{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/24765"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/24765","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"Combining Chemometrics and non-target GC-IMS gas phase analysis for improved bioprocess monitoring","abstract":"Industrial biotechnology uses microbial fermentation processes to develop or modify products and is a major contributor to the production of high-value goods, including pharmaceutical active ingredients, feed nutrients, and flavor and fragrance compounds. Process Analytical Technology (PAT) is essential to evaluate batch performance and ensure product quality. It aims to automatically measure as many parameters as necessary, in real time and to gain process insight through chemometric data analysis. Improving process understanding through novel and advanced analytical technologies is an active area of research. In bioprocess monitoring, most established PAT focuses on the liquid broth. A potential addition to established PAT is the analysis of volatile organic compounds (VOC) including microbial VOCs (mVOC), in the fermentation headspace. The hypothesis was, that mVOC undergo qualitative and quantitative changes during the dynamic fermentation process and can be correlated to parameters that are difficult to measure directly. Gas chromatography hyphenated to ion mobility spectrometry (GC-IMS) is an emerging analytical platform for trace-level VOC analysis. GC-IMS is particularly known for combining sensitivity, and selectivity due to two-dimensional separation with a robust and reliable setup, ideal for point-of-care use. The principal objective of this study was to examine the potential of GC-IMS-based VOC measurements in conjunction with chemometric non-target screening techniques as a soft sensor for bioprocess monitoring. In consequence of the lack of suitable software, a Python package for the multivariate analysis of GC IMS data was developed and subsequently published as open-source software under the name gc-ims-tools. It implements essential, data-specific functionalities, including file readers, visualizations, preprocessing, dataset organization, and workflows for common chemometric algorithms. A significant challenge in the analysis of GC-IMS data is the high dimensionality and collinearity. Two strategies were investigated in detail to address this question. The suitability of different dimensionality reduction and feature selection methods were evaluated as suitable preprocessing steps for a variety of machine learning algorithms and interpretation purposes. In the context of classification tasks, filtering based on PLS variable importance in projection scores proved to be an effective variable selection approach. The second approach, to reduce the dimensionality, was to extract peak lists from the raw data. Persistent homology was proposed as a suitable algorithm for automated peak detection in two-dimensional GC-IMS data. Its performance was evaluated on two publicly available datasets with different GC setups, peak shapes, and resolution. The utilization of certain preprocessing methods, mainly asymmetric least squares baseline correction was found to improve the number of correctly detected peaks. Two studies on exemplary processes demonstrated that GC-IMS-based VOC measurements do contain information about the process. In the first experiment, shake flask cultures of E. coli, S. cerevisiae, L. brevis, and P. fluorescens were prepared and monitored with an offline GC-IMS system following headspace incubation of a liquid sample. In addition to pure cultures, mixed cultures with combinations of two organisms were measured as simulated contaminations. The microorganisms were successfully classified by PLS-DA and the mixed cultures could be distinguished from the pure cultures. The results are a promising first step towards headspace contamination detection. Furthermore, the optical density, as a surrogate for biomass, could be predicted with the gas-phase measurements and support vector regression (SVR). An industrial, continuous Bacillus licheniformis process was monitored. The online data can be used for both, targeted tracing of specific compounds and combined with chemometrics as non-target “fingerprints” characteristic of more abstract variables, such as batch maturity. Some of the peaks detected showed oscillating concentration variations, not present in other PAT and not caused by the process control parameters, such as feed rate. In conclusion, GC-IMS-based VOC measurements proved to be a promising novel process analytical technology for bioprocess monitoring with high potential for future application development.","abstract_html":"Industrial biotechnology uses microbial fermentation processes to develop or modify products and is a major contributor to the