Future Aspects of Bioprocess Monitoring

  • Thomas Becker
  • Bernd Hitzmann
  • K. Muffler
  • Ralf Pörtner
  • Kenneth F. Reardon
  • Frank Stahl
  • Roland Ulber
Part of the Advances in Biochemical Engineering/Biotechnology book series (ABE, volume 105)


Nature has the impressive ability to efficiently and precisely control biological processes by applying highly evolved principles and using minimal space and relatively simple building blocks. The challenge is to transfer these principles into technically applicable and precise analytical systems that can be used for many applications. This article summarizes some of the new approaches in sensor technology and control strategies for different bioprocesses such as fermentations, biotransformations, and downstream processes. It focuses on bio- and chemosensors, optical sensors, DNA and protein chip technology, software sensors, and modern aspects of data evaluation for improved process monitoring and control.

Biosensors Microarray technologies Process control Process monitoring Software sensors 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Monk DJ, Walt DR (2004) Optical fiber-based biosensors. Anal Bioanal Chem 379(7–8):931–945 Google Scholar
  2. 2.
    Mehrvar M, Abdi M (2004) Recent developments, characteristics, and potential applications of electrochemical biosensors. Anal Sci 20(8):1113–1126 Google Scholar
  3. 3.
    Nakamura H, Karube I (2003) Current research activity in biosensors. Anal Bioanal Chem 377(3):446–468 Google Scholar
  4. 4.
    Scheper TH, Hilmer JM, Lammers F, Muller C, Reinecke M (1996) Biosensors in bioprocess monitoring. J Chromatography A 725(1):3–12 Google Scholar
  5. 5.
    Wolfbeis OS (2004) Fiber-optic chemical sensors and biosensors. Anal Chem 76(12):3269–3283 Google Scholar
  6. 6.
    Kandimalla VB, Ju HX (2004) New horizons with a multidimensional tool for applications in analytical chemistry – aptamer. Anal Lett 37(11):2215–2233 Google Scholar
  7. 7.
    Kirby R, Cho EJ, Gehrke B, Bayer T, Park YS, Neikirk DP, McDevitt JT, Ellington AD (2004) Aptamer-based sensor arrays for the detection and quantitation of proteins. Anal Chem 76(14):4066–4075 Google Scholar
  8. 8.
    Luzi E, Minunni M, Tombelli S, Mascini M (2003) New trends in affinity sensing: aptamers for ligand binding. Trac-Trends Anal Chem 22(11):810–818 Google Scholar
  9. 9.
    O'Connell PJ, Guilbault GG (2001) Future trends in biosensor research. Anal Lett 34(7):1063–1078 Google Scholar
  10. 10.
    O'Sullivan CK (2002) Aptasensors – the future of biosensing. Anal Bioanal Chem 372(1):44–48 Google Scholar
  11. 11.
    Rimmele M (2003) Nucleic acid aptamers as tools and drugs: recent developments. Chem Bio Chem 4(10):963–971 Google Scholar
  12. 12.
    Haseley SR (2002) Carbohydrate recognition: a nascent technology for the detection of bioanalytes. Analytica Chimica Acta 457(1):39–45 Google Scholar
  13. 13.
    Critchley P, Clarkson GJ (2003) Carbohydrate–protein interactions at interfaces: comparison of the binding of Ricinus communis lectin to two series of synthetic glycolipids using surface plasmon resonance studies. Org Biomol Chem 1(23):4148–4159 Google Scholar
  14. 14.
    Duverger E, Frison N, Roche AC, Monsigny M (2003) Carbohydrate-lectin interactions assessed by surface plasmon resonance. Biochimie 85(1–2):167–179 Google Scholar
  15. 15.
    Svedhem S, Ohberg L, Borrelli S, Valiokas R, Andersson M, Oscarson S, Svensson SCT, Liedberg B, Konradsson P (2002) Synthesis and self-assembly of globotriose derivatives: A model system for studies of carbohydrate-protein interactions. Langmuir 18(7):2848–2858 Google Scholar
  16. 16.
    Sota H, Lee RT, Lee YC, Shinohara Y (2003) Quantitative lectin-carbohydrate interaction analysis on solid-phase surfaces using biosensor based on surface plasmon resonance. In: Lee YC, Lee RT (eds) Methods in enzymology, vol 362: Recognition of carbohydrates in biological systems. Part A: General procedures. Elsevier, Amsterdam, pp 330–340 Google Scholar
  17. 17.
    Liljeblad M, Lundblad A, Padhlsson P (2002) Analysis of glycoproteins in cell culture supernatants using a lectin immunosensor technique. Biosens Bioelectron 17(10):883–891 Google Scholar
  18. 18.
    Cao W, Cudney HH, Waser R (1999) Smart materials and structures. Proc Natl Acad Sci USA 96:8330–8331 Google Scholar
  19. 19.
    van der Linden HJ, Herber S, Olthuis W, Bergveld P (2003) Stimulus-sensitive hydrogels and their applications in chemical (micro)analysis. Analyst 128(4):325–331 Google Scholar
  20. 20.
    Kazanci M (2003) A review of polymeric smart materials for biomedical applications. Mater Technol 18(2):87–93 Google Scholar
  21. 21.
    Jeong B, Gutowska A (2002) Lessons from nature: stimuli-responsive polymers and their biomedical applications. Trends Biotechnol 20(7):305–311 Google Scholar
  22. 22.
    Roy I, Gupta MN (2003) Smart polymeric materials: Emerging biochemical applications. Chem Biol 10(12):1161–1171 Google Scholar
  23. 23.
