Use this URL to cite or link to this record in EThOS:
Title: Estimation of chemical information in scattering media using radiative transfer theory to remove multiple scattering effects
Author: Steponavicius, Raimundas
ISNI:       0000 0004 2709 9557
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2011
Availability of Full Text:
Access from EThOS:
Access from Institution:
Two approaches for removing multiple light scattering effects using the radiative transfer theory in order to improve the performance of multivariate calibration models have been proposed namely: partial correction of multiple scattering effects and full correction of multiple scattering effects. The first approach is applicable for predicting the concentration of a scattering-absorbing (particulate) component as well as the concentration of an absorbing only species. The second approach is applicable only for estimation of the concentration of an absorbing only species. Application of the first approach to a simulated four component system showed that it will lead to calibration models which perform appreciably better than when empirically scatter corrected measurements of total transmittance or total reflectance are used. The validity of the method was tested experimentally using a two-component (polystyrene-water) and a fourcomponent (polystyrene - ethanol - water - deuterated water) system. The proposed methodology of partial correction showed significantly better performance than the empirically pre-processed direct measurements (total transmittance, total reflectance and collimated transmittance) in all experiments. The results of applying the full correction approach showed that despite all errors the performance of PLS calibration model built on the corrected bulk absorption coefficient was marginally better than the performance of PLS model built on uncorrected bulk absorption coefficient. Finally, the benchmarking analysis revealed that there is still a significant potential for an improvement in the prediction performance in the quantitative analysis of turbid samples.
Supervisor: Not available Sponsor: Marie Curie FP6 (project INTROSPECT)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available