Skip to main content
Log in

Spectroscopic sensors for in-line bioprocess monitoring in research and pharmaceutical industrial application

  • Review
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

The use of spectroscopic sensors for bioprocess monitoring is a powerful tool within the process analytical technology (PAT) initiative of the US Food and Drug Administration. Spectroscopic sensors enable the simultaneous real-time bioprocess monitoring of various critical process parameters including biological, chemical, and physical variables during the entire biotechnological production process. This potential can be realized through the combination of spectroscopic measurements (UV/Vis spectroscopy, IR spectroscopy, fluorescence spectroscopy, and Raman spectroscopy) with multivariate data analysis to obtain relevant process information out of an enormous amount of data. This review summarizes the newest results from science and industry after the establishment of the PAT initiative and gives a critical overview of the most common in-line spectroscopic techniques. Examples are provided of the wide range of possible applications in upstream processing and downstream processing of spectroscopic sensors for real-time monitoring to optimize productivity and ensure product quality in the pharmaceutical industry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. FDA. Guidance for industry PAT — a framework for innovative pharmaceutical development, manufacuring, and quality assurance. Rockville: FDA; 2004. p. 16.

    Google Scholar 

  2. Rathore AS, Bhambure R, Ghare V. Process analytical technology (PAT) for biopharmaceutical products. Anal Bioanal Chem. 2010;398:137–54. doi:10.1007/s00216-010-3781-x.

    Article  CAS  Google Scholar 

  3. Hoehse M, Alves-Rausch J, Prediger A, Roch P, Grimm C. Near-infrared spectroscopy in upstream bioprocesses. Pharm Bioprocess. 2015;3:153–72. doi:10.4155/pbp.15.1.

    Article  CAS  Google Scholar 

  4. Clavaud M, Roggo Y, Von Daeniken R, Liebler A, Schwabe JO. Chemometrics and in-line near infrared spectroscopic monitoring of a biopharmaceutical Chinese hamster ovary cell culture: prediction of multiple cultivation variables. Talanta. 2013;111:28–38. doi:10.1016/j.talanta.2013.03.044.

    Article  CAS  Google Scholar 

  5. Biechele P, Busse C, Solle D, Scheper T, Reardon K. Sensor systems for bioprocess monitoring. Eng Life Sci. 2015;15:469–88. doi:10.1002/elsc.201500014.

    Article  CAS  Google Scholar 

  6. Scheper T, Hitzmann B, Stärk E, Ulber R, Faurie R, Sosnitza P, et al. Bioanalytics: detailed insight into bioprocesses. Anal Chim Acta. 1999;400:121–34.

    Article  CAS  Google Scholar 

  7. Becker T, Hitzmann B, Muffler K, Pörtner R, Reardon KF, Stahl F, et al. Future aspects of bioprocess monitoring. Adv Biochem Eng Biotechnol. 2006;105:249–93. doi:10.1007/10_2006_036.

    Google Scholar 

  8. Glindkamp A, Riechers D, Rehbock C, Hitzmann B, Scheper T, Reardon KF. Sensors in disposable bioreactors status and trends. Adv Biochem Eng Biotechnol. 2010;115:145–69. doi:10.1007/10_2009_10.

    Google Scholar 

  9. Kessler RW. Prozessanalytik: Strategien und Fallbeispiele aus der industriellen Praxis. Weinheim: Wiley; 2006. p. 3–10.

    Book  Google Scholar 

  10. Chmiel H, Briechle S. Bioprozesstechnik 3. Auflage Bioprozesstechnik. 2014. doi:10.1007/s13398-014-0173-7.2.

    Google Scholar 

  11. Scheper TH, Reardon KF. Sensors in biotechnology. In: Göpel W, Hesse J, Zemel JN, editors. Sensors set: a comprehensive survey. Weinheim: Wiley; 2008. p. 1023–46. doi:10.1002/9783527619269.ch9b.

    Google Scholar 

  12. Vojinovic V, Cabral JMS, Fonseca LP. Real-time bioprocess monitoring: Part I: in situ sensors. Sensors Actuators B Chem. 2006;114:1083–91. doi:10.1016/j.snb.2005.07.059.