    Zhang YF, Ji HF, Snow D, Sterling R, Brown GM (2004) A pH sensor based on a microcantilever coated with intelligent hydrogel. Instrument Sci Technol 32(4):361–369 Google Scholar
  24. 24.
    Herber S, Olthuis W, Bergveld P, van den Berg A (2004) Exploitation of a pH-sensitive hydrogel disk for CO2 detection. Sens Actuators B-Chem 103(1–2):284–289 Google Scholar
  25. 25.
    Cai QY, Zeng KF, Ruan CM, Desai TA, Grimes CA (2004) A wireless, remote query glucose biosensor based on a pH-sensitive polymer. Anal Chem 76(14):4038–4043 Google Scholar
  26. 26.
    Lee MC, Kabilan S, Hussain A, Yang XP, Blyth J, Lowe CR (2004) Glucose-sensitive holographic sensors for monitoring bacterial growth. Anal Chem 76(19):5748–5755 Google Scholar
  27. 27.
    Chatzandroulis S, Tegou E, Goustouridis D, Polymenakos S, Tsoukalas D (2004) Fabrication of chemical sensors based on Si/polymer bimorphs. Microelectron Eng 73–74:847–851 Google Scholar
  28. 28.
    Feller JF, Langevin D, Marais S (2004) Influence of processing conditions on sensitivity of conductive polymer composites to organic solvent vapours. Synth Metals 144(1):81–88 Google Scholar
  29. 29.
    Kataky R, Morgan E (2003) Potential of enzyme mimics in biomimetic sensors: a modified-cyclodextrin as a dehydrogenase enzyme mimic. Biosens Bioelectron 18(11):1407–1417 Google Scholar
  30. 30.
    Sotomayor MD, Tanaka AA, Kubota LT (2003) Tris (2,2′-bipyridil) copper (II) chloride complex: a biomimetic tyrosinase catalyst in the amperometric sensor construction. Electrochim Acta 48(7):855–865 Google Scholar
  31. 31.
    Gupta G, Lowe CR (2004) An artificial receptor for glycoproteins. J Mol Recog 17(3):218–235 Google Scholar
  32. 32.
    Piletsky SA, Alcock S, Turner APF (2001) Molecular imprinting: at the edge of the third millennium. Trends Biotechnol 19(1):9–12 Google Scholar
  33. 33.
    He YH, Gao ZX, Chao FH (2004) The progress of the study on molecular imprinting-based biomimetic sensors. Chinese J Anal Chem 32(10):1407–1412 Google Scholar
  34. 34.
    Kindschy LM, Alocilja EC (2004) A review of molecularly imprinted polymers for biosensor development for food and agricultural applications. Trans Asae 47(4):1375–1382 Google Scholar
  35. 35.
    Zimmerman SC, Lemcoff NG (2004) Synthetic hosts via molecular imprinting – are universal synthetic antibodies realistically possible? Chem Commun 2004(1):5–14 Google Scholar
  36. 36.
    Ye L, Mosbach K (2001) The technique of molecular imprinting – Principle, state of the art, and future aspects. J Incl Phenom Macrocyc Chem 41(1–4):107–113 Google Scholar
  37. 37.
    Warsinke A, Lettau K, Werner D, Micheel B, Kwak YK (2003) Biornimetic binders and catalysts for sensorics. Tech Messen 70(12):585–588 Google Scholar
  38. 38.
    Yamazaki T, Ohta S, Yanai Y, Sode K (2003) Molecular imprinting catalyst based artificial enzyme sensor for fructosylamines. Anal Lett 36(1):75–89 Google Scholar
  39. 39.
    Hirayama K, Sakai Y, Kameoka K, Noda K, Naganawa R (2002) Preparation of a sensor device with specific recognition sites for acetaldehyde by molecular imprinting technique. Sens Actuators B-Chem 86(1):20–25 Google Scholar
  40. 40.
    Huan SY, Shen GL, Yu RQ (2004) Enantioselective recognition of amino acid by differential pulse voltammetry in molecularly imprinted monolayers assembled on Au electrodes. Electroanalysis 16(12):1019–1023 Google Scholar
  41. 41.
    Reddy PS, Kobayashi T, Abe M, Fujii N (2002) Molecular imprinted Nylon-6 as a recognition material of amino acids. Eur Poly J 38(3):521–529 Google Scholar
  42. 42.
    Stanley S, Percival CJ, Morel T, Braithwaite A, Newton MI, McHale G, Hayes W (2003) Enantioselective detection of l-serine. Sens Actuators B-Chem 89(1–2):103–106 Google Scholar
  43. 43.
    Dickert FL, Achatz P, Halikias K (2001) Double molecular imprinting – a new sensor concept for improving selectivity in the of polycyclic aromatic hydrocarbons (PAHs) in water. Fresenius J Anal Chem 371(1):11–15 Google Scholar
  44. 44.
    Bachinger T, Mandenius CF (2001) Physiologically motivated monitoring of fermentation processes by means of an electronic nose. Chem Eng Technol 24(7):33–42 Google Scholar
  45. 45.
    Dickinson TA, White J, Kauer JS, Walt DR (1998) Current trends in artificial-nose technology. Trends Biotechnol 16(6):250–258 Google Scholar
  46. 46.
    Deisingh AK, Stone DC, Thompson M (2004) Applications of electronic noses and tongues in food analysis. Int J Food Sci Technol 39(6):587–560 Google Scholar
  47. 47.
