS data showed clear evidence of activities neighboring a gas station. This study shows that this wearable air sampler has potential applications including hazardous VOC exposure monitoring in occupational hazard assessment for certain professions, for example in industries that involve direct handling of petroleum products.

Peak detection and random forests classification software for gas chromatography/differential mobility spectrometry (GC/DMS) data

Gas Chromatography/Differential Mobility Spectrometry (GC/DMS) is an effective tool to discern volatile chemicals. The process of correlating GC/DMS data outputs to chemical identities requires time and effort from trained chemists due to lack of commercially available software and the lack of appropriate libraries. This paper describes the coupling of computer vision techniques to develop models for peak detection and can align chemical signatures across datasets. The result is an automatically generated peak table that provides integrated peak areas for the inputted samples. The software was tested against a simulated dataset, whereby the number of detected features highly correlated to the number of actual features (r² = 0.95). This software has also been developed to include random forests, a discriminant analysis technique that generates prediction models for application to unknown samples with different chemical signatures. In an example dataset described herein, the model achieves 3% classification error with 12 trees and 0% classification error with 48 trees. The number of trees can be optimized based on the computational resources available. We expect the public release of this software can provide other GC/DMS researchers with a tool for automated featured extraction and discriminant analysis capabilities.

Breath carbonyl levels in a human population of seven hundred participants

Oxidative stress is associated with numerous health conditions and disorders, and aldehydes are known biomarkers of oxidative stress that can be non-invasively measured in exhaled human breath. Few studies report breath aldehyde levels in human populations, and none claim participant numbers in the hundreds or more. Further, the breath community must first define the existing aldehyde concentration variance in a normal population to understand when these levels are significantly perturbed by exogenous stressors or health conditions. In this study, we collected breath samples from 692 participants and quantified C₄-C₁₀ straight chain aldehyde levels. C₉ aldehyde was the most abundant in breath, followed by C₆. C₄ and C₅ appear to have bimodal distributions. Post hoc, we mined our dataset for other breath carbonyls captured by our assay, which involves elution of breath samples onto a solid phase extraction cartridge, derivatization and liquid chromatography-quadrupole time of flight mass spectrometry (LC-qTOF). We found a total of 21 additional derivatized compounds. Using self-reported demographic factors from our participants, we found no correlation between these breath carbonyls and age, gender, body mass index (BMI), ethnicity or smoking habit (tobacco and marijuana). This work was preceded by a small confounders study, which was intended to refine our breath collection procedure. We found that breath aldehyde levels can be affected by participants' using scented hygiene products such as lotions and mouthwashes, while collecting consecutive breath samples, rinsing the mouth with water, and filtering inspired air did not have an effect. Using these parameters to guide our sampling, subjects were instructed to avoid the prior conditions to provide a breath sample for our study.

Volatile organic compound (VOC) emissions of CHO and T cells correlate to their expansion in bioreactors

Volatile organic compound (VOC) emissions were measured from Chinese Hamster Ovary (CHO) cell and T cell bioreactor gas exhaust lines with the goal of non-invasively metabolically profiling the expansion process. Measurements of cellular 'breath' were made directly from the gas exhaust lines using polydimethylsiloxane (PDMS)-coated magnetic stir bars, which underwent subsequent thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) analysis. Baseline VOC profiles were observed from bioreactors filled with only liquid media. After inoculation, unique VOC profiles correlated to cell expansion over the course of 8 d. Partial least squares (PLS) regression models were built to predict cell culture density based on VOC profiles of CHO and T cells (R ² = 0.671 and R ² = 0.769, respectively, based on a validation data set). T cell runs resulted in 47 compounds relevant to expansion while CHO cell runs resulted in 45 compounds; the 20 most relevant compounds of each cell type were putatively identified. On the final experimental days, sorbent-covered stir bars were placed directly into cell-inoculated media and into media controls. Liquid-based measurements from spent media containing cells could be distinguished from media-only controls, indicating soluble VOCs excreted by the cells during expansion. A PLS-discriminate analysis (PLS-DA) was performed, and 96 compounds differed between T cell-inoculated media and media controls with 72 compounds for CHO cells; the 20 most relevant compounds of each cell line were putatively identified. This work demonstrates that the volatilome of cell cultures can be exploited by chemical detectors in bioreactor gas and liquid waste lines to non-invasively monitor cellular health and could possibly be used to optimize cell expansion conditions 'on-the-fly' with appropriate control loop systems. Although the basis for statistical models included compounds without certain identification, this work provides a foundation for future research of bioreactor emissions. Future studies must move towards identifying relevant compounds for understanding of underlying biochemistry.