    Article  CAS  Google Scholar 

  13. Růžička J, Hansen EH. Flow injection analysis. Anal Chim Acta. 1978;99:37–76. doi:10.1016/S0003-2670(01)84498-6.

    Article  Google Scholar 

  14. Bochenkov VE, Sergeev GB. Sensitivity, selectivity, and stability of gas-sensitive metal-oxide nanostructures. In: Umar A, Hahn YB, editors. Metal oxide nanostructures and their applications, vol 3; 2010. Valencia, CA: ASP. p. 31–52.

  15. Herwig C. Prozess analytische technologie in der biotechnologie. Chemie-Ingenieur-Technik. 2010;82:405–14. doi:10.1002/cite.200900136.

    Article  CAS  Google Scholar 

  16. Lourenço ND, Lopes JA, Almeida CF, Sarraguça MC, Pinheiro HM. Bioreactor monitoring with spectroscopy and chemometrics: a review. Anal Bioanal Chem. 2012;404:1211–37. doi:10.1007/s00216-012-6073-9.

    Article  CAS  Google Scholar 

  17. Rathore AS, Bhushan N, Hadpe S. Chemometrics applications in biotech processes: a review. Biotechnol Prog. 2011;27:307–15. doi:10.1002/btpr.561.

    Article  CAS  Google Scholar 

  18. Tkachenko NV. Steady state absorption spectroscopy. In: Optical spectroscopy: methods and instrumentations; 2006. New York: Elsevier. Chap. 5, p. 89–106.

  19. Kessler W. Multivariate Datenanalyse: für die Pharma-, Bio- und Prozessanalytik. Weinheim: Wiley; 2013. doi:10.1002/9783527610037.

    Google Scholar 

  20. Glassey J. Multivariate data analysis for advancing the interpretation of bioprocess measurement and monitoring data. In: Mandenius C-F, Titchener-Hooker NJ, editors. Measurement, monitoring, modelling and control of bioprocesses. Berlin: Springer; 2013. p. 167–91.

    Google Scholar 

  21. Henriques JG, Buziol S, Stocker E, Voogd A, Menezes JC. Monitoring mammalian cell cultivations for monoclonal antibody production using near-infrared spectroscopy. Adv Biochem Eng Biotechnol. 2009;116:73–97. doi:10.1007/10_2009_11.

    CAS  Google Scholar 

  22. Pomerantsev AL, Rodionova OY. Process analytical technology: a critical view of the chemometricians. J Chemom. 2012;26:299–310. doi:10.1002/cem.2445.

    Article  CAS  Google Scholar 

  23. Alves-Rausch J, Bienert R, Grimm C, Bergmaier D. Real time in-line monitoring of large scale Bacillus fermentations with near-infrared spectroscopy. J Biotechnol. 2014;189:120–8. doi:10.1016/j.jbiotec.2014.09.004.

    Article  CAS  Google Scholar 

  24. Navrátil M, Norberg A, Lembrén L, Mandenius CF. On-line multi-analyzer monitoring of biomass, glucose and acetate for growth rate control of a Vibrio cholerae fed-batch cultivation. J Biotechnol. 2005;115:67–79. doi:10.1016/j.jbiotec.2004.07.013.

    Article  CAS  Google Scholar 

  25. Roggo Y, Chalus P, Maurer L, Lema-Martinez C, Edmond A, Jent N. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. J Pharm Biomed Anal. 2007;44:683–700. doi:10.1016/j.jpba.2007.03.023.

    Article  CAS  Google Scholar 

  26. Cervera AE, Petersen N, Lantz AE, Larsen A, Gernaey KV. Application of near-infrared spectroscopy for monitoring and control of cell culture and fermentation. Biotechnol Prog. 2009;25:1561–81. doi:10.1002/btpr.280.

    CAS  Google Scholar 

  27. Clementschitsch F, Jürgen K, Florentina P, Karl B. Sensor combination and chemometric modelling for improved process monitoring in recombinant E. coli fed-batch cultivations. J Biotechnol. 2005;120:183–96. doi:10.1016/j.jbiotec.2005.05.030.

    Article  CAS  Google Scholar 

  28. Boehl D, Solle D, Hitzmann B, Scheper T. Chemometric modelling with two-dimensional fluorescence data for Claviceps purpurea bioprocess characterization. J Biotechnol. 2003;105:179–88. doi:10.1016/S0168-1656(03)00189-5.