    Dickinson TA, Michael KL, Kauer JS, Walt DR (1999) Convergent, self-encoded bead sensor arrays in the design of an artificial nose. Anal Chem 71(11):2192–2198 Google Scholar
  48. 48.
    Esbensen K, Kirsanov D, Legin A, Rudnitskaya A, Mortensen J, Pedersen J, Vognsen L, Makarychev-Mikhailov S, Vlasov Y (2004) Fermentation monitoring using multisensor systems: feasibility study of the electronic tongue. Anal Bioanal Chem 378(2):391–395 Google Scholar
  49. 49.
    Liden H, Mandenius CF, Gorton L, Meinander NQ, Lundstrom I, Winquist F (1998) On-line monitoring of a cultivation using an electronic nose. Anal Chim Acta 361(3):223–231 Google Scholar
  50. 50.
    Namdev PK, Alroy Y, Singh V (1998) Sniffing out trouble: Use of an electronic nose in bioprocesses. Biotechnol Prog 14(1):75–78 Google Scholar
  51. 51.
    Santos JP, Arroyo T, Aleixandre M, Lozano J, Sayago I, Garcia M, Fernandez MJ, Ares L, Gutierrez J, Cabellos JM, Gil M, Horrillo MC (2004) A comparative study of sensor array and GC-MS: application to Madrid wines characterization. Sens Actuators B-Chem 102(2):299–307 Google Scholar
  52. 52.
    Bachinger T, Martensson P, Mandenius CF (1998) Estimation of biomass and specific growth rate in a recombinant Escherichia coli batch cultivation process using a chemical multisensor array. J Biotechnol 60(1–2):55–66 Google Scholar
  53. 53.
    Bachinger T, Riese U, Eriksson RK, Mandenius CF (2002) Gas sensor arrays for early detection of infection in mammalian cell culture. Biosens Bioelectron 17(5):395–403 Google Scholar
  54. 54.
    Colton RJ (2004) Nanoscale measurements and manipulation. J Vacuum Sci Technol B 22(4):1609–1635 Google Scholar
  55. 55.
    Sarikaya M, Tamerler C, Jen AKY, Schulten K, Baneyx F (2003) Molecular biomimetics: nanotechnology through biology. Nature Mater 2(9):577–585 Google Scholar
  56. 56.
    Chovan T, Guttman A (2002) Microfabricated devices in biotechnology and biochemical processing. Trends Biotechnol 20(3):116–122 Google Scholar
  57. 57.
    Buot FA (1993) Mesoscopic Physics and Nanoelectronics – Nanoscience and Nanotechnology. Physics Reports 234(2–3):73–174 Google Scholar
  58. 58.
    Demidov VV (2004) Nanobiosensors and molecular diagnostics: a promising partnership. Expert Rev Mol Diag 4(3):267–268 Google Scholar
  59. 59.
    Haes AJ, Van Duyne RP (2003) Nanosensors enable portable detectors for environmental and medical applications. Laser Focus World 39(5):153–156 Google Scholar
  60. 60.
    Dai YQ, Shiu KK (2004) Glucose biosensor based on multi-walled carbon nanotube modified glassy carbon electrode. Electroanalysis 16(20):1697–1703 Google Scholar
  61. 61.
    Guiseppi-Elie A, Lei CH, Baughman RH (2002) Direct electron transfer of glucose oxidase on carbon nanotubes. Nanotechnology 13(5):559–564 Google Scholar
  62. 62.
    Zhang FF, Wan Q, Li CX, Wang XL, Zhu ZQ, Xiang YZ, Jin LT, Yamamoto K (2004) Simultaneous assay of glucose, lactate, l-glutamate and hypoxanthine levels in a rat striatum using enzyme electrodes based on neutral red-doped silica nanoparticles. Anal Bioanal Chem 380(4):637–642 Google Scholar
  63. 63.
    Tschmelak J, Proll G, Gauglitz G (2005) Optical biosensor for pharmaceuticals, antibiotics, hormones, endocrine disrupting chemicals and pesticides in water: Assay optimization process for estrone as example. Talanta 65(2):313–323 Google Scholar
  64. 64.
    Noui L, Hill J, Keay PJ, Wang RY, Smith T, Yeung K, Habib G, Hoare M (2002) Development of a high resolution UV spectrophotometer for at-line monitoring of bioprocesses. Chem Eng Proc 41(2):107–114 Google Scholar
  65. 65.
    Pons MN, Le Bonte S, Potier O (2004) Spectral analysis and fingerprinting for biomedia characterisation. J Biotechnol 113(1–3):211–230 Google Scholar
  66. 66.
    Nomikos P, Mac Gregor JF (1994) Monitoring batch processes using multiway principal component analysis. AIChE 40(8):1361–1375 Google Scholar
  67. 67.
    Nomikos P, Mac Gregor JF (1995) Multivariate SPC charts for monitoring batch processes. Technometrics 37(1):41–59 Google Scholar
  68. 68.
    Jorgensen P, Pedersen JG, Jensen EP, Esbensen KH (2004) On-line batch fermentation process monitoring (NIR) – introducing biological process time. J Chemomet 18(2):81–91 Google Scholar
  69. 69.
    Sorensen LK (2004) Prediction of fermentation parameters in grass and corn silage by near infrared spectroscopy J Dairy Sci 87(11):3826–3835 Google Scholar
  70. 70.