production of high-value goods, including pharmaceutical active ingredients, feed nutrients, and flavor and fragrance compounds. Process Analytical Technology (PAT) is essential to evaluate batch performance and ensure product quality. It aims to automatically measure as many parameters as necessary, in real time and to gain process insight through chemometric data analysis. Improving process understanding through novel and advanced analytical technologies is an active area of research. In bioprocess monitoring, most established PAT focuses on the liquid broth. A potential addition to established PAT is the analysis of volatile organic compounds (VOC) including microbial VOCs (mVOC), in the fermentation headspace. The hypothesis was, that mVOC undergo qualitative and quantitative changes during the dynamic fermentation process and can be correlated to parameters that are difficult to measure directly. Gas chromatography hyphenated to ion mobility spectrometry (GC-IMS) is an emerging analytical platform for trace-level VOC analysis. GC-IMS is particularly known for combining sensitivity, and selectivity due to two-dimensional separation with a robust and reliable setup, ideal for point-of-care use. The principal objective of this study was to examine the potential of GC-IMS-based VOC measurements in conjunction with chemometric non-target screening techniques as a soft sensor for bioprocess monitoring. In consequence of the lack of suitable software, a Python package for the multivariate analysis of GC IMS data was developed and subsequently published as open-source software under the name gc-ims-tools. It implements essential, data-specific functionalities, including file readers, visualizations, preprocessing, dataset organization, and workflows for common chemometric algorithms. A significant challenge in the analysis of GC-IMS data is the high dimensionality and collinearity. Two strategies were investigated in detail to address this question. The suitability of different dimensionality reduction and feature selection methods were evaluated as suitable preprocessing steps for a variety of machine learning algorithms and interpretation purposes. In the context of classification tasks, filtering based on PLS variable importance in projection scores proved to be an effective variable selection approach. The second approach, to reduce the dimensionality, was to extract peak lists from the raw data. Persistent homology was proposed as a suitable algorithm for automated peak detection in two-dimensional GC-IMS data. Its performance was evaluated on two publicly available datasets with different GC setups, peak shapes, and resolution. The utilization of certain preprocessing methods, mainly asymmetric least squares baseline correction was found to improve the number of correctly detected peaks. Two studies on exemplary processes demonstrated that GC-IMS-based VOC measurements do contain information about the process. In the first experiment, shake flask cultures of E. coli, S. cerevisiae, L. brevis, and P. fluorescens were prepared and monitored with an offline GC-IMS system following headspace incubation of a liquid sample. In addition to pure cultures, mixed cultures with combinations of two organisms were measured as simulated contaminations. The microorganisms were successfully classified by PLS-DA and the mixed cultures could be distinguished from the pure cultures. The results are a promising first step towards headspace contamination detection. Furthermore, the optical density, as a surrogate for biomass, could be predicted with the gas-phase measurements and support vector regression (SVR). An industrial, continuous Bacillus licheniformis process was monitored. The online data can be used for both, targeted tracing of specific compounds and combined with chemometrics as non-target “fingerprints” characteristic of more abstract variables, such as batch maturity. Some of the peaks detected showed oscillating concentration variations, not present in other PAT and not caused by the process control parameters, such as feed rate. In conclusion, GC-IMS-based VOC measurements proved to be a promising novel process analytical technology for bioprocess monitoring with high potential for future application development.","abstract_has_math":false,"creators":["Christmann, Joscha"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Rohn, Sascha"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:28:29Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-23581"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-23581","href":"https://doi.org/10.14279/depositonce-23581","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/24765","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rohn, Sascha"]},{"key":"dc:creator","label":"Author","values":["Christmann, Joscha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-17T15:45:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-17T15:45:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/24765","https://doi.org/10.14279/depositonce-23581"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Industrial