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

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 f_carb, f_fat and f_prot , total energy expenditure TEE) in a resolution never available before for a minimal invasive protocol of mice under anesthesia.

iHWG-MOX: A Hybrid Breath Analysis System via the Combination of Substrate-Integrated Hollow Waveguide Infrared Spectroscopy with Metal Oxide Gas Sensors

According to their materials and operating parameters, metal oxide (MOX) sensors respond to target gases only by a change in sensor resistance with a lack in selectivity. By the use of infrared spectroscopy, highly discriminatory information from samples at a molecular level can be obtained and the selectivity can be enhanced. A low-volume gas cell was developed for a commercially available semiconducting MOX methane gas sensor and coupled directly to a mid-infrared gas sensor based on substrate-integrated hollow waveguide (iHWG) technology combined with a Fourier transform infrared spectrometer. This study demonstrates a sensing process with combined orthogonal sensors for fast, time-resolved, and synergic detection of methane and carbon dioxide in gas samples.

Hybrid Analytical Platform Based on Field-Asymmetric Ion Mobility Spectrometry, Infrared Sensing, and Luminescence-Based Oxygen Sensing for Exhaled Breath Analysis

The reliable online analysis of volatile compounds in exhaled breath remains a challenge, as a plethora of molecules occur in different concentration ranges (i.e., ppt to %) and need to be detected against an extremely complex background matrix. Although this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three different detector types based on truly orthogonal measurement principles as an alternative solution: Field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). By carefully aligning the experimental needs and measurement protocols of all three methods, they were successfully integrated into a single compact analytical platform suitable for online measurements. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N₂), oxygen (O₂), carbon dioxide (CO₂), and acetone as a model volatile organic compound (VOC) commonly present in breath. All three target analytes could be detected within their respectively breath-relevant concentration range, i.e., CO₂ and O₂ at 3-5 % and at ~19.6 %, respectively, while acetone could be detected with LOQs as low as 165-405 ppt. Orthogonality of the three methods operating in concert was clearly proven, which is essential to cover a possibly wide range of detectable analytes. Finally, the remaining challenges toward the implementation of the developed hybrid FAIMS-FTIR-LS system for exhaled breath analysis for metabolic studies in small animal intensive care units are discussed.