Bacteria isolated from Bengal cat (Felis catus × Prionailurus bengalensis) anal sac secretions produce volatile compounds potentially associated with animal signaling

In social animals, scent secretions and marking behaviors play critical roles in communication, including intraspecific signals, such as identifying individuals and group membership, as well as interspecific signaling. Anal sacs are an important odor producing organ found across the carnivorans (species in the mammalian Order Carnivora). Secretions from the anal sac may be used as chemical signals by animals for behaviors ranging from defense to species recognition to signaling reproductive status. In addition, a recent study suggests that domestic cats utilize short-chain free fatty acids in anal sac secretions for individual recognition. The fermentation hypothesis is the idea that symbiotic microorganisms living in association with animals contribute to odor profiles used in chemical communication and that variation in these chemical signals reflects variation in the microbial community. Here we examine the fermentation hypothesis by characterizing volatile organic compounds (VOC) and bacteria isolated from anal sac secretions collected from a male Bengal cat (Felis catus × Prionailurus bengalensis), a cross between the domestic cat and the leopard cat. Both left and right anal sacs of a male Bengal cat were manually expressed (emptied) and collected. Half of the material was used to culture bacteria or to extract bacterial DNA and the other half was used for VOC analysis. DNA was extracted from the anal sac secretions and used for a 16S rRNA gene PCR amplification and sequencing based characterization of the microbial community. Additionally, some of the material was plated out in order to isolate bacterial colonies. Three taxa (Bacteroides fragilis, Tessaracoccus, and Finegoldia magna) were relatively abundant in the 16S rRNA gene sequence data and also isolated by culturing. Using Solid Phase Microextraction (SPME) gas chromatography-mass spectrometry (GC-MS), we tentatively identified 52 compounds from the Bengal cat anal sac secretions and 67 compounds from cultures of the three bacterial isolates chosen for further analysis. Among 67 compounds tentatively identified from bacterial isolates, 51 were also found in the anal sac secretion. We show that the bacterial community in the anal sac consists primarily of only a few abundant taxa and that isolates of these taxa produce numerous volatiles that are found in the combined anal sac volatile profile. Several of these volatiles are found in anal sac secretions from other carnivorans, and are also associated with known bacterial biosynthesis pathways. This is consistent with the fermentation hypothesis and the possibility that the anal sac is maintained at least in part to house bacteria that produce volatiles for the host.

Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures

Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. These devices separate chemical compounds in a mixture and provide information to identify specific chemical species of interest. Further, these types of devices operate well in both controlled lab environments and in-field applications. Frequently, the commercial versions of these devices are highly tailored for niche applications (e.g., explosives detection) because of the difficulty involved in reconfiguring instrumentation hardware and data analysis software algorithms. In order for researchers to quickly adapt these tools for new purposes and broader panels of chemical targets, it is critical to develop new algorithms and methods for generating libraries of these sensor responses. Microelectromechanical system (MEMS) technology has been used to fabricate DMS devices that miniaturize the platforms for easier deployment; however, concurrent advances in advanced data analytics are lagging. DMS generates complex three-dimensional dispersion plots for both positive and negative ions in a mixture. Although simple spectra of single chemicals are straightforward to interpret (both visually and via algorithms), it is exceedingly challenging to interpret dispersion plots from complex mixtures with many chemical constituents. This study uses image processing and computer vision steps to automatically identify features from DMS dispersion plots. We used the bag-of-words approach adapted from natural language processing and information retrieval to cluster and organize these features. Finally, a support vector machine (SVM) learning algorithm was trained using these features in order to detect and classify specific compounds in these represented conceptualized data outputs. Using this approach, we successfully maintain a high level of correct chemical identification, even when a gas mixture increases in complexity with interfering chemicals present.