biotechnology uses microbial fermentation processes to develop or modify products and is a major contributor to the production of high-value goods, including pharmaceutical active ingredients, feed nutrients, and flavor and fragrance compounds. Process Analytical Technology (PAT) is essential to evaluate batch performance and ensure product quality. It aims to automatically measure as many parameters as necessary, in real time and to gain process insight through chemometric data analysis. Improving process understanding through novel and advanced analytical technologies is an active area of research. In bioprocess monitoring, most established PAT focuses on the liquid broth. A potential addition to established PAT is the analysis of volatile organic compounds (VOC) including microbial VOCs (mVOC), in the fermentation headspace. The hypothesis was, that mVOC undergo qualitative and quantitative changes during the dynamic fermentation process and can be correlated to parameters that are difficult to measure directly. Gas chromatography hyphenated to ion mobility spectrometry (GC-IMS) is an emerging analytical platform for trace-level VOC analysis. GC-IMS is particularly known for combining sensitivity, and selectivity due to two-dimensional separation with a robust and reliable setup, ideal for point-of-care use. The principal objective of this study was to examine the potential of GC-IMS-based VOC measurements in conjunction with chemometric non-target screening techniques as a soft sensor for bioprocess monitoring. In consequence of the lack of suitable software, a Python package for the multivariate analysis of GC IMS data was developed and subsequently published as open-source software under the name gc-ims-tools. It implements essential, data-specific functionalities, including file readers, visualizations, preprocessing, dataset organization, and workflows for common chemometric algorithms. A significant challenge in the analysis of GC-IMS data is the high dimensionality and collinearity. Two strategies were investigated in detail to address this question. The suitability of different dimensionality reduction and feature selection methods were evaluated as suitable preprocessing steps for a variety of machine learning algorithms and interpretation purposes. In the context of classification tasks, filtering based on PLS variable importance in projection scores proved to be an effective variable selection approach. The second approach, to reduce the dimensionality, was to extract peak lists from the raw data. Persistent homology was proposed as a suitable algorithm for automated peak detection in two-dimensional GC-IMS data. Its performance was evaluated on two publicly available datasets with different GC setups, peak shapes, and resolution. The utilization of certain preprocessing methods, mainly asymmetric least squares baseline correction was found to improve the number of correctly detected peaks. Two studies on exemplary processes demonstrated that GC-IMS-based VOC measurements do contain information about the process. In the first experiment, shake flask cultures of E. coli, S. cerevisiae, L. brevis, and P. fluorescens were prepared and monitored with an offline GC-IMS system following headspace incubation of a liquid sample. In addition to pure cultures, mixed cultures with combinations of two organisms were measured as simulated contaminations. The microorganisms were successfully classified by PLS-DA and the mixed cultures could be distinguished from the pure cultures. The results are a promising first step towards headspace contamination detection. Furthermore, the optical density, as a surrogate for biomass, could be predicted with the gas-phase measurements and support vector regression (SVR). An industrial, continuous Bacillus licheniformis process was monitored. The online data can be used for both, targeted tracing of specific compounds and combined with chemometrics as non-target “fingerprints” characteristic of more abstract variables, such as batch maturity. Some of the peaks detected showed oscillating concentration variations, not present in other PAT and not caused by the process control parameters, such as feed rate. In conclusion, GC-IMS-based VOC measurements proved to be a promising novel process analytical technology for bioprocess monitoring with high potential for future application development.","Die industrielle Biotechnologie nutzt mikrobielle Fermentationsprozesse, um Produkte zu entwickeln oder zu modifizieren, und leistet einen wichtigen Beitrag zur