Online monitoring of air quality at the postanesthetic care unit by proton-transfer-reaction mass spectrometry

Detecting Hexafluoroisopropanol Using Soft Chemical Ionization Mass Spectrometry and Analytical Applications to Exhaled Breath

Here we explore the potential use of proton transfer reaction/selective reagent ion-time-of-flight-mass spectrometry (PTR/SRI-ToF-MS) to monitor hexafluoroisopropanol (HFIP) in breath. Investigations of the reagent ions H₃O⁺, NO⁺, and O₂^+• are reported using dry (relative humidity (rH) ≈ 0%) and humid (rH ≈ 100%)) nitrogen gas containing traces of HFIP, i.e., divorced from the complex chemical environment of exhaled breath. HFIP shows no observable reaction with H₃O⁺ and NO⁺, but it does react efficiently with O₂^+• via dissociative charge transfer resulting in CHF₂⁺, CF₃⁺, C₂HF₂O⁺, and C₂H₂F₃O⁺. A minor competing hydride abstraction channel results in C₃HF₆O⁺ + HO₂^• and, following an elimination of HF, C₃F₅O⁺. There are two issues associated with the use of the three dominant product ions of HFIP, CHF₂⁺, CF₃⁺, and C₂H₂F₃O⁺, to monitor it in breath. One is that CHF₂⁺ and CF₃⁺ also result from the reaction of O₂^+• with the more abundant sevoflurane. The second is the facile reaction of these product ions with water, which reduces analytical sensitivity to detect HFIP in humid breath. To overcome the first issue, C₂H₂F₃O⁺ is the ion marker for HFIP. The second issue is surmounted by using a Nafion tube to reduce the breath sample's humidity prior to its introduction into drift tube. The success of this approach is illustrated by comparing the product ion signals either in dry or humid nitrogen gas flows and with or without the use of the Nafion tube, and practically from the analysis of a postoperative exhaled breath sample from a patient volunteer.

Elimination characteristics of post-operative isoflurane levels in alveolar exhaled breath via PTR-MS analysis

Isoflurane (1-chloro-2,2,2-trifluoroethyl difluoromethyl ether), C₃H₂ClF₅O, is a commonly used inhalation anaesthetic. Using a proton transfer reaction mass spectrometer (PTR-MS) we have detected isoflurane in the breath of patients several weeks following major surgery. That isoflurane is detected in the breath of patients so long after being anaesthetised raises questions about when cognitive function has fully returned to a patient. Temporal profiles of isoflurane concentrations in breath are presented for five patients (F/M 3/2, mean age 50 years, min-max 36-58 years) who had undergone liver transplant surgery. In addition, results from a headspace analysis of isoflurane are presented so that the product ions resulting from the reactions of H₃O⁺ with isoflurane in PTR-MS could be easily identified in the absence of the complex chemical environment of breath. Six product ions were identified. In order of increasing m/z (using the ³⁵Cl isotope where appropriate) these are [Formula: see text] (m/z 51), CHFCl⁺ (m/z 67), CF₃CHCl⁺ (m/z 117), C₃F₄OCl⁺ (m/z 163), C₃H₂F₄OCl⁺ (m/z 165), and C₃F₄OCl⁺ H₂O (m/z 183). No protonated parent was detected. For the headspace study both clean air and CO₂ enriched clean air (4% CO₂) were used as buffer gases in the drift tube of the PTR-MS. The CO₂ enriched air was used to determine if exhaled breath would affect the product ion branching ratios. Importantly no significant differences were observed, and therefore for isoflurane the product ion distributions determined in a normal air mixture can be used for breath analysis. Given that PTR-MS can be operated under different reduced electric fields (E/N), the dependence of the product ion branching percentages for isoflurane on E/N (96-138 Td) are reported.

Multi-capillary column-ion mobility spectrometry (MCC-IMS) as a new method for the quantification of occupational exposure to sevoflurane in anaesthesia workplaces: an observational feasibility study

Background

Occupational exposure to sevoflurane has the potential to cause health damage in hospital personnel. Workplace contamination with the substance mostly is assessed by using photoacoustic infrared spectrometry with detection limits of 10 ppbv. Multi-capillary column-ion mobility spectrometry (MCC-IMS) could be an alternative technology for the quantification of sevoflurane in the room air and could be even more accurate because of potentially lower detection limits. The aim of this study was to test the hypothesis that MCC-IMS is able to detect and monitor very low concentrations of sevoflurane (<10 ppbv) and to evaluate the exposure of hospital personnel to sevoflurane during paediatric anaesthesia and in the post anaesthesia care unit (PACU).

Methods

A MCC-IMS device was calibrated to several concentrations of sevoflurane and limits of detection (LOD) and quantification (LOQ) were calculated. Sevoflurane exposure of hospital personnel was measured at two anaesthesia workplaces and time-weighted average (TWA) values were calculated.

Results

The LOD was 0.0068 ppbv and the LOQ was 0.0189 ppbv. During paediatric anaesthesia the mean sevoflurane concentration was 46.9 ppbv (8.0 - 314.7 ppbv) with TWA values between 5.8 and 45.7 ppbv. In the PACU the mean sevoflurane concentration was 27.9 ppbv (8.0 – 170.2 ppbv) and TWA values reached from 8.3 to 45.1 ppbv.

Conclusions

MCC-IMS shows a significantly lower LOD and LOQ than comparable methods. It is a reliable technology for monitoring sevoflurane concentrations at anaesthesia workplaces and has a particular strength in quantifying low-level contaminations of sevoflurane. The exposure of the personnel working in these areas did not exceed recommended limits and therefore adverse health effects are unlikely.

Improvement of bioprocess monitoring: development of novel concepts

The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.