Modeling cellular metabolomic effects of oxidative stress impacts from hydrogen peroxide and cigarette smoke on human lung epithelial cells

The respiratory system is continuously exposed to variety of biological and chemical irritants that contain reactive oxygen species, and these are well known to cause oxidative stress responses in lung epithelial cells. There is a clinical need to identify biomarkers of oxidative stress which could potentially support early indicators of disease and health management. To identify volatile biomarkers of oxidative stress, we analyzed the headspace above human bronchial epithelial cell cultures (HBE1) before and after hydrogen peroxide (H₂O₂) and cigarette smoke extract (CSE) exposure. Using stir bar and headspace sorptive extraction-gas chromatography-mass spectrometry, we searched for volatile organic compounds (VOC) of these oxidative measures. In the H₂O₂ cell peroxidation experiments, four different H₂O₂ concentrations (0.1, 0.5, 10, 50 mM) were applied to the HBE1 cells, and VOCs were collected every 12 h over the time course of 48 h. In the CSE cell peroxidation experiments, four different smoke extract concentrations (0%, 10%, 30%, 60%) were applied to the cells, and VOCs were collected every 12 h over the time course of 48 h. We used partial-least squares (PLS) analysis to identify putative compounds from the mass spectrometry results that highly correlated with the known applied oxidative stress. We observed chemical emissions from the cells that related to both the intensity of the oxidative stress and followed distinct time courses. Additionally, some of these chemicals are aldehydes, which are thought to be non-invasive indicators of oxidative stress in exhaled human breath. Together, these results illustrate a powerful in situ cell culture model of oxidative stress that can be used to explore the putative biological genesis of exhaled breath biomarkers that are often observed in human clinical studies.

Wearable Environmental Monitor To Quantify Personal Ambient Volatile Organic Compound Exposures

Air pollution can cause acute and chronic health problems. It has many components, and one component of interest is volatile organic compounds (VOCs). While the outdoor environment may have regulations regarding exposure limits, the indoor environment is often unregulated and VOCs often appear in greater concentrations in the indoor environment. Therefore, it is equally critical to monitor both the indoor and outdoor environments for ambient chemical levels that an individual person is exposed to. While a number of different chemical detectors exist, most lack the ability to provide portable monitoring. We have developed a portable and wearable sampler that collects environmental VOCs in a person's immediate "exposure envelope" onto custom micro-preconcentrator chips for later benchtop analysis. The system also records ambient temperature and humidity and the GPS location during sampling, and the chip cartridges can be used in sequence over time to complete a profile of individual chemical exposure over the course of hours/days/weeks/months. The system can be programmed to accumulate sample for various times with varying periodicity. We first tested our sampler in the laboratory by completing calibration curves and testing saturation times for various common chemicals. The sampler was also tested in the field by collecting both indoor and outdoor personal exposure samples. Additionally under IRB approval, a teenaged volunteer wore the sampler for 5 days during which it sampled periodically throughout a 12 h period each day and the volunteer replaced the micro-preconcentrator chip each day.