Herstellung von hochwertigen Gütern wie pharmazeutischen Wirkstoffen, Futtermitteln, Aromen und Duftstoffen. Die Prozessanalytik (PAT) ist für die Bewertung der Chargenleistung und die Sicherung der Produktqualität unerlässlich. Sie zielt darauf ab, so viele Parameter wie nötig automatisch und in Echtzeit zu messen und durch chemometrische Datenanalyse Erkenntnisse über den Prozess zu gewinnen. Die Verbesserung des Prozessverständnisses durch neue und fortgeschrittene Analysetechnologien ist ein aktives Forschungsgebiet. Bei der Überwachung von Bioprozessen konzentrieren sich die meisten etablierten PAT-Verfahren auf das flüssige Nährmedium. Eine mögliche Ergänzung der etablierten PAT ist die Analyse von flüchtigen organischen Verbindungen (), einschließlich mikrobieller VOC (mVOC), in der Dampfphase (Headspace). Die Hypothese war, dass sich mVOC während des dynamischen Fermentationsprozesses qualitativ und quantitativ verändern und mit Parametern korreliert werden können, die schwer direkt messbar sind. Die Gaschromatographie mit Ionenmobilitätsspektrometrie (GC-IMS) ist eine analytische Plattform für die Analyse von VOC im Spurenbereich. Die GC-IMS ist vor allem dafür bekannt, dass sie Empfindlichkeit und Selektivität aufgrund der zweidimensionalen Trennung mit einem robusten und zuverlässigen instrumentellen Aufbau kombiniert, der sich ideal für den Einsatz am Point-of-Care eignet. Das Hauptziel dieser Studie war es, das Potential von GC-IMS-basierten VOC-Messungen in Kombination mit chemometrischen Non-Target-Screening-Techniken als Soft-Sensor für die Bioprozessüberwachung zu untersuchen. Aufgrund des Mangels an geeigneter Auswertesoftware wurde ein Python-Paket für die multivariate Analyse von GC-IMS-Daten entwickelt und als Open-Source-Software unter dem Namen gc-ims-tools veröffentlicht. Implementiert sind wesentliche datenspezifische Funktionalitäten, einschließlich Dateireader, Visualisierung, Vorverarbeitung, Datensatzorganisation und Workflows für gängige chemometrische Algorithmen. Eine große Herausforderung bei der Analyse von GC-IMS Daten ist die hohe Dimensionalität und Co-Linearität. Zwei Strategien wurden im Detail untersucht, um dieses Problem zu lösen. Die Eignung verschiedener Methoden zur Dimensionalitätsreduktion und Auswahl von Variablen als geeignete Vorverarbeitungsschritte für eine Vielzahl von Algorithmen des maschinellen Lernens und für Interpretationszwecke wurde evaluiert. Im Zusammenhang mit Klassifizierungsaufgaben erwies sich die Filterung auf der Basis der PLS-Variablenbedeutung in den Projektionsergebnissen als effektiver Ansatz zur Variablenauswahl. Der zweite Ansatz zur Reduktion der Dimensionalität bestand in der Extraktion von Peaklisten aus den Rohdaten. Persistente Homologie wurde als geeigneter Algorithmus für die automatische Peakerkennung in zweidimensionalen GC-IMS-Daten vorgeschlagen. Seine Leistungsfähigkeit wurde an zwei öffentlich zugänglichen Datensätzen mit unterschiedlichen GC-Setups, Peakformen und Auflösungen evaluiert. Es wurde festgestellt, dass die Anwendung bestimmter Vorverarbeitungsmethoden, insbesondere die asymmetrische Basislinienkorrektur nach der Methode der kleinsten Quadrate, die Anzahl der korrekt erkannten Peaks erhöht. Zwei exemplarische Prozessstudien zeigten, dass GC-IMS-basierte VOC-Messungen Informationen über den Prozess liefern. Im ersten Experiment wurden Schüttelkolbenkulturen mit E. coli, S. cerevisiae, L. brevis und P. fluorescens angelegt und nach Headspace-Inkubation einer flüssigen Probe mit einem Offline-GC-IMS-System überwacht. Neben Reinkulturen wurden auch Mischkulturen mit Kombinationen von zwei Mikroorganismen als simulierte Kontaminationen gemessen. Die Mikroorganismen wurden erfolgreich mittels PLS-DA klassifiziert und die Mischkulturen konnten von den Reinkulturen unterschieden werden. Die Ergebnisse stellen einen vielversprechenden ersten Schritt zum Nachweis von mikrobiellen Kontaminationen dar. Darüber hinaus konnte die optische Dichte als Surrogat für die Biomasse durch Gasphasenmessungen und Support-Vector-Regression (SVR) vorhergesagt werden. Ein industrieller, kontinuierlicher Bacillus licheniformis Fermentationsprozess wurde überwacht. Einige der detektierten Peaks zeigten Konzentrationsprofile, die in anderen PATs nicht auftraten und nicht durch Prozesssteuerungsparameter wie z. B. die Zufuhrrate verursacht wurden. Zusammenfassend erwies sich die GC-IMS-basierte VOC-Messung als eine vielversprechende neue prozessanalytische Technologie für die Bioprozessüberwachung, die ein hohes Potenzial für zukünftige Anwendungsentwicklungen aufweist."]