    Garrido-Vidal D, Esteban-Diez I, Perez-del-Notario N, Gonzalez-Saiz JM, Pizarro C (2004) On-line monitoring of kinetic and sensory parameters in acetic fermentation by near infrared spectroscop J Near Infrared Spectry 12(1):15–27 Google Scholar
  71. 71.
    Sivakesava S, Irudayaraj J, Ali D (2001) Simultaneous determination of multiple components in lactic acid fermentation using FT-MIR, NIR, and FT-Raman spectroscopic techniques. Proc Biochem 37(4):371–378 Google Scholar
  72. 72.
    Cozzolino D, Kwiatkowski MJ, Parker M, Cynkar WU, Dambergs RG, Gishen M, Herderich MJ (2004) Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta 513(1):73–80 Google Scholar
  73. 73.
    Navratil M, Cimander C, Mandenius CF (2004) On-line multisensor monitoring of yogurt and Filmjolk fermentations on production scale. J Agric Food Chem 52(3):415–420 Google Scholar
  74. 74.
    Tosi S, Rossi M, Tamburini E, Vaccari G, Amaretti A, Matteuzzi D (2003) Assessment of in-line near-infrared spectroscopy for continuous monitoring of fermentation processes. Biotechnol Prog 19(6):1816–1821 Google Scholar
  75. 75.
    Suehara KI, Yano T (2004) Bioprocess monitoring using near-wrared spectroscopy. In: Kobayashi T (ed) Recent progress of biochemical and biomedical engineering in Japan I. Advances in biochemical engineering/biotechnology, vol 90. Springer, Berlin Heidelberg New York, pp 173–198 Google Scholar
  76. 76.
    Acha V, Meurens M, Naveau H, Agathos SN (2000) ATR-FTIR sensor development for continuous on-line monitoring of chlorinated aliphatic hydrocarbons in a fixed-bed bioreactor. Biotechnol Bioeng 68(5):473–487 Google Scholar
  77. 77.
    Kansiz M, Gapes JR, McNaughton D, Lendl B, Schuster KC (2001) Mid-infrared spectroscopy coupled to sequential injection analysis for the on-line monitoring of the acetone-butanol fermentation process. Anal Chim Acta 438(1–2):175–186 Google Scholar
  78. 78.
    McGovern AC, Broadhurst D, Taylor J, Kaderbhai N, Winson MK, Small DA, Rowland JJ, Kell DB, Goodacre R (2002) Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production. Biotechnol Bioeng 78(5):527–538 Google Scholar
  79. 79.
    Vankeirsbilck T, Vercauteren A, Baeyens W, Van der Weken G, Verpoort F, Vergote G, Remon JP (2002) Applications of Raman spectroscopy in pharmaceutical analysis. Trac-Trends Anal Chem 21(12):869–877 Google Scholar
  80. 80.
    Shaw AD, Kaderbhai N, Jones A, Woodward AM, Goodacre R, Rowland JJ, Kell DB (1999) Noninvasive, on-line monitoring of the biotransformation by yeast of glucose to ethanol using dispersive Raman spectroscopy and chemometrics. Appl Spectrosc 53(11):1419–1428 Google Scholar
  81. 81.
    Lee HLT, Boccazzi P, Gorret N, Ram RJ, Sinskey AJ (2004) In situ bioprocess monitoring of Escherichia coli bioreactions using Raman spectroscopy. Vibrat Spectrosc 35(1–2):131–137 Google Scholar
  82. 82.
    Bell SEJ, Bourguignon ESO, Grady AO, Villaumie J, Dennis AC (2002) Extracting Raman spectra from highly fluorescent samples with Scissors (SSRS, shifted-substracted Raman spectroscopy). Spectrosc Eur 14(6):17–20 Google Scholar
  83. 83.
    Ulber R, Protsch C, Sölle D, Hitzmann B, Willke B, Faurie R, Scheper T (2001) Use of bioanalytical systems for the improvement of industrial tryptophan production. Eng Life Sci 1(1):15–17 Google Scholar
  84. 84.
    Ulber R, Faurie R, Sosnitza P, Fischer L, Stärk E, Harbeck C, Scheper T (2000) Monitoring and control of industrial downstream processing of sugar beet molasses. J Chromatogr A 882(1–2):329–334 Google Scholar
  85. 85.
    Zhang XC (2002) Terahertz wave imaging: horizons and hurdles. Phys Med Biol 47(21):3667–3677 Google Scholar
  86. 86.
    Chen JY, Markelz AG (2003) Towards biosensing with terahertz spectroscopy: Ligand binding effects. Biophys J 84(2):156A–156A Google Scholar
  87. 87.
    Knab J, Chen JY, Markelz A (2004) Protein-ligand binding detected by terahertz spectroscopy. Biophys J 86(1):84A-84A Google Scholar
  88. 88.
    Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative Monitoring of gene expression patterns with complementary DNA Mikroarray. Science 270:467 Google Scholar
  89. 89.
    Southern EM, Maskos U, Elder JK (1992) Analyzing and comparing nucleic acid sequences by hybridization to arrays of oligonucleotides: Evaluation using experimental models. Genomics 13:1008 Google Scholar
  90. 90.
    DeRisi J, Iyer VR, Brown PO (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278:680–686 Google Scholar
  91. 91.
    Cargill M, Altshule D, Ireland J, Sklar P, Ardle K, Patil N, Shaw N, Lane CR, Lim EP, Kalyanaraman N, Nemesh J, Ziaugra L, Friedland L, Rolfe A, Warrington J, Lipshutz R, Daley GO, Lander ES (1999) Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet 22:231–238 Google Scholar
  92. 92.
