Breath analysis with broadly tunable quantum cascade lasers.

With the availability of broadly tunable external cavity quantum cascade lasers (EC-QCLs), particularly bright mid-infrared (MIR; 3-20 µm) light sources are available offering high spectral brightness along with an analytically relevant spectral tuning range of >2 µm. Accurate isotope ratio determination of (12)CO2 and (13)CO2 in exhaled breath is of critical importance in the field of breath analysis, which may be addressed via measurements in the MIR spectral regime. Here, we combine for the first time an EC-QCL tunable across the (12)CO2/(13)CO2 spectral band with a miniaturized hollow waveguide gas cell for quantitatively determining the (12)CO2/(13)CO2 ratio within the exhaled breath of mice. Due to partially overlapping spectral features, these studies are augmented by appropriate multivariate data evaluation and calibration techniques based on partial least-squares regression along with optimized data preprocessing. Highly accurate determinations of the isotope ratio within breath samples collected from a mouse intensive care unit validated via hyphenated gas chromatography-mass spectrometry confirm the viability of IR-HWG-EC-QCL sensing techniques for isotope-selective exhaled breath analysis.

Multivariate determination of 13CO2/12CO2 ratios in exhaled mouse breath with mid-infrared hollow waveguide gas sensors.

The (12)CO2/(13)CO2 isotope ratio is a well-known marker in breath for a variety of biochemical processes and enables monitoring, e.g., of the glucose metabolism during sepsis. Using animal models-here, at a mouse intensive care unit-the simultaneous determination of (12)CO2 and (13)CO2 within small volumes of mouse breath was enabled by coupling a novel low-volume hollow waveguide gas cell to a compact Fourier transform infrared spectrometer combined with multivariate data evaluation based on partial least squares regression along with optimized data preprocessing routines.

Toward the quantification of the 13CO2/12CO2 ratio in exhaled mouse breath with mid-infrared hollow waveguide gas sensors.

Mouse sepsis models are used to gain insight into the complex processes involved with patients suffering from glucose metabolism disorders. Measuring the expiratory release of (13)CO(2) after administering stable labeled (13)C(6)-glucose enables assessment of the in vivo integrity and functionality of key metabolic processes. In the present study, we demonstrate that Fourier transform infrared spectroscopy operating in the mid-infrared spectral regime (2-20 µm) combined with hollow waveguide gas sensing modules simultaneously serving as a miniaturized gas cell and as a waveguide are capable of quantitatively monitoring (13)CO(2) enrichment levels in low volume mouse breath samples.

Metabolic monitoring via on-line analysis of 13C-enriched carbon dioxide in exhaled mouse breath using substrate-integrated hollow waveguide infrared spectroscopy and luminescence sensing combined with Bayesian sampling.

In studies that target specific functions or organs, the response is often overlaid by indirect effects of the intervention on global metabolism. The metabolic side of these interactions can be assessed based on total energy expenditure (TEE) and the contributions of the principal energy sources, carbohydrates, proteins and fat to whole body CO2 production. These parameters can be identified from indirect calorimetry using respiratory oxygen intake and CO2 dioxide production data that are combined with the response of the 13CO2 release in the expired air and the glucose tracer enrichment in plasma following a 13C glucose stable isotope infusion. This concept is applied to a mouse protocol involving anesthesia, mechanical respiration, a disease model, like hemorrhage and therapeutic intervention. It faces challenges caused by a small sample size for both breath and plasma as well as changes in metabolic parameters caused by disease and intervention. Key parameters are derived from multiple measurements, all afflicted with errors that may accumulate leading to unrealistic values. To cope with these challenges, a sensitive on-line breath analysis system based on substrate-integrated hollow waveguide infrared spectroscopy and luminescence (iHWG-IR-LS) was used to monitor gas exchange values. A Bayesian statistical model is developed that uses established equations for indirect calorimetry to predict values for respiratory gas exchange and tracer data that are consistent with the corresponding measurements and also provides statistical error bands for these parameters. With this new methodology, it was possible to estimate important metabolic parameters (respiratory quotient (RQ), relative contribution of carbohydrate, protein and fat oxidation fcarb, ffat and fprot , total energy expenditure TEE) in a resolution never available before for a minimal invasive protocol of mice under anesthesia.