    Article  CAS  Google Scholar 

  29. Teixeira AP, Portugal CAM, Carinhas N, Dias JML, Crespo JP, Alves PM, et al. In situ 2D fluorometry and chemometric monitoring of mammalian cell cultures. Biotechnol Bioeng. 2009;102:1098–106. doi:10.1002/bit.22125.

    Article  CAS  Google Scholar 

  30. Swinehart DF. The Beer-Lambert law. J Chem Educ. 1962;39:333. doi:10.1021/ed039p333.

    Article  CAS  Google Scholar 

  31. Samorski M, Müller-Newen G, Büchs J. Quasi-continuous combined scattered light and fluorescence measurements: a novel measurement technique for shaken microtiter plates. Biotechnol Bioeng. 2005;92:61–8. doi:10.1002/bit.20573.

    Article  CAS  Google Scholar 

  32. Edlich A, Magdanz V, Rasch D, Demming S, Aliasghar Zadeh S, Segura R, et al. Microfluidic reactor for continuous cultivation of Saccharomyces cerevisiae. Biotechnol Prog. 2010;26:1259–70. doi:10.1002/btpr.449.

    Article  CAS  Google Scholar 

  33. Ude C, Schmidt-Hager J, Findeis M, John GT, Scheper T, Beutel S. Application of an online-biomass sensor in an optical multisensory platform prototype for growth monitoring of biotechnical relevant microorganism and cell lines in single-use shake flasks. Sensors (Basel). 2014;14:17390–405. doi:10.3390/s140917390.

    Article  CAS  Google Scholar 

  34. Kiefer J, Ebel N, Schlücker E, Leipertz A. Characterization of Escherichia coli suspensions using UV/Vis/NIR absorption spectroscopy. Anal Methods. 2010;2:123. doi:10.1039/b9ay00185a.

    Article  CAS  Google Scholar 

  35. Alupoaei CE, García-Rubio LH. Growth behavior of microorganisms using UV-Vis spectroscopy: Escherichia coli. Biotechnol Bioeng. 2004;86:163–7. doi:10.1002/bit.20001.

    Article  CAS  Google Scholar 

  36. Hansen SK, Jamali B, Hubbuch J. Selective high throughput protein quantification based on UV absorption spectra. Biotechnol Bioeng. 2013;110:448–60. doi:10.1002/bit.24712.

    Article  CAS  Google Scholar 

  37. Hansen SK, Skibsted E, Staby A, Hubbuch J. A label-free methodology for selective protein quantification by means of absorption measurements. Biotechnol Bioeng. 2011;108:2661–9. doi:10.1002/bit.23229.

    Article  CAS  Google Scholar 

  38. Brestrich N, Briskot T, Osberghaus A, Hubbuch J. A tool for selective inline quantification of co-eluting proteins in chromatography using spectral analysis and partial least squares regression. Biotechnol Bioeng. 2014;111:1365–73. doi:10.1002/bit.25194.

    Article  CAS  Google Scholar 

  39. Rathore AS, Li X, Bartkowski W, Sharma A, Lu Y. Case study and application of process analytical technology (PAT) towards bioprocessing: use of tryptophan fluorescence as at-line tool for making pooling decisions for process chromatography. Biotechnol Prog. 2009;25:1433–9. doi:10.1002/btpr.212.

    Article  CAS  Google Scholar 

  40. Kamga MH, Woo Lee H, Liu J, Yoon S. Quantification of protein mixture in chromatographic separation using multi-wavelength UV spectra. Biotechnol Prog. 2013;29:664–71. doi:10.1002/btpr.1712.

    Article  CAS  Google Scholar 

  41. Renaut P, Annarelli D. Evaluation of a new single-use UV sensor for protein a capture. Bioprocess Int. 2013;11:48–51.

    Google Scholar 

  42. Hahn T, Baumann P, Huuk T, Heuveline V, Hubbuch J. UV absorption-based inverse modeling of protein chromatography. Eng Life Sci. 2016;16:99–106. doi:10.1002/elsc.201400247.

