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dc.contributor.author Broadhurst, David I.
dc.contributor.author Goodacre, Royston
dc.contributor.author Kaderbhai, Naheed Nazly
dc.contributor.author Small, David A.
dc.contributor.author Winson, Michael Kenneth
dc.contributor.author Kell, Douglas B.
dc.contributor.author McGovern, Aoife C.
dc.contributor.author Taylor, Janet
dc.contributor.author Rowland, Jeremy John
dc.date.accessioned 2009-12-08T11:08:09Z
dc.date.available 2009-12-08T11:08:09Z
dc.date.issued 2002
dc.identifier.citation Broadhurst , D I , Goodacre , R , Kaderbhai , N N , Small , D A , Winson , M K , Kell , D B , McGovern , A C , Taylor , J & Rowland , J J 2002 , ' Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production ' Biotechnology and Bioengineering , vol 78 , no. 5 , pp. 527-538 . , 10.1002/bit.10226 en
dc.identifier.issn 0006-3592
dc.identifier.other PURE: 134205
dc.identifier.other dspace: 2160/3780
dc.identifier.uri http://hdl.handle.net/2160/3780
dc.description McGovern, A. C., Broadhurst, D., Taylor, J., Kaderbhai, N., Winson, M. K., Small, D. A., Rowland, J. J., Kell, D. B., Goodacre, R. (2002). Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production   Biotechnology and Bioengineering, 78, (5), 527-538 Sponsorship: Zeneca Pharmaceuticals Science in Finance, Ltd. The Wellcome Trust UK BBSRC EPSRC; Grant Number: 042615/Z/94/Z en
dc.description.abstract Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was en
dc.format.extent 12 en
dc.language.iso eng
dc.relation.ispartof Biotechnology and Bioengineering en
dc.title Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1002/bit.10226
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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