},{"key":"dc:title","label":"Title","values":["Combining Chemometrics and non-target GC-IMS gas phase analysis for improved bioprocess monitoring"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rohn, Sascha"],"dc:creator":["Christmann, Joscha"],"dc:date.accessioned":["2025-06-17T15:45:22Z"],"dc:date.available":["2025-06-17T15:45:22Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Industrial biotechnology uses microbial fermentation processes to develop or modify products and is a major contributor to the production of high-value goods, including pharmaceutical active ingredients, feed nutrients, and flavor and fragrance compounds. Process Analytical Technology (PAT) is essential to evaluate batch performance and ensure product quality. It aims to automatically measure as many parameters as necessary, in real time and to gain process insight through chemometric data analysis. Improving process understanding through novel and advanced analytical technologies is an active area of research. In bioprocess monitoring, most established PAT focuses on the liquid broth. A potential addition to established PAT is the analysis of volatile organic compounds (VOC) including microbial VOCs (mVOC), in the fermentation headspace. The hypothesis was, that mVOC undergo qualitative and quantitative changes during the dynamic fermentation process and can be correlated to parameters that are difficult to measure directly. Gas chromatography hyphenated to ion mobility spectrometry (GC-IMS) is an emerging analytical platform for trace-level VOC analysis. GC-IMS is particularly known for combining sensitivity, and selectivity due to two-dimensional separation with a robust and reliable setup, ideal for point-of-care use. The principal objective of this study was to examine the potential of GC-IMS-based VOC measurements in conjunction with chemometric non-target screening techniques as a soft sensor for bioprocess monitoring. In consequence of the lack of suitable software, a Python package for the multivariate analysis of GC IMS data was developed and subsequently published as open-source software under the name gc-ims-tools. It implements essential, data-specific functionalities, including file readers, visualizations, preprocessing, dataset organization, and workflows for common chemometric algorithms. A significant challenge in the analysis of GC-IMS data is the high dimensionality and collinearity. Two strategies were investigated in detail to address this question. The suitability of different dimensionality reduction and feature selection methods were evaluated as suitable preprocessing steps for a variety of machine learning algorithms and interpretation purposes. In the context of classification tasks, filtering based on PLS variable importance in projection scores proved to be an effective variable selection approach. The second approach, to reduce the dimensionality, was to extract peak lists from the raw data. Persistent homology was proposed as a suitable algorithm for automated peak detection in two-dimensional GC-IMS data. Its performance was evaluated on two publicly available datasets with different GC setups, peak shapes, and resolution. The utilization of certain preprocessing methods, mainly asymmetric least squares baseline correction was found to improve the number of correctly detected peaks. Two studies on exemplary processes demonstrated that GC-IMS-based VOC measurements do contain information about the process. In the first experiment, shake flask cultures of E. coli, S. cerevisiae, L. brevis, and P. fluorescens were prepared and monitored with an offline GC-IMS system following headspace incubation of a liquid sample. In addition to pure cultures, mixed cultures with combinations of two organisms were measured as simulated contaminations. The microorganisms were successfully classified by PLS-DA and the mixed cultures could be distinguished from the pure cultures. The results are a promising first step towards headspace contamination detection. Furthermore, the optical density, as a surrogate for biomass, could be predicted with the gas-phase measurements and support vector regression (SVR). An industrial, continuous Bacillus licheniformis process was monitored. The online data can be used for both, targeted tracing of specific compounds and combined with chemometrics as non-target “fingerprints” characteristic of more abstract variables, such as batch maturity. Some of the peaks detected showed oscillating concentration variations, not present in other PAT and not caused by the process control parameters, such as feed rate. In conclusion, GC-IMS-based VOC measurements proved to be a promising novel process analytical technology for bioprocess monitoring with high potential for future application development.","Die industrielle Biotechnologie nutzt mikrobielle Fermentationsprozesse, um Produkte zu entwickeln oder zu modifizieren, und leistet einen wichtigen Beitrag zur Herstellung von hochwertigen Gütern wie pharmazeutischen Wirkstoffen, Futtermitteln, Aromen und Duftstoffen. Die