    Article  CAS  Google Scholar 

  43. Kiviharju K, Salonen K, Moilanen U, Meskanen E, Leisola M, Eerikäinen T. On-line biomass measurements in bioreactor cultivations: comparison study of two on-line probes. J Ind Microbiol Biotechnol. 2007;34:561–6. doi:10.1007/s10295-007-0233-5.

    Article  CAS  Google Scholar 

  44. Nordon A, Littlejohn D, Dann AS, Jeffkins PA, Richardson MD, Stimpson SL. In situ monitoring of the seed stage of a fermentation process using non-invasive NIR spectrometry. Analyst. 2008;133:660–6. doi:10.1039/b719318a.

    Article  CAS  Google Scholar 

  45. Soons ZITA, Streefland M, van Straten G, van Boxtel AJB. Assessment of near infrared and “software sensor” for biomass monitoring and control. Chemom Intell Lab Syst. 2008;94:166–74. doi:10.1016/j.chemolab.2008.07.009.

    Article  CAS  Google Scholar 

  46. Streefland M, Van Herpen PFG, Van De Waterbeemd B, Van Der Pol LA, Beuvery EC, Tramper J, et al. A practical approach for exploration and modeling of the design space of a bacterial vaccine cultivation process. Biotechnol Bioeng. 2009;104:492–504. doi:10.1002/bit.22425.

    Article  CAS  Google Scholar 

  47. Lopes MB, Scholtz T, Silva D, Santos I, Silva T, Sampaio P, Couto A, Lopes VV, Calado CRC. Modelling, monitoring and control of plasmid bioproduction in Escherichia coli cultures. 2012 I.E. 2nd Port Meet Bioeng ENBENG 2012. doi:10.1109/ENBENG.2012.6331370.

  48. Petersen N, Ödman P, Cervera Padrell AE, Stocks S, Lantz AE, Gernaey KV. In situ near infrared spectroscopy for analyte-specific monitoring of glucose and ammonium in Streptomyces coelicolor fermentations. Biotechnol Prog. 2010;26:263–71. doi:10.1002/btpr.288.

    Article  CAS  Google Scholar 

  49. Goldfeld M, Christensen J, Pollard D, Gibson ER, Olesberg JT, Koerperick EJ, et al. Advanced near-infrared monitor for stable real-time measurement and control of Pichia pastoris bioprocesses. Biotechnol Prog. 2014;30:749–59. doi:10.1002/btpr.1890.

    Article  CAS  Google Scholar 

  50. Rodrigues LO, Vieira L, Cardoso JP, Menezes JC. The use of NIR as a multi-parametric in situ monitoring technique in filamentous fermentation systems. Talanta. 2008;75:1356–61.

    Article  CAS  Google Scholar 

  51. Roychoudhury P, O’Kennedy R, McNeil B, Harvey LM. Multiplexing fibre optic near infrared (NIR) spectroscopy as an emerging technology to monitor industrial bioprocesses. Anal Chim Acta. 2007;590:110–7. doi:10.1016/j.aca.2007.03.011.

    Article  CAS  Google Scholar 

  52. Sandor M, Rüdinger F, Bienert R, Grimm C, Solle D, Scheper T. Comparative study of non-invasive monitoring via infrared spectroscopy for mammalian cell cultivations. J Biotechnol. 2013;168:636–45. doi:10.1016/j.jbiotec.2013.08.002.

    Article  CAS  Google Scholar 

  53. Qiu J, Arnold MA, Murhammer DW. On-line near infrared bioreactor monitoring of cell density and concentrations of glucose and lactate during insect cell cultivation. J Biotechnol. 2014;173:106–11. doi:10.1016/j.jbiotec.2014.01.009.

    Article  CAS  Google Scholar 

  54. Petiot E, Bernard-Moulin P, Magadoux T, Gény C, Pinton H, Marc A. In situ quantification of microcarrier animal cell cultures using near-infrared spectroscopy. Process Biochem. 2010;45:1832–6. doi:10.1016/j.procbio.2010.08.010.

    Article  CAS  Google Scholar 

  55. Walther C, Mayer S, Jungbauer A, Dürauer A. Getting ready for PAT: scale up and inline monitoring of protein refolding of Npro fusion proteins. Process Biochem. 2014;49:1113–21. doi:10.1016/j.procbio.2014.03.022.

