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Raman on-line monitoring approach for bioprocess and bio-pharmaceutical manufacturing

  • There is a high demand of monitoring in the era of QbD in industrial scale require new approaches to gain data rapidly and of sufficient quality in real time. Raman spectroscopy technology has great potential but not yet shown it fully in process on-line monitoring due to limitations such as i) uncomplete separation between cells and growth media alone, ii) general weak Raman signals of analytes in complex solutions and iii) strong background signals such as the auto-fluorescence, cosmic rays and surrounding lights overlapping the weak Raman signals. Here we demonstrate a Proof-of-Concept on an the example lactic acid bacteria process using a Streptococcus thermophiles fermentation. Results from three different Raman approaches are presented: 1) Time-Gated Raman Spectroscopy (TG-Raman), 2) Surface Enhanced Raman Spectroscopy (SERS) and 3) Raman process spectroscopy with NIR excitation combined with multivariate data analysis (MVDA) using Principal Component Analysis (PCA) and PartialThere is a high demand of monitoring in the era of QbD in industrial scale require new approaches to gain data rapidly and of sufficient quality in real time. Raman spectroscopy technology has great potential but not yet shown it fully in process on-line monitoring due to limitations such as i) uncomplete separation between cells and growth media alone, ii) general weak Raman signals of analytes in complex solutions and iii) strong background signals such as the auto-fluorescence, cosmic rays and surrounding lights overlapping the weak Raman signals. Here we demonstrate a Proof-of-Concept on an the example lactic acid bacteria process using a Streptococcus thermophiles fermentation. Results from three different Raman approaches are presented: 1) Time-Gated Raman Spectroscopy (TG-Raman), 2) Surface Enhanced Raman Spectroscopy (SERS) and 3) Raman process spectroscopy with NIR excitation combined with multivariate data analysis (MVDA) using Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR).zeige mehrzeige weniger

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  • PAT-colloquium_TimeGate.pdf
    eng
  • Kurzvortrag_Slide_ENG_Martin_Koegler.pdf
    eng
  • 2016-Tagungsband_12_Kolloquium_Prozessanalytik_Berlin_final_Impressum.pdf
    eng

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Metadaten
Autor*innen:M. Koegler, Andrea PaulORCiD, K. P. Alborch, M. Birkholz, S. Junne, P. Neubauer
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Tagungsband 12. Kolloquium Prozessanalytik
Jahr der Erstveröffentlichung:2016
Herausgeber (Institution):GDCh
Verlagsort:Berlin
Erste Seite:91
Letzte Seite:93
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Chemometrics; Raman; SERS; TimeGate
Veranstaltung:12. Kolloquium Arbeitskreis Prozessanalytik
Veranstaltungsort:Berlin, Germany
Beginndatum der Veranstaltung:28.11.2016
Enddatum der Veranstaltung:30.11.2016
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:20.12.2016
Referierte Publikation:Nein
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