Details of Grant 

EPSRC Reference:	GR/R28782/02
Title:	The Development of Deep UV Resonance Raman Spectroscopy and Evolvable Machine Learning For Biotechnology
Principal Investigator:	Goodacre, Professor R
Other Investigators:	Kaderbhai, Dr M Kell, Professor DB
Researcher Co-Investigators:
Project Partners:
Department:	Chemistry
Organisation:	University of Manchester, The
Scheme:	Standard Research (Pre-FEC)
Starts:	01 February 2003	Ends:	30 November 2004	Value (£):	103,907
EPSRC Research Topic Classifications:	Bioprocess Engineering
EPSRC Industrial Sector Classifications:	Manufacturing Pharmaceuticals and Biotechnology
Related Grants:
Panel History:
Summary on Grant Application Form
Within the biotechnology process environment the use of dispersive Raman spectroscopy for non-invasive monitoring has been reported. However, even with excitation in the far red the relatively week Raman signal is often swamped by high fluorescence and so dispersive Raman spectroscopy has been considered unattractive. By contrast, in UV resonance Raman (UVRR) spectroscopy the excitation is in resonance with the electronic transition and yields Raman scattering that is resonance enhanced and typically 1000-10000x higher than the dispersive Raman effect. UVRR has thus emerged as a very promising technique for studying biological materials. However, no attempts to exploit UVRR for the quantitative determination of multiple determinands in fermentor broths have been reported, consequently, the purpose of the present proposal is to develop and exploit UVRR for the on-line, non-invasive measurement of fermentation samples of biotechnological interest. UVRR will give quantitative information about the total biochemical composition of the fermentation samples, and the extraction of the relevant quantitative information will involve the use of advanced chemometric techniques. We have recently discovered that a variety of novel evolutionary computational-based methods, including genetic algorithms and genetic programming can be used to produce models which allow the deconvolution of hyperspectral data in chemical terms. We will therefore use these methods to probe the Raman spectra and so elucidate which bands contribute most to the models formed, thereby allowing chemical interpretation of the industrial fermentations.
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Organisation Website:	http://www.man.ac.uk