    Article  CAS  Google Scholar 

  56. Rodrigues LO, Cardoso JP, Menezes JC. Applying near-infrared spectroscopy in downstream processing: one calibration for multiple clarification processes of fermentation media. Biotechnol Prog. 2008;24:432–5. doi:10.1021/bp070328x.

    Article  CAS  Google Scholar 

  57. Crowley J, Arnold SA, Wood N, Harvey LM, McNeil B. Monitoring a high cell density recombinant Pichia pastoris fed-batch bioprocess using transmission and reflectance near infrared spectroscopy. Enzyme Microb Technol. 2005;36:621–8. doi:10.1016/j.enzmictec.2003.12.016.

    Article  CAS  Google Scholar 

  58. Forbes RA, Persinger ML, Smith DR. Development and validation of analytical methodology for near-infrared conformance testing of pharmaceutical intermediates. J Pharm Biomed Anal. 1996;15:315–27. doi:10.1016/S0731-7085(96)01875-4.

    Article  CAS  Google Scholar 

  59. Hakemeyer C, Strauss U, Werz S, Jose GE, Folque F, Menezes JC. At-line NIR spectroscopy as effective PAT monitoring technique in Mab cultivations during process development and manufacturing. Talanta. 2012;90:12–21. doi:10.1016/j.talanta.2011.12.042.

    Article  CAS  Google Scholar 

  60. Misra NN, Sullivan C, Cullen PJ. Process analytical technology (PAT) and multivariate methods for downstream processes. Curr Biochem Eng. 2015;2:4–16. doi:10.2174/2213385203666150219231836.

    Article  CAS  Google Scholar 

  61. Beutel S, Henkel S. In situ sensor techniques in modern bioprocess monitoring. Appl Microbiol Biotechnol. 2011;91:1493–505. doi:10.1007/s00253-011-3470-5.

    Article  CAS  Google Scholar 

  62. Hantelmann K, Kollecker M, Hüll D, Hitzmann B, Scheper T. Two-dimensional fluorescence spectroscopy: a novel approach for controlling fed-batch cultivations. J Biotechnol. 2006;121:410–7. doi:10.1016/j.jbiotec.2005.07.016.

    Article  CAS  Google Scholar 

  63. Mazarevica G, Diewok J, Baena JR, Rosenberg E, Lendl B. On-line fermentation monitoring by mid-infrared spectroscopy. Appl Spectrosc. 2004;58:804–10. doi:10.1366/0003702041389229.

    Article  CAS  Google Scholar 

  64. Schenk J, Marison IW, von Stockar U. Simplified Fourier-transform mid-infrared spectroscopy calibration based on a spectra library for the on-line monitoring of bioprocesses. Anal Chim Acta. 2007;591:132–40. doi:10.1016/j.aca.2007.03.056.

    Article  CAS  Google Scholar 

  65. Schenk J, Marison IW, von Stockar U. A simple method to monitor and control methanol feeding of Pichia pastoris fermentations using mid-IR spectroscopy. J Biotechnol. 2007;128:344–53. doi:10.1016/j.jbiotec.2006.09.015.

    Article  CAS  Google Scholar 

  66. Veale EL, Irudayaraj J, Demirci A. An on-line approach to monitor ethanol fermentation using FTIR spectroscopy. Biotechnol Prog. 2003;23:494–500. doi:10.1021/bp060306v.

    Article  CAS  Google Scholar 

  67. Kornmann H, Valentinotti S, Duboc P, Marison I, Von Stockar U. Monitoring and control of Gluconacetobacter xylinus fed-batch cultures using in situ mid-IR spectroscopy. J Biotechnol. 2004;113:231–45. doi:10.1016/j.jbiotec.2004.03.029.

    Article  CAS  Google Scholar 

  68. Schenk J, Marison IW, Von Stockar U. pH prediction and control in bioprocesses using mid-infrared spectroscopy. Biotechnol Bioeng. 2008;100:82–93. doi:10.1002/bit.21719.

    Article  CAS  Google Scholar 

  69. Franco VG, Perín JC, Mantovani VE, Goicoechea HC. Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection. Talanta. 2006;68:1005–12. doi:10.1016/j.talanta.2005.07.003.