Strategies for 13C enrichment calculation in Fourier-transform infrared CO2 spectra containing spectral overlapping and nonlinear abundance-amount relations utilizing response surface fits.

The metabolism can be explored via 13C labeling of biological active substances and subsequent quantification of 13C enrichment in the exhaled carbon dioxide in breath. The resulting tracer enrichment values can be determined by Fourier-transform Infrared Spectroscopy (FTIR), since different CO2 isotopologues result in distinct absorption lines in the spectrum.The corresponding determination poses two challenges: first, FTIR absorbance can contain a nonlinear relationship between analyte amount and spectral signal and second, the spectral peaks for the different isotopologues overlap. The overlap precludes a separate calibration to asses the isotopologue concentration values and with it a determination of enrichments from concentration values. We propose here, first, a data reduction step like Principal Component Analysis (PCA) to convert the spectral information into one score pertaining to the 13CO2 enrichment. In a second step, a calibration function between score and enrichment values was established. The enrichment score can be derived by normalizing a subset of the spectrum by some measure for the 12CO2 sample content. Alternatively, the overlapping spectra were decomposed into two isotopologue spectra and the intensity of the separated spectra was used to form an enrichment score. For spectral separation, either Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) was used or a novel decomposition strategy developed for this paper called Rotation and Angle-Bending Bayesian induced Transformation - Multivariate Curve Resolution (RABBIT - MCR) that operates in a Principal Component Analysis (PCA) subspace and is derived from MCR. We compared 13C enrichment estimates from FTIR CO2 spectra using different normalization variants with the two spectral separation models. In conclusion, the two spectral separation variants performed nearly equal, but better than any normalization variant.

Online monitoring of carbon dioxide and oxygen in exhaled mouse breath via substrate-integrated hollow waveguide Fourier-transform infrared-luminescence spectroscopy.

Exhaled breath offers monitoring bio markers, as well as diagnosing diseases and therapeutic interventions. In addition, vital functions may be non-invasively monitored online. Animal models are frequently used in research for determining novel therapeutic approaches and/or for investigating biological pathways. The exhaled carbon dioxide concentration, exhaled and inhaled oxygen concentration, and the subsequent respiratory quotient (RQ) offer insight into metabolic activity. One may adapt breath sampling systems and equipment designed for human applications to large animal studies. However, such adaptations are usually impossible for small animals due to their minuscule breath volume. Here, we present a system for the online monitoring of exhaled breath in a 'mouse intensive care unit' (MICU) based on a modified Fourier-transform infrared spectrometer equipped with a substrate-integrated hollow waveguide gas cell, and a luminescence-based oxygen flow-through sensor integrated into the respiratory equipment of the MICU. Thereby, per-minute resolution of O2 consumption and CO2 production was obtained, and the 95% confidence range of the determined RQ was ±0.04 or approximately ±5% of the nominal value. Changes in the RQ value caused by intervention in either the metabolic or respiratory system may therefore reliably be detected.

Response-surface fits and calibration transfer for the correction of the oxygen effect in the quantification of carbon dioxide via FTIR spectroscopy.

During routine Fourier-Transform Infrared Spectroscopy (FTIR) based quantification of carbon dioxide in breath, it is necessary to account for a non-linear signal response to the analyte concentration and disturbance factors arising from the gas background matrix. These factors as well as day-to-day fluctuation should be corrected via calibration. We present a novel strategy to combine the information of previous calibrations with a minimal number of actual calibration measurements to obtain a precise calibration. After decomposition of the FTIR spectra via principal component analysis (PCA) into scores (corresponding to intensity) and loadings (corresponding to spectral curves), an empirical response surface fit equation between scores, analyte concentration and disturbance factors is established. The fit equation can be characterized via the coefficients determined by calibration. Out of a pool of coefficients gained from several calibrations, a multivariate inter-day distribution is generated. By requiring the coefficient set of the actual calibration to be a sample of the multivariate inter-day distribution, the number of necessary routine calibration samples is reduced to two. The corresponding coefficients are determined using the Lagrange Multipliers approach and the inter-day variability of coefficients is estimated using Bayesian statistics and Hierarchical models. The best calibration parameters in terms of calibration equation, wavelength region, preprocessing options and choice of routine calibration samples were determined; optimized for minimal number of calibration samples.