Prozessanalytik (PAT) ist für die Bewertung der Chargenleistung und die Sicherung der Produktqualität unerlässlich. Sie zielt darauf ab, so viele Parameter wie nötig automatisch und in Echtzeit zu messen und durch chemometrische Datenanalyse Erkenntnisse über den Prozess zu gewinnen. Die Verbesserung des Prozessverständnisses durch neue und fortgeschrittene Analysetechnologien ist ein aktives Forschungsgebiet. Bei der Überwachung von Bioprozessen konzentrieren sich die meisten etablierten PAT-Verfahren auf das flüssige Nährmedium. Eine mögliche Ergänzung der etablierten PAT ist die Analyse von flüchtigen organischen Verbindungen (), einschließlich mikrobieller VOC (mVOC), in der Dampfphase (Headspace). Die Hypothese war, dass sich mVOC während des dynamischen Fermentationsprozesses qualitativ und quantitativ verändern und mit Parametern korreliert werden können, die schwer direkt messbar sind. Die Gaschromatographie mit Ionenmobilitätsspektrometrie (GC-IMS) ist eine analytische Plattform für die Analyse von VOC im Spurenbereich. Die GC-IMS ist vor allem dafür bekannt, dass sie Empfindlichkeit und Selektivität aufgrund der zweidimensionalen Trennung mit einem robusten und zuverlässigen instrumentellen Aufbau kombiniert, der sich ideal für den Einsatz am Point-of-Care eignet. Das Hauptziel dieser Studie war es, das Potential von GC-IMS-basierten VOC-Messungen in Kombination mit chemometrischen Non-Target-Screening-Techniken als Soft-Sensor für die Bioprozessüberwachung zu untersuchen. Aufgrund des Mangels an geeigneter Auswertesoftware wurde ein Python-Paket für die multivariate Analyse von GC-IMS-Daten entwickelt und als Open-Source-Software unter dem Namen gc-ims-tools veröffentlicht. Implementiert sind wesentliche datenspezifische Funktionalitäten, einschließlich Dateireader, Visualisierung, Vorverarbeitung, Datensatzorganisation und Workflows für gängige chemometrische Algorithmen. Eine große Herausforderung bei der Analyse von GC-IMS Daten ist die hohe Dimensionalität und Co-Linearität. Zwei Strategien wurden im Detail untersucht, um dieses Problem zu lösen. Die Eignung verschiedener Methoden zur Dimensionalitätsreduktion und Auswahl von Variablen als geeignete Vorverarbeitungsschritte für eine Vielzahl von Algorithmen des maschinellen Lernens und für Interpretationszwecke wurde evaluiert. Im Zusammenhang mit Klassifizierungsaufgaben erwies sich die Filterung auf der Basis der PLS-Variablenbedeutung in den Projektionsergebnissen als effektiver Ansatz zur Variablenauswahl. Der zweite Ansatz zur Reduktion der Dimensionalität bestand in der Extraktion von Peaklisten aus den Rohdaten. Persistente Homologie wurde als geeigneter Algorithmus für die automatische Peakerkennung in zweidimensionalen GC-IMS-Daten vorgeschlagen. Seine Leistungsfähigkeit wurde an zwei öffentlich zugänglichen Datensätzen mit unterschiedlichen GC-Setups, Peakformen und Auflösungen evaluiert. Es wurde festgestellt, dass die Anwendung bestimmter Vorverarbeitungsmethoden, insbesondere die asymmetrische Basislinienkorrektur nach der Methode der kleinsten Quadrate, die Anzahl der korrekt erkannten Peaks erhöht. Zwei exemplarische Prozessstudien zeigten, dass GC-IMS-basierte VOC-Messungen Informationen über den Prozess liefern. Im ersten Experiment wurden Schüttelkolbenkulturen mit E. coli, S. cerevisiae, L. brevis und P. fluorescens angelegt und nach Headspace-Inkubation einer flüssigen Probe mit einem Offline-GC-IMS-System überwacht. Neben Reinkulturen wurden auch Mischkulturen mit Kombinationen von zwei Mikroorganismen als simulierte Kontaminationen gemessen. Die Mikroorganismen wurden erfolgreich mittels PLS-DA klassifiziert und die Mischkulturen konnten von den Reinkulturen unterschieden werden. Die Ergebnisse stellen einen vielversprechenden ersten Schritt zum Nachweis von mikrobiellen Kontaminationen dar. Darüber hinaus konnte die optische Dichte als Surrogat für die Biomasse durch Gasphasenmessungen und Support-Vector-Regression (SVR) vorhergesagt werden. Ein industrieller, kontinuierlicher Bacillus licheniformis Fermentationsprozess wurde überwacht. Einige der detektierten Peaks zeigten Konzentrationsprofile, die in anderen PATs nicht auftraten und nicht durch Prozesssteuerungsparameter wie z. B. die Zufuhrrate verursacht wurden. Zusammenfassend erwies sich die GC-IMS-basierte VOC-Messung als eine vielversprechende neue prozessanalytische Technologie für die Bioprozessüberwachung, die ein hohes Potenzial für zukünftige Anwendungsentwicklungen aufweist."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/24765","https://doi.org/10.14279/depositonce-23581"],"dc:language.iso":["en"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:title":["Combining Chemometrics and non-target GC-IMS gas phase analysis for improved bioprocess monitoring"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:29Z"}