    Halushka MK, Fan IB, Bently K, Hsie L, Shen N, Weder A, Cooper R, Lipshutz R, Chakarvarti A (1999) Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat Genet 22:239–247 Google Scholar
  93. 93.
    Behr MA, Wilson MA, Gill WP, Salamon H, Schoolnik GK, Rane S, Small PM (1999) Comparative genomics of BCG vaccines by whole-genome DNA microarray. Science 284:1520–1523 Google Scholar
  94. 94.
    Walter G, Bussow K, Cahill D, Lueking A, Lehrach H (2000) Protein arrays for gene expression and molecular interaction screening. Curr Opin Microbiol 3:298–302 Google Scholar
  95. 95.
    Wang DG, Fan JB, Siao C 1., Berno A, Young P, Sapolsky R, Ghandour G, Perkins N, Winchester E, Spencer J (1998) Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280:1077–1082 Google Scholar
  96. 96.
    Sebat JL, Colwell FS, Crawford RL (2003) Metagenomic profiling: microarray analysis of an environmental genomic library. Appl Envir Microbiol 69:4927–4934 57 Google Scholar
  97. 97.
    Richmond CS, Glasner JD, Mau R, Jin H, Blatther FR (1999). Genome-wide expression profiling in Escherichia coli K-12. Nucleic Acid Res 27:3821–3835 Google Scholar
  98. 98.
    Oh MK, Liao JC (2000) Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnol Prog 16:278–286 Google Scholar
  99. 99.
    Woychik RP, Klebig ML, Justice MJ, Magnuson TR, Avner ED, Avrer ED (1998) Functional genomics in the post-genome era. Mutat Res 400:3 Google Scholar
  100. 100.
    Khan J, Bittner M, Chen Y, Meltzer PS, Trent JM (1999) DNA microarray technology: the anticipated impact on the study of human disease. Biochim Biophys Acta 1423:M17–M28 Google Scholar
  101. 101.
    Duggan DJ, Bittner M Chen Y Metzler P, Trent JM (1999) Expression profiling using cDNA microarrays. Nat Genet 21(Suppl.):10 Google Scholar
  102. 102.
    Brown PO and Botstein D (1999) Exploring the new worlds of the genome with DNA microarrays. Nat Genet 21(Suppl.):33 Google Scholar
  103. 103.
    Vukmirovic OG, Tilghman SM (2000) Exploring genome space. Nature 405:820 Google Scholar
  104. 104.
    Ermolaeva O, Rastogi M, Pruitt KD, Schuler GD, Bittner ML, Chen Y, Simon R, Meltzer P, Trent JM, Boguski MS (1998) Data management and analysis for gene expression arrays. Nat Genet 20:19 Google Scholar
  105. 105.
    Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FCP, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001): Minimum information about a Mikroarray experiment (MIAME) – toward standards for Mikroarray data. Nat Genet 29:365 Google Scholar
  106. 106.
    MacBeath G (2002) Protein microarrays and proteomics. Nat Genet 32:526–532 Google Scholar
  107. 107.
    Templin MF, Stoll D, Schrenk M, Traub PC, Vöhringer CF, Joos TO (2002) Protein microarray technology. Trends Biotechnol 20(4):160–166 Google Scholar
  108. 108.
    Zhu H, Snyder M (2003) Protein chip technology. Curr Opin Chem Biol 7:55–63 Google Scholar
  109. 109.
    Lee SY, Lee SJ, Jung H-T (2003) Protein microarrays and chips. J Ind Eng Chem 9(1):9–15 Google Scholar
  110. 110.
    Kusnezow W, Hoheisel JD (2002) Antibody microarrays: promises and problems. Bio Techniques 33:14–23, Google Scholar
  111. 111.
    Kusnezow W, Jacob A, Walijew A, Diehl F, Hoheisel JD (2003) Antibody microarrays: An evaluation of production parameters. Proteomics 3:254–264 Google Scholar
  112. 112.
    Bastin G, Dochain D (eds)(1990) On-line estimation and adaptive control of bioreactors. Elsevier, Amsterdam Google Scholar
  113. 113.
    Cheruy A (1997) Software sensors in bioprocess engineering. J Biotechnol 52:193–199 Google Scholar
  114. 114.
    Vassileva S, Tzvetkova B, Katranoushkova C, Losseva L (2000) Neuro-fuzzy predicition of uricase procduction. Bioprocess Eng 22:363–367 Google Scholar
  115. 115.
    Liebsch G, Klimant I, Frank B, Holst G, Wolfbeis OS (2000) Luminescence lifetime imaging of oxygen, pH, and carbon dioxide distribution using optical sensors. Appl Spectrosc 54:548–559 Google Scholar
  116. 116.
    Shiraishi F (1994) Apparent kinetic parameters of an immobilized enzyme reaction: What is expected from oversimplification? Enzyme Microb Technol 16:349–350 Google Scholar
  117. 117.
    Jung YK, Hur W (2000) A new method of on-line measurement of buffer capacity and alkali consumption rate of a fermentation process. J Biosci Bioeng 90:580–582 CrossRefGoogle Scholar
  118. 118.
    Bernard O, Hadj SZ, Dochain D (2000) Software sensors to monitor the dynamics of microbial communities: Application to anaerobic digestion. Acta Biotheor 48:197–205 Google Scholar
  119. 119.
    Acha V, Meurens M, Naveau H, Dochain D, Bastin G, Agathos SN (1999) Model-based estimation of an anaerobic reductive dechlorination process via an attenuated total reflection-Fourier transform infrared sensor. Water Sci Technol 40:33–40 Google Scholar
  120. 120.