Nonlinear calibration transfer based on hierarchical Bayesian models and Lagrange Multipliers: Error bounds of estimates via Monte Carlo - Markov Chain sampling.

The calibration of analytical systems is time-consuming and the effort for daily calibration routines should therefore be minimized, while maintaining the analytical accuracy and precision. The 'calibration transfer' approach proposes to combine calibration data already recorded with actual calibrations measurements. However, this strategy was developed for the multivariate, linear analysis of spectroscopic data, and thus, cannot be applied to sensors with a single response channel and/or a non-linear relationship between signal and desired analytical concentration. To fill this gap for a non-linear calibration equation, we assume that the coefficients for the equation, collected over several calibration runs, are normally distributed. Considering that coefficients of an actual calibration are a sample of this distribution, only a few standards are needed for a complete calibration data set. The resulting calibration transfer approach is demonstrated for a fluorescence oxygen sensor and implemented as a hierarchical Bayesian model, combined with a Lagrange Multipliers technique and Monte-Carlo Markov-Chain sampling. The latter provides realistic estimates for coefficients and prediction together with accurate error bounds by simulating known measurement errors and system fluctuations. Performance criteria for validation and optimal selection of a reduced set of calibration samples were developed and lead to a setup which maintains the analytical performance of a full calibration. Strategies for a rapid determination of problems occurring in a daily calibration routine, are proposed, thereby opening the possibility of correcting the problem just in time.

Fiber-Coupled Substrate-Integrated Hollow Waveguides: An Innovative Approach to Mid-infrared Remote Gas Sensors.

In this study, an innovative approach based on fiberoptically coupled substrate-integrated hollow waveguide (iHWG) gas cells for the analysis of low sample volumes suitable for remote broad- and narrow-band mid-infrared (MIR; 2.5-20 µm) sensing applications is reported. The feasibility of remotely addressing iHWG gas cells, configured in a double-pass geometry via a reflector, by direct coupling to a 7-around-1 mid-infrared fiber bundle is demonstrated, facilitating low-level hydrocarbon gas analysis. For comparison studies, two iHWGs with substrate dimensions of 50 × 50 × 12 mm (L × W × H) and geometric channel lengths of 138 and 58.5 mm, serving as miniature light-guiding gas cells, were fiber-coupled to a Fourier transform infrared spectrometer enabling broadband MIR sensing. In addition to the fundamental feasibility of this concept, the achievable sensitivity toward several gaseous hydrocarbons and the reproducibility of assembling the fiber-iHWG interface were investigated.

A mobile instrumentation platform to distinguish airway disorders.

Asthma and chronic obstructive pulmonary disease (COPD) are distinct but clinically overlapping airway disorders which often create diagnostic and therapeutic dilemmas. Current strategies to discriminate these diseases are limited by insensitivity and poor performance due to biologic variability. We tested the hypothesis that a gas chromatograph/differential mobility spectrometer (GC/DMS) sensor could distinguish between clinically well-defined groups with airway disorders based on the volatile organic compounds (VOCs) obtained from exhaled breath. After comparing VOC profiles obtained from 13 asthma, 5 COPD and 13 healthy control subjects, we found that VOC profiles distinguished asthma from healthy controls and also a subgroup of asthmatics taking the drug omalizumab from healthy controls. The VOC profiles could not distinguish between COPD and any of the other groups. Our results show a potential application of the GC/DMS for non-invasive and bedside diagnostics of asthma and asthma therapy monitoring. Future studies will focus on larger sample sizes and patient cohorts.