    Article  CAS  Google Scholar 

  70. Roychoudhury P, McNeil B, Harvey LM. Simultaneous determination of glycerol and clavulanic acid in an antibiotic bioprocess using attenuated total reflectance mid infrared spectroscopy. Anal Chim Acta. 2007;585:246–52. doi:10.1016/j.aca.2006.12.051.

    Article  CAS  Google Scholar 

  71. Schenk J, Viscasillas C, Marison IW, von Stockar U. On-line monitoring of nine different batch cultures of E. coli by mid-infrared spectroscopy, using a single spectra library for calibration. J Biotechnol. 2008;134:93–102. doi:10.1016/j.jbiotec.2007.12.014.

    Article  CAS  Google Scholar 

  72. Roychoudhury P, Harvey LM, McNeil B. The potential of mid infrared spectroscopy (MIRS) for real time bioprocess monitoring. Anal Chim Acta. 2006;571:159–66. doi:10.1016/j.aca.2006.04.086.

    Article  CAS  Google Scholar 

  73. Foley R, Hennessy S, Marison IW. Potential of mid-infrared spectroscopy for on-line monitoring of mammalian cell culture medium components. Appl Spectrosc. 2012;66:33–9. doi:10.1366/11-06395.

    Article  CAS  Google Scholar 

  74. Capito F, Skudas R, Kolmar H, Hunzinger C. At-line mid infrared spectroscopy for monitoring downstream processing unit operations. Process Biochem. 2015;50:997–1005. doi:10.1016/j.procbio.2015.03.005.

    Article  CAS  Google Scholar 

  75. Lakowicz JR, editor. Principles of fluorescence spectroscopy. Berlin: Springer. doi:10.1007/978-0-387-46312-4.

  76. Faassen SM, Hitzmann B. Fluorescence spectroscopy and chemometric modeling for bioprocess monitoring. Sensors (Switzerland). 2015;15:10271–91. doi:10.3390/s150510271.

    Article  CAS  Google Scholar 

  77. Hisiger S, Jolicoeur M. A multiwavelength fluorescence probe: Is one probe capable for on-line monitoring of recombinant protein production and biomass activity. J Biotechnol. 2005;117:325–36. doi:10.1016/j.jbiotec.2005.03.004.

    Article  CAS  Google Scholar 

  78. Surribas A, Montesinos JL, Valero FF. Biomass estimation using fluorescence measurements in Pichia pastoris bioprocess. J Chem Technol Biotechnol. 2006;81:23–8. doi:10.1002/jctb.1352.

    Article  CAS  Google Scholar 

  79. Amigo JM, Surribas A, Coello J, Montesinos JL, Maspoch S, Valero F. On-line parallel factor analysis. A step forward in the monitoring of bioprocesses in real time. Chemom Intell Lab Syst. 2008;92:44–52. doi:10.1016/j.chemolab.2007.12.001.

    Article  CAS  Google Scholar 

  80. Rossi DM, Solle D, Hitzmann B, Ayub MAZ. Chemometric modeling and two-dimensional fluorescence analysis of bioprocess with a new strain of Klebsiella pneumoniae to convert residual glycerol into 1,3-propanediol. J Ind Microbiol Biotechnol. 2012;39:701–8. doi:10.1007/s10295-011-1075-8.

    Article  CAS  Google Scholar 

  81. Haack MB, Lantz AE, Mortensen PP, Olsson L. Chemometric analysis of in-line multi-wavelength fluorescence measurements obtained during cultivations with a lipase producing Aspergillus oryzae strain. Biotechnol Bioeng. 2007;96:904–13. doi:10.1002/bit.21170.

    Article  CAS  Google Scholar 

  82. Eliasson Lantz A, Jørgensen P, Poulsen E, Lindemann C, Olsson L. Determination of cell mass and polymyxin using multi-wavelength fluorescence. J Biotechnol. 2006;121:544–54. doi:10.1016/j.jbiotec.2005.08.007.

    Article  CAS  Google Scholar 

  83. Jain G, Jayaraman G, Kökpinar Ö, Rinas U, Hitzmann B. On-line monitoring of recombinant bacterial cultures using multi-wavelength fluorescence spectroscopy. Biochem Eng J. 2011;58–59:133–9. doi:10.1016/j.bej.2011.09.005.