    Yano T, Harata M (1994) Prediction of the concentration of several constituents in a mouse-mouse hybridoma by near infrared spectroscopy. J Ferment Bioeng 77:659–662 Google Scholar
  121. 121.
    Schindler R, Thanh HL, Lendl B, Kellner R (1998) Determination of enzyme kinetics and chemometric evaluation of reaction products by FTIR spectroscopy on the example of beta-fructofuranosidase. Vibrat Spectrosc 16:127–135 Google Scholar
  122. 122.
    Hoyer H (1997) NIR on-line analysis in the food industry. Proc Contr Qual 9:143–152 Google Scholar
  123. 123.
    Downey G (1996) Authentication of food and food ingredients by near infrared spectroscopy. J Near Infrared Spectrosc 4:47–61 Google Scholar
  124. 124.
    Hamrita TK, Wang S (2000) Pattern recognition for modeling and on-line diagnosis of bioprocesses. IEEE Trans Industr Appl 36:1295–1299 Google Scholar
  125. 125.
    Bachmann TT, Leca B, Vilatte F, Marty JL, Fournier D, Schmid R (2000) Improved multianalyte detection of organophosphates and carbamates with disposable multielectrode biosensors using recombinant mutants of Drosophila acetylcholinesterase and artificial neural networks. Biosens Bioelectron 15:193–201 Google Scholar
  126. 126.
    De Mol RM, Woldt WE (2001) Application of fuzzy logic in automated cow status monitoring. J Dairy Sc 84:400–410 CrossRefGoogle Scholar
  127. 127.
    Shi Z, Shimizu K (1992) Neuro-fuzzy control of bioreactor systems with pattern recognition. J Ferment. Bioeng 74:39–45 Google Scholar
  128. 128.
    Stephanopoulos G, Locher G, Duff M (1995) Pattern recognition methods for fermentation database mining. In: Munack A, Schügerl (eds) Reprints of the 6th conference on computer applications in biotechnology, Garmisch-Partenkirchen, Germany. Elsevier, Amsterdam, pp 195–198 Google Scholar
  129. 129.
    Chang SK (2001) Biotechnology: Updates and new developments. Biomed Environ Sci 14:32–39 Google Scholar
  130. 130.
    Ryu DDY, Nam DH (2000) Recent progress in biomolecular engineering. Biotechnol Prog 16:2–16 Google Scholar
  131. 131.
    Edwards JS, Palsson BO (1998) How will bioinformatics influence metabolic engineering? Biotechnol Bioeng 58:162–169 Google Scholar
  132. 132.
    Schuster S, Dandekar T, Fell DA (1999) Detection of elementary flux modes in biochemical networks: A promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 17:53–60 Google Scholar
  133. 133.
    Fellner M, Delgado A, Becker T (2003) Functional neurons in dynamic neural networks for bioprocess modelling. Bioproc Biosys Eng 25:263–270 Google Scholar
  134. 134.
    Zaydan R, Dion M, Boujtita M (2004) Development of a new method, based on a bioreactor coupled with an l-lactate biosensor, toward the determination of a non-specific inhibition of l-lactic acid production during milk fermentation. J Agric Food Chem 52(1):8–14 Google Scholar
  135. 135.
    Ferreira LS, Trierweiler JO, De Souza MB, Folly ROM (2004) A lactose FIA-biosensor system for monitoring and process control. Braz J Chem Eng 21(2):307–315 Google Scholar
  136. 136.
    Arndt M, Hitzmann B (2004) Kalman filter based glucose control at small set points during fed-batch cultivation of Saccharomyces cerevisiae. Biotechnol Prog 20:377–383 Google Scholar
  137. 137.
    Inaba Y, Mizukarni K, Harnada-Sato N, Kobayashi T, Imada C, Watanabe E (2003) Development of a d-alanine sensor for the monitoring of a fermentation using the improved selectivity by the combination of d-amino acid oxidase and pyruvate oxidase. Biosens Bioelectron 19(5):423–431 Google Scholar
  138. 138.
    Bracewell DG, Gill A, Hoare M (2002) An in-line flow injection optical biosensor for real-time bioprocess monitoring. Food Bioprod Proc 80(C2):71–77 Google Scholar
  139. 139.
    Stefan RI, van Staden JF, Mulaudzi LV, Aboul-Enein HY (2002) On-line simultaneous determination of S- and R-perindopril using amperometric biosensors as detectors in flow systems. Anal Chim Acta 467(1–2)189–195 Google Scholar
  140. 140.
    Rocha I, Ferreira EC (2002) On-line simultaneous monitoring of glucose and acetate with FIA during high cell density fermentation of recombinant E-coli. Anal Chim Acta 462(2):293–304 Google Scholar
  141. 141.
    Rhee JI, Yamashita M, Scheper T (2002) Development of xylitol oxidase-based flow injection analysis for monitoring of xylitol concentrations. Anal Chim Acta 456(2):293–301 Google Scholar
  142. 142.
    Klockewitz K, Riechel P, Hagedorn J, Scheper T, Noe W, Howaldt M, Vorlop J (2000) Fast FIA-immunoanalysis systems for the monitoring of downstream processes. Chem Biochem Eng Quart 14(2):43–46 Google Scholar
  143. 143.
    Nandakumar MP, Palsson E, Gustavsson PE, Larsson PO, Mattiasson B (2000) Superporous agarose monoliths as mini-reactors in flow injection systems – On-line monitoring of metabolites and intracellular enzymes in microbial cultivation processes. Bioseparation 9(4):193–202 Google Scholar
  144. 144.