SPME-based mobile field device for active sampling of volatiles

Monitoring plant volatile organic compound (VOC) profiles can reveal information regarding the health state of the plant, such as whether it is nutrient stressed or diseased. Typically, plant VOC sampling uses sampling enclosures. Enclosures require time and equipment which are not easily adapted to high throughput sampling in field environments. We have developed a new, easily assembled active sampling device using solid phase microextraction (SPME) that uses a commercial off the shelf (COTS) hand vacuum base to provide rapid and easy mobile plant VOC collection. Calibration curves for three representative plant VOCs (α-pinene, limonene, and ocimene) were developed to verify device functionality and enable the quantification of field-samples from a Meyer lemon tree. We saw that the active sampling allowed us to measure and quantify this chemical in an orchard setting. This device has the potential to be used for VOC sampling as a preliminary diagnostic in precision agriculture applications due to its ease of manufacturing, availability, and low cost of the COTS hand vacuum module.

Portable combination of Fourier transform infrared spectroscopy and differential mobility spectrometry for advanced vapor phase analysis

Designing mobile devices for the analysis of complex sample mixtures containing a variety of analytes at different concentrations across a large dynamic range remains a challenging task in many analytical scenarios. To meet this challenge, a compact hybrid analytical platform has been developed combining Fourier transform infrared spectroscopy based on substrate-integrated hollow waveguides (iHWG-FTIR) with gas chromatography coupled differential mobility spectrometry (GC-DMS). Due to the complementarity of these techniques regarding analyte type and concentration, their combination provides a promising tool for the detection of complex samples containing a broad range of molecules at different concentrations. To date, the combination of infrared spectroscopy and ion mobility techniques remains expensive and bound to a laboratory utilizing e.g. IMS as prefilter or IR as ionization source. In the present study, a cost-efficient and portable solution has been developed and characterized representing the first truly hyphenated IR-DMS system. As a model analyte mixture, 5 ppm isopropylmercaptan (IPM) in methane (CH4) were diluted, and the concentration-dependent DMS signal of IPM along with the concentration-dependent IR signal of CH4 were recorded for all three hybrid IR-DMS systems. While guiding the sample through the iHWG-FTIR or the GC-DMS first did not affect the obtained signals, optimizing the IR data acquisition parameters did benefit the analytical results.

Nectar-inhabiting microorganisms influence nectar volatile composition and attractiveness to a generalist pollinator

The plant microbiome can influence plant phenotype in diverse ways, yet microbial contribution to plant volatile phenotype remains poorly understood. We examine the presence of fungi and bacteria in the nectar of a coflowering plant community, characterize the volatiles produced by common nectar microbes and examine their influence on pollinator preference. Nectar was sampled for the presence of nectar-inhabiting microbes. We characterized the headspace of four common fungi and bacteria in a nectar analog. We examined electrophysiological and behavioral responses of honey bees to microbial volatiles. Floral headspace samples collected in the field were surveyed for the presence of microbial volatiles. Microbes commonly inhabit floral nectar and the common species differ in volatile profiles. Honey bees detected most microbial volatiles tested and distinguished among solutions based on volatiles only. Floral headspace samples contained microbial-associated volatiles, with 2-ethyl-1-hexanol and 2-nonanone - both detected by bees - more often detected when fungi were abundant. Nectar-inhabiting microorganisms produce volatile compounds, which can differentially affect honey bee preference. The yeast Metschnikowia reukaufii produced distinctive compounds and was the most attractive of all microbes compared. The variable presence of microbes may provide volatile cues that influence plant-pollinator interactions.

Modular and reconfigurable gas chromatography / differential mobility spectrometry (GC/DMS) package for detection of volatile organic compounds (VOCs)

Due to the versatility of present day microcontroller boards and open source development environments, new analytical chemistry devices can now be built outside of large industry and instead within smaller individual groups. While there are a wide range of commercial devices available for detecting and identifying volatile organic compounds (VOCs), most of these devices use their own proprietary software and complex custom electronics, making modifications or reconfiguration of the systems challenging. The development of microprocessors for general use, such as the Arduino prototyping platform, now enables custom chemical analysis instrumentation. We have created an example system using commercially available parts, centered around on differential mobility spectrometer (DMS) device. The Modular Reconfigurable Gas Chromatography - Differential Mobility Spectrometry package (MR-GC-DMS) has swappable components allowing it to be quickly reconfigured for specific application purposes as well as broad, generic use. The MR-GC-DMS has a custom user-friendly graphical user interface (GUI) and precisely tuned proportional-integral-derivative controller (PID) feedback control system managing individual temperature-sensitive components. Accurate temperature control programmed into the microcontroller greatly increases repeatability and system performance. Together, this open-source platform enables researchers to quickly combine DMS devices in customized configurations for new chemical sensing applications.