    Article  CAS  Google Scholar 

  84. Ödman P, Johansen CL, Olsson L, Gernaey KV, Lantz AE. On-line estimation of biomass, glucose and ethanol in Saccharomyces cerevisiae cultivations using in-situ multi-wavelength fluorescence and software sensors. J Biotechnol. 2009;144:102–12. doi:10.1016/j.jbiotec.2009.08.018.

    Article  CAS  Google Scholar 

  85. Gahlawat G, Srivastava AK. Use of NAD(P)H fluorescence measurement for on-line monitoring of metabolic state of Azohydromonas australica in poly(3-hydroxybutyrate) production. Appl Biochem Biotechnol. 2013;169:821–31. doi:10.1007/s12010-012-0040-y.

    Article  CAS  Google Scholar 

  86. Ganzlin M, Marose S, Lu X, Hitzmann B, Scheper T, Rinas U. In situ multi-wavelength fluorescence spectroscopy as effective tool to simultaneously monitor spore germination, metabolic activity and quantitative protein production in recombinant Aspergillus niger fed-batch cultures. J Biotechnol. 2007;132:461–8. doi:10.1016/j.jbiotec.2007.08.032.

    Article  CAS  Google Scholar 

  87. Rhee JI, Kang TH. On-line process monitoring and chemometric modeling with 2D fluorescence spectra obtained in recombinant E. coli fermentations. Process Biochem. 2007;42:1124–34. doi:10.1016/j.procbio.2007.05.007.

    Article  CAS  Google Scholar 

  88. Johansson L, Lidén G. A study of long-term effects on plasmid-containing Escherichia coli in carbon-limited chemostat using 2D-fluorescence spectrofluorimetry. Biotechnol Prog. 2006;22:1132–9. doi:10.1021/bp060061m.

    Article  CAS  Google Scholar 

  89. Teixeira AP, Duarte TM, Carrondo MJT, Alves PM. Synchronous fluorescence spectroscopy as a novel tool to enable PAT applications in bioprocesses. Biotechnol Bioeng. 2011;108:1852–61. doi:10.1002/bit.23131.

    Article  CAS  Google Scholar 

  90. Bonk S, Sandor M, Rüdinger F, Tscheschke B, Prediger A, Babitzky A, et al. In-situ microscopy and 2D fluorescence spectroscopy as online methods for monitoring CHO cells during cultivation. BMC Proc. 2011;5:P76. doi:10.1186/1753-6561-5-S8-P76.

    Article  Google Scholar 

  91. Kensy F, Zang E, Faulhammer C, Tan R-K, Büchs J. Validation of a high-throughput fermentation system based on online monitoring of biomass and fluorescence in continuously shaken microtiter plates. Microb Cell Fact. 2009;8:31. doi:10.1186/1475-2859-8-31.

    Article  CAS  Google Scholar 

  92. Surribas A, Amigo JM, Coello J, Montesinos JL, Valero F, Maspoch S. Parallel factor analysis combined with PLS regression applied to the on-line monitoring of Pichia pastoris cultures. Anal Bioanal Chem. 2006;385:1281–8. doi:10.1007/s00216-006-0355-z.

    Article  CAS  Google Scholar 

  93. Ju LK, Chen F, Xia Q. Monitoring microaerobic denitrification of Pseudomonas aeruginosa by online NAD(P)H fluorescence. J Ind Microbiol Biotechnol. 2005;32:622–8. doi:10.1007/s10295-005-0035-6.

    Article  CAS  Google Scholar 

  94. Rathore AS, Kapoor G. Application of process analytical technology for downstream purification of biotherapeutics. J Chem Technol Biotechnol. 2015;90:228–36. doi:10.1002/jctb.4447.

    Article  CAS  Google Scholar 

  95. Das RS, Agrawal YK. Raman spectroscopy: recent advancements, techniques and applications. Vib Spectrosc. 2011;57:163–76. doi:10.1016/j.vibspec.2011.08.003.

    Article  CAS  Google Scholar 

  96. Iversen JA, Berg RW, Ahring BK. Quantitative monitoring of yeast fermentation using Raman spectroscopy. Anal Bioanal Chem. 2014;406:4911–9. doi:10.1007/s00216-014-7897-2.