    Schügerl K (1993) Which requirements do flow injection analyzer/biosensor systems have to meet for controlling the bioprocess? J Biotechnol 31:241–256 Google Scholar
  145. 145.
    Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1987) Numerical recipes, the art of scientific computing. Cambridge University Press, Cambridge Google Scholar
  146. 146.
    Hitzmann B, Broxtermann O, Cha Y-L, Sobieh O, Stärk E, Scheper T (2000) The control of glucose concentration during yeast fed-batch cultivation using a fast measurement complemented by an extended Kalman filter. Bioproc Eng 23:337–341 Google Scholar
  147. 147.
    Hitzmann B (1998) Optimierung von FIA-Systemen für die Bioprozesstechnik. Springer, Berlin Heidelberg New York Google Scholar
  148. 148.
    Saurina J, Hernandez-Cassou S (2001) Quantitative determinations in conventional flow injection analysis based on different chemometric calibration strategies: a review. Anal Chim Acta 438(1–2):335–352 Google Scholar
  149. 149.
    Tryzell R, Karlberg B (1998) Comparison of various peak evaluation techniques for limited and extended dynamic ranges in flow injection systems. Anal Chim Acta 364(1–3):97–106 Google Scholar
  150. 150.
    Wu X, Bellgardt KH (1998) Fast on-line data evaluation of flow-injection analysis signals based on parameter estimation by an extended Kalman filter. J Biotechnol 62(1):11–28 Google Scholar
  151. 151.
    Brandt J, Hitzmann B (1994) Knowledge-based fault detection and diagnosis in flow injection analysis. Anal Chim Acta 291:29–40 Google Scholar
  152. 152.
    Hitzmann B, Gomersall R, Brandt J, vanPutten A (1995) An expert system for the supervision of a multichannel flow injection analysis system. ACS Symp Ser 613:133–143 CrossRefGoogle Scholar
  153. 153.
    Alvares-Ribeiro LMBC, Machado AASC (1997) Usefulness of a ruggedness test in the validation of flow injection analysis systems. Anal Chim Acta 355(2–3):195–201 Google Scholar
  154. 154.
    Hitzmann B, Kullick T (1994) Evaluation of pH field effect transistor measurement signals by neural networks. Anal Chim Acta 294:243–249 Google Scholar
  155. 155.
    Hitzmann B, Ritzka A, Ulber R, Schöngarth K, Broxtermann O (1998) Neural networks as a modeling tool for the evaluation and analysis of FIA signals. J Biotechnol 65(1):15–22 Google Scholar
  156. 156.
    Becker TM, Schmidt HL (2000) Data model for the elimination of matrix effects in enzyme-based flow-injection systems. Biotechnol Bioeng 69(4):377–384 Google Scholar
  157. 157.
    Buratti S, Benedetti S, Scampicchio M, Pangerod EC (2004) Characterization and classification of Italian Barbera wines by using an electronic nose and an amperometric electronic tongue. Anal Chim Acta 525(1):133–139 Google Scholar
  158. 158.
    da Costa RS, Santos SRB, Almeida LF, Nascimento ECL, Pontes MJC, Lima RAC, Simoes SS, Araujo MCU (2004) A novel strategy to verification of adulteration in alcoholic beverages based on Schlieren effect measurements and chemometric techniques. Microchem J 78(1):27–33 Google Scholar
  159. 159.
    Tortajada-Genaro LA, Campins-Falco P, Verdu-Andres J, Bosch-Reig F (2001) Multivariate versus univariate calibration for non-linear chemiluminescence data – Application to chromium determination by luminol-hydrogen peroxide reaction. Anal Chim Acta 450(1–2):155–173 Google Scholar
  160. 160.
    Schöngarth K, Hitzmann B (1998) Simultaneous calibration in flow-injection analysis using multiple injection signals evaluated by partial least squares. Anal Chim Acta 363:183–189 Google Scholar
  161. 161.
    vanderPol JJ, Joksch B, Gatgens J, Biselli M, deGooijer CD, Tramper J, Wandrey C (1995) On-line control of an immobilized hybridoma culture with multi-channel flow injection analysis. J Biotechnol 43(3):229–242 Google Scholar
  162. 162.
    Vannecke C, Bloomfield MS, Vander Heyden Y, Massart DL (2002) Development of a generic flow-injection analysis method for compounds with a secondary amine or amide function, using an experimental design approach Part II Selection and evaluation of the chemical reaction parameters. Anal Chim Acta 455(1):117–130 Google Scholar
  163. 163.
    Tovar A, Moreno C, Manuel-Vez MP, Garcia-Vargas M (2002) A simple procedure to improve the analytical performance of flow injection systems. Spectrosc Lett 35(5):715–728 Google Scholar
  164. 164.
    Zhou YY, Yan AX, Xu HP, Wang KT, Chen XG, Hu ZD (2000) Flow injection analysis of fluoride: optimization of experimental conditions and non-linear calibration using artificial neural networks. Analyst 125(12):2376–2380 Google Scholar
  165. 165.
    Vannecke C, Bloomfield MS, Vander Heyden Y, Massart DL (1999) Use of experimental design to optimise a flow injection analysis assay for l-N-monomethylarginine. J Pharmaceut Biomed Anal 21(2):241–255 Google Scholar
  166. 166.
    Wrotnowski C (2000) Cell culture now a drug discovery bottleneck. Gen Eng News 20:15 Google Scholar
  167. 167.