Power-efficient self-cleaning hydrophilic condenser surface for portable exhaled breath condensate (EBC) metabolomic sampling

In this work, we present a hydrophilic self-cleaning condenser surface for the collection of biological and environmental aerosol samples. The condenser is installed in a battery-operated hand-held breath sampling device. The device performance is characterized by the collection and analysis of exhaled breath samples from a group of volunteers. The exhaled breath condensate is collected on a subcooled condenser surface, transferred into a storage vial, and its chemical content is analyzed using mass spectrometric methods. The engineered surface supports upon it a continuous condensation cycle, and this allows the collection of liquid samples exceeding the saturation mass/area limit of a plain hydrophilic surface. The condenser surface employs two constituent parameters: a low surface energy barrier to enhance nucleation and condensation efficiency, and a network of surface microstructures to create a self-cleaning mechanism for fluid aggregation into a reservoir. Removal of the liquid condensate from the condenser surface prevents the formation of a thick liquid layer, and thus maintains a continuous condensation cycle with a minimum decrease in heat transfer efficiency as condensation occurs on the surface. The self-cleaning condenser surfaces may have a number of applications in the collection of biological, chemical, or environmental aerosol samples. Sample phase conversion to liquid can facilitate sample manipulation and chemical analysis of matrices with low concentrations. Here, we demonstrate the use of a self-cleaning microcondenser for the collection of exhaled breath condensate with a hand-held portable device. All breath collections with the two devices were performed with the same group of volunteers under UC Davis IRB protocol 63701-3.

Headspace sorptive extraction-gas chromatography-mass spectrometry method to measure volatile emissions from human airway cell cultures

The human respiratory tract releases volatile metabolites into exhaled breath that can be utilized for noninvasive health diagnostics. To understand the origin of this metabolic process, our group has previously analyzed the headspace above human epithelial cell cultures using solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). In the present work, we improve our model by employing sorbent-covered magnetic stir bars for headspace sorptive extraction (HSSE). Sorbent-coated stir bar analyte recovery increased by 52 times and captured 97 more compounds than SPME. Our data show that HSSE is preferred over liquid extraction via stir bar sorptive extraction (SBSE), which failed to distinguish volatiles unique to the cell samples compared against media controls. Two different cellular media were also compared, and we found that Opti-MEM® is preferred for volatile analysis. We optimized HSSE analytical parameters such as extraction time (24 h), desorption temperature (300 °C) and desorption time (7 min). Finally, we developed an internal standard for cell culture VOC studies by introducing 842 ng of deuterated decane per 5 mL of cell medium to account for error from extraction, desorption, chromatography and detection. This improved model will serve as a platform for future metabolic cell culture studies to examine changes in epithelial VOCs caused by perturbations such as viral or bacterial infections, opening opportunities for improved, noninvasive pulmonary diagnostics.

An Easy to Manufacture Micro Gas Preconcentrator for Chemical Sensing Applications

We have developed a simple-to-manufacture microfabricated gas preconcentrator for MEMS-based chemical sensing applications. Cavities and microfluidic channels were created using a wet etch process with hydrofluoric acid, portions of which can be performed outside of a cleanroom, instead of the more common deep reactive ion etch process. The integrated heater and resistance temperature detectors (RTDs) were created with a photolithography-free technique enabled by laser etching. With only 28 V DC (0.1 A), a maximum heating rate of 17.6 °C/s was observed. Adsorption and desorption flow parameters were optimized to be 90 SCCM and 25 SCCM, respectively, for a multicomponent gas mixture. Under testing conditions using Tenax TA sorbent, the device was capable of measuring analytes down to 22 ppb with only a 2 min sample loading time using a gas chromatograph with a flame ionization detector. Two separate devices were compared by measuring the same chemical mixture; both devices yielded similar peak areas and widths (fwhm: 0.032-0.033 min), suggesting reproducibility between devices.

