Analysis methods for gas sensor arrays

Der Schwerpunkt dieser Arbeit lag in der Entwicklung neuer Sensorsysteme und der Verbesserung der damit verbundenen Auswerteverfahren. Die im Rahmen dieser Arbeit entwickelten Sensorsysteme sind bereits kommerziell verfügbar und werden in verschiedensten Anwendungen eingesetzt. Sie sind Grundlage für eine ganze Reihe weiterer Arbeiten und wissenschaftlicher Publikationen. Durch den breiten Einsatz der entwickelten Systeme war es möglich umfangreiche Datensätze auszuwerten und Anhaltspunkte für praxisrelevante Verbesserungen zu erhalten. Die Arbeit beschäftigt sich deshalb im Ergebnissteil im wesentlichen mit Verfahren, die die Einsetzbarkeit und Zuverlässigkeit derartiger Systeme verbessern. Die Grundlage für alle diese Verbesserungen sind vereinfachte Funktionsmodelle der chemischen Sensoren und der daraus aufgebauten Systeme, die im Rahmen dieser Arbeit aufgestellt wurden. Von diesen einfachen Modellen wurden dann verschiedene Verfahren zur Driftkompensation und zur Querempfindlichkeitsreduktion abgeleitet und anhand von realen Messdaten überprüft. Als Rekalibrationsverfahren entstanden daraus ein sehr einfach anzuwendendes Verfahren, welches spezielle Eigenschaften der QMBSensoren ausnutzt und ohne zusätzliche Kalibrationsmessungen auskommt, und ein naheliegendes Verfahren, welches lediglich einen Kalibrationsfaktor pro Sensor verwendet. Ein wesentlich komplexeres Verfahren, welches auch in der Lage ist Veränderungen der Selektivitätsmuster der Sensoren zu berücksichtigen, wurde ebenfalls vorgestellt. Allerdings sind die Einschränkungen dieses Verfahrens im Vergleich zum notwendigen Aufwand zu groß, um es im praktischen Einsatz zu empfehlen. Darüber hinaus wurde eine einfaches Verfahren, welches lediglich einen Kalibrationsfaktor pro Sensor verwendet, implementiert und anhand verschiedener Datensätze mit gutem Erfolg getestet. Durch die einfache Handhabung findet dieses Verfahren breite Anwendung in der Praxis. Zur Elimination von Querempfindlichkeiten wurden in dieser Arbeit insgesamt fünf verschiedene Verfahren angewendet und miteinander verglichen. Vier dieser Verfahren wurden dabei von den einfachen Funktionsmodellen abgeleitet und zeigen zum Teil drastische Vereinfachungen bei der Anwendung. Insbesondere die Methode der Projektion auf den Residualraum erlaubt es mit geringstem experimentellem Aufwand erhebliche Verbesserungen zu erreichen. Darüber hinaus wurde gezeigt, wie durch Anwendung einer einfachen Normierung die Temperaturempfindlichkeit von QMB Sensoren drastisch reduziert werden kann. Dies ermöglicht es deutlich kostengünstigere Messsysteme aufzubauen, da eine aufwendige Thermostatisierung in bestimmten Anwendungen entfallen kann. Obwohl die aufgestellten Funktionsmodelle sehr einfach gehalten sind, ermöglichen sie es Einflüsse, die zu Vorhersagefehlern führen könnten, explizit zu berücksichtigen. Durch Berechnung simulierter Daten kann auf Basis dieser Modelle Vorabwissen über bestimmte Umgebungseinflüsse oder die zu untersuchenden Proben in die Referenzdaten aufgenommen werden. Anhand eines Beispiels konnte erfolgreich demonstriert werden, dass sich auf diese Weise die Vorhersagequalität eines Auswerteverfahrens wesentlich verbessern lässt. Zudem werden durch Simulation entsprechender Testdaten Analysen möglich, wie sich Fehlerursachen auf ein Messverfahren auswirken, sodass Gegenmaßnahmen möglich sind. Ein Überblick über verschiedene Fehlerquellen wird deshalb im Ergebnisteil ebenfalls gegeben.The main focus in this thesis is the development of novel sensor systems and the improvement of the incorporated data analysis algorithms. The sensor systems, that have been developed in the scope of this work, are already commercially available and are applied in a multitude of applications. Through the broad deployment of these systems it has been possible to analyse extensive datasets and to derive clues for improvements relevant to their practical use. The basis for all these improvements are simplified functional models of chemical sensors and sensor systems. Several procedures to compensate for drift and cross sensitivities have been deduced from these simple models and were tested against real life data. A simple to use method for recalibration was developed, which takes advantage of special properties of QMB sensors and does completely without recalibration measurements. A more complex method was suggested, which is able to account for changes in the selectivity pattern of sensors. However, this complex method is not recommended for practical use, due to its constraints in comparison to the efforts needed. In addition a simple method, employing one calibration factor per sensor, has been implemented and was tested against various datasets with good success. It is broadly used in practical applications because of its simple handling. Five different algorithms for the elimination of cross sensitivities have been compared in this work. Four of these methods are derived from the simplified functional methods and may drastically ease the application of sensor arrays. Especially the method using a residual space projection may achieve substantial improvements at a minimum of experimental efforts. In addition it has been shown how simple normalization may reduce temperature sensitivity of QMB sensors. This allows for significant cost savings, since extensive temperature control may be avoided in certain applications. Although the employed functional models are very simple, they do permit to explicitly account for influences leading to prediction error. Pre-existing knowledge about environmental influences or the samples under investigation may be incorporated into the reference data through the calculation of simulated data on the basis of these models. It was demonstrated in one example that the prediction quality of an analysis method may be significantly improved in this way. Furthermore, it is possible to test the influence of certain error sources on a measurement procedure by the simulation of corresponding test data. An overview of various error sources is therefore given in this work