    Article  CAS  Google Scholar 

  97. Ávila TC, Poppi RJ, Lunardi I, Tizei PAG, Pereira GAG. Raman spectroscopy and chemometrics for on-line control of glucose fermentation by Saccharomyces cerevisiae. Biotechnol Prog. 2012;28:1598–604. doi:10.1002/btpr.1615.

    Article  CAS  Google Scholar 

  98. Picard A, Daniel I, Montagnac G, Oger P. In situ monitoring by quantitative Raman spectroscopy of alcoholic fermentation by Saccharomyces cerevisiae under high pressure. Extremophiles. 2007;11:445–52. doi:10.1007/s00792-006-0054-x.

    Article  CAS  Google Scholar 

  99. Paul A, Carl P, Westad F, Voss J-P, Maiwald M. Towards process spectroscopy in complex fermentation samples and mixtures. Chemie Ing Tech. 2016;1–9. doi:10.1002/cite.201500118.

  100. Golabgir A, Herwig C. Combining mechanistic modeling and Raman spectroscopy for real-time monitoring of fed-batch penicillin production. Chemie Ing Tech. 2016;88:746–76. doi:10.1002/cite.201500101.

    Article  CAS  Google Scholar 

  101. Whelan J, Craven S, Glennon B. In situ Raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors. Biotechnol Prog. 2012;28:1355–62. doi:10.1002/btpr.1590.

    Article  CAS  Google Scholar 

  102. Berry B, Dobrowsky T, Timson R, Wiltberger K, Kshirsagar R, Ryll T. Quick generation of Raman spectroscopy based in-process glucose control to influence biopharmaceutical protein product quality during mammalian cell culture. Biotechnol Prog. 2015;30:429–42. doi:10.1002/btpr.2205.

    Google Scholar 

  103. Mehdizadeh H, Lauri D, Karry KM, Moshgbar M, Procopio-Melino R, Drapeau D. Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors. Biotechnol Prog. 2015;31:1004–13. doi:10.1002/btpr.2079.

    Article  CAS  Google Scholar 

  104. Abu-Absi NR, Kenty BM, Cuellar ME, Borys MC, Sakhamuri S, Strachan DJ, et al. Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe. Biotechnol Bioeng. 2011;108:1215–21. doi:10.1002/bit.23023.

    Article  CAS  Google Scholar 

  105. Moretto J, Smelko JP, Cuellar M, Berry B, Doane A, Ryll T, et al. Process Raman spectroscopy for in-line CHO cell culture monitoring. Am Pharm Rev. 2011;14:18–25.

    CAS  Google Scholar 

  106. Current Trends in Raman Spectroscopy for Upstream Bioprocess Monitoring and Control : Capability and Efficiency.

  107. André S, Saint Cristau L, Gaillard S, Devos O, Calvosa E, Duponchel L. In-line and real-time prediction of recombinant antibody titer by in situ Raman spectroscopy. Anal Chim Acta. 2015;892:148–52. doi:10.1016/j.aca.2015.08.050.

    Article  CAS  Google Scholar 

  108. Li B, Ryan PW, Ray BH, Leister KJ, Sirimuthu NMS, Ryder AG. Rapid characterization and quality control of complex cell culture media solutions using Raman spectroscopy and chemometrics. Biotechnol Bioeng. 2010;107:290–301. doi:10.1002/bit.22813.

    Article  CAS  Google Scholar 

  109. Ashton L, Hogwood CEM, Tait AS, Kuligowski J, Smales CM, Bracewell DG, et al. UV resonance Raman spectroscopy: a process analytical tool for host cell DNA and RNA dynamics in mammalian cell lines. J Chem Technol Biotechnol. 2015;90:237–43. doi:10.1002/jctb.4420.

    Article  CAS  Google Scholar 

  110. Craven S, Whelan J, Glennon B. Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller. J Process Control. 2014;24:344–57. doi:10.1016/j.jprocont.2014.02.007.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dörte Solle.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Published in the topical collection Process Analytics in Science and Industry with guest editor Rudolf W. Kessler.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Claßen, J., Aupert, F., Reardon, K.F. et al. Spectroscopic sensors for in-line bioprocess monitoring in research and pharmaceutical industrial application. Anal Bioanal Chem 409, 651–666 (2017). https://doi.org/10.1007/s00216-016-0068-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-016-0068-x

Keywords

Navigation