    Glaser V (2001) Current trends and innovations in cell culture. Gen Eng News 21:11 Google Scholar
  168. 168.
    Pörtner R, Schilling A, Lüdemann I, Märkl H (1996) High density fed-batch cultures for hybridoma cells performed with the aid of a kinetic model. Bioproc Eng 15:117–124 Google Scholar
  169. 169.
    Schwabe JO, Pörtner R, Märkl H (1999) Improving an on-line feeding strategy for fed-batch cultures of hybridoma cells by dialysis and nutrient-split-feeding. Bioproc Eng 20:475–484 Google Scholar
  170. 170.
    Garcia CE, Prett DM, Morari M (1989) Model predictive control: theory and practice – a survey. Automat 25:335–348 Google Scholar
  171. 171.
    Munack A (1987) Application of receding horizon adaptive control to an underfloor heating system. IFAC Proc Ser 1986 IFAC Conference on simulation of control systems, pp 263–268 Google Scholar
  172. 172.
    Dreyfuss SE (1962) Some types of optimal control of stochastic systems. SIAM J Con 2:120–134 Google Scholar
  173. 173.
    Hass VC, Schneider R, Munack A (1992) Investigation of mathematical fermentation models of different complexity applied for on-line optimization by the OLFO controller. Proc DECHEMA Biotechnol Conf, vol 5, Part A, pp 329–332 Google Scholar
  174. 174.
    Witte VC (1996) Mathematische Modellierung und adaptive Prozesssteuerung der Kultivierung von Cyatus striatus. Fortschr-Ber VDI 17:144, VDI Google Scholar
  175. 175.
    Schneider R, Hass VC, Munack A (1993) OLFO Controller performance study using mathematical fermentation models of different complexity. Preprints 12th IFAC World Congress, vol 7, pp 435–438 Google Scholar
  176. 176.
    Schneider R (1999) Untersuchung eines adaptiven prädiktiven Regelungsverfahrens zur Optimierung von bioverfahrenstechnischen Prozessen. Fortschr-Ber VDI 8:755, VDI Google Scholar
  177. 177.
    Frahm B, Lane P, Atzert H, Munack A, Hoffmann M, Hass V, Pörtner R (2002) Adaptive, model-based control by the open-loop-feedback-optimal (OLFO) controller for the effective fed-batch cultivation of hybridoma cells. Biotechnol Progr 18:1095–1103 Google Scholar
  178. 178.
    Frahm B, Lane P, Munack A, Pörtner R (2005): Optimierung und Steuerung von Zellkultur-Fed-Batch-Prozessen mittels einer Kollokationsmethode. Chem Ing Tech 77(4):429–435 Google Scholar
  179. 179.
    Kasche V, Gottschlich N, Lindberg A, Niebuhr-Redder C, Schmieding J(1994) Perfusible and non-perfusible supports with monoclonal antibodies for bioaffinity chromatography of Escherichia coli penicillin amidase within its pH stability range. J Chrom 660(1–2):137–145 Google Scholar
  180. 180.
    Hoffmann M, Frahm B, Schwabe JO, Lane P, Pörtner R, Hass VC, Munack A (2000) Modellgestützte Prozessführung für Hybridoma-Kulturen mit Hilfe des Open-Loop-Feedback-Optimal (OLFO) – Algorithmus Proc 10. Heiligenstädter Kolloquium Google Scholar
  181. 181.
    Schneider R, Munack A (1995) Improvements in the on-line parameter identification of bioprocesses. Preprints and postprints 6th international conference computer applications in biotechnology. Pergamon, Oxford, pp 177–182 Google Scholar
  182. 182.
    Stryk O (2002). DIRCOL-2.1 users Guide. TU Darmstadt Google Scholar
  183. 183.
    Pörtner R, Schwabe JO, Frahm B (2004) Evaluation of selected control strategies for fed-batch cultures of a hybridoma cell line. Biotechnol Appl Biochem 40:47–55 Google Scholar
  184. 184.
    Lüdemann I, Pörtner R, Schaefer C, Schick K, Šrámková K, Reher K, Neumaier M, Franék F, Märkl H (1996) Improvement of the culture stability of non-anchorage-dependent animal cells grown in serum-free media through immobilization. Cytotechnol 19:111–124 Google Scholar
  185. 185.
    Frahm B, Lane P, Märkl H, Pörtner R (2003) Improvement of a mammalian cell culture process by adaptive, model-based dialysis fed-batch cultivation and suppression of apoptosis. Bioproc Biosyst Eng 26:1–10 Google Scholar
  186. 186.
    Schwabe JO (2001) Feeding strategies for fed-batch cultures of animal cells. Fortschr-Ber VDI 17:206, VDI Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thomas Becker
    • 1
  • Bernd Hitzmann
    • 2
  • K. Muffler
    • 5
  • Ralf Pörtner
    • 3
  • Kenneth F. Reardon
    • 4
  • Frank Stahl
    • 2
  • Roland Ulber
    • 5
  1. 1.Universität HohenheimProcess AnalysisStuttgartGermany
  2. 2.Institute of Technical ChemistryUniversity of HannoverHannoverGermany
  3. 3.Hamburg University of TechnologyBioprocess and Biochemical EngineeringHamburgGermany
  4. 4.Colorado State UniversityDepartment of Chemical EngineeringColoradoUSA
  5. 5.University of KaiserslauternFaculty of Mechanical and Process EngineeringKaiserslauternGermany

Personalised recommendations