Non-contact breath sampling for sensor-based breath analysis

Breath analysis holds great promise for real-time and non-invasive medical diagnosis. Thus, there is a considerable need for simple-in-use and portable analyzers for rapid detection of breath indicators for different diseases in their early stages. Sensor technology meets all of these demands. However, miniaturized breath analyzers require adequate breath sampling methods. In this context, we propose non-contact sampling; namely the collection of breath samples by exhalation from a distance into a miniaturized collector without bringing the mouth into direct contact with the analyzing device. To evaluate this approach different breathing maneuvers have been tested in a real-time regime on a cohort of 23 volunteers using proton transfer reaction mass spectrometry. The breathing maneuvers embraced distinct depths of respiration, exhalation manners, size of the mouth opening and different sampling distances. Two inhalation modes(normal, relaxed breathing and deep breathing) and two exhalation manners(via smaller and wider lips opening)forming four sampling scenarios were selected. A sampling distance of approximately 2 cm was found to be a reasonable trade-off between sample dilution and requirement of no physical contact of the subject with the analyzer. All four scenarios exhibited comparable measurement reproducibility spread of around 10%. For normal, relaxed inspiration both dead-space and end-tidal phases of exhalation lasted approximately 1.5 s for both expiration protocols. Deep inhalation prolongs the end-tidal phase to about 3 s in the case of blowing via a small lips opening, and by 50% when the air is exhaled via a wide one. In conclusion, non-contact breath sampling can be considered as a promising alternative to the existing breath sampling methods, being relatively close to natural spontaneous breathing. --///-- This work is licensed under a CC BY 4.0 license.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 644031, Smart Phone for Disease Detection from Exhaled Breath. PM and KU gratefully acknowledge financial support from the Austrian Science Fund (FWF) under Grant No. P24736-B23

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class

Breath Sensor Technology for the Use in Mechanical Lung Ventilation Equipment for Monitoring Critically Ill Patients

Background: The need for mechanical lung ventilation is common in critically ill patients, either with COVID-19 infection or due to other causes. Monitoring of patients being ventilated is essential for timely and improved management. We here propose the use of a novel breath volatile organic compound sensor technology to be used in a mechanical lung ventilation machine for this purpose; the technology was evaluated in critically ill COVID-19 patients on mechanical lung ventilation. Methods: Based on the consistency results of our study data, the breath sensor device with metal oxide gas sensors and environment-controlling sensors was mounted on the ventilation exhaust port of the ventilation machine; this allowed to ensure additional safety since the device was placed outside the contour between the patient and equipment. Results: The sensors allowed stable registration of the signals for up to several weeks for 10 patients in total, depending on the storage amount; a proportion of patients were intubated or received tracheostoma during the evaluation period. Future studies are on the way to correlate sensor readings to other parameters characterizing the severity of the patient condition and outcome. Conclusions: We suppose that such technology will allow patient monitoring in real-time for timely identification of deterioration, potentially requiring some change of management. The obtained results are preliminary and further studies are needed to examine their clinical significance