Metabolite content profiling of bottlenose dolphin exhaled breath

Changing ocean health and the potential impact on marine mammal health are gaining global attention. Direct health assessments of wild marine mammals, however, is inherently difficult. Breath analysis metabolomics is a very attractive assessment tool due to its noninvasive nature, but it is analytically challenging. It has never been attempted in cetaceans for comprehensive metabolite profiling. We have developed a method to reproducibly sample breath from small cetaceans, specifically Atlantic bottlenose dolphins (Tursiops truncatus). We describe the analysis workflow to profile exhaled breath metabolites and provide here a first library of volatile and nonvolatile compounds in cetacean exhaled breath. The described analytical methodology enabled us to document baseline compounds in exhaled breath of healthy animals and to study changes in metabolic content of dolphin breath with regard to a variety of factors. The method of breath analysis may provide a very valuable tool in future wildlife conservation efforts as well as deepen our understanding of marine mammals biology and physiology.

Volatile emanations from in vitro airway cells infected with human rhinovirus

Respiratory viral infections such as human rhinovirus (HRV) can lead to substantial morbidity and mortality, especially in people with underlying lung diseases such as asthma and COPD. One proposed strategy to detect viral infections non-invasively is by volatile organic compound (VOC) assessment via analysis of exhaled breath. The epithelial cells are one of the most important cell lines affected during respiratory infections as they are the first line of pathogen defense. Efforts to discover infection-specific biomarkers can be significantly aided by understanding the VOC emanations of respiratory epithelial cells. Here we test the hypothesis that VOCs obtained from the headspace of respiratory cell culture will differentiate healthy cells from those infected with HRV. Primary human tracheobronchial cells were cultured and placed in a system designed to trap headspace VOCs. HRV-infected cells were compared to uninfected control cells. In addition, cells treated with heat-killed HRV and poly(I:C), a TLR3 agonist, were compared to controls. The headspace was sampled with solid-phase microextraction fibers and VOCs were analyzed by gas chromatography/mass spectrometry. We determined differential expression of compounds such as aliphatic alcohols, branched hydrocarbons, and dimethyl sulfide by the infected cells, VOCs previously associated with oxidative stress and bacterial infection. We saw no major differences between the killed-HRV, poly(I:C), and control cell VOCs. We postulate that these compounds may serve as biomarkers of HRV infection, and that the production of VOCs is not due to TLR3 stimulation but does require active viral replication. Our novel approach may be used for the in vitro study of other important respiratory viruses, and ultimately it may aid in identifying VOC biomarkers of viral infection for point-of-care diagnostics.

Metformin and contrast media--a dangerous combination?

Metformin is a biguanide used to treat type II diabetes mellitus. Since the recent introduction of this drug into the United States there has been considerable interest in metformin associated lactic acidosis (MALA) following intravenous contrast media. The Royal College of Radiologists published advice in November, 1996 (Advice to Members and Fellows with regard to metformin-induced lactic acidosis and X-ray contrast medium agents, RCR Publication) supporting the manufacturers' advice that metformin should not be used in the 48 h before or after intravenous (i.v.) contrast medium. We performed a systematic review of the literature and this has shown that almost all reported cases of MALA following i.v. contrast medium occurred where there was either pre-existing poor renal function or another contraindication to metformin usage. There has been only one reported case of lactic acidosis following the use of intravenous contrast medium in a patient with normal renal function. We suggest that the Royal College of Radiologists' advice should be modified and that it is safe to give i.v. contrast medium to patients on metformin with normal renal function.