Extraction of chemical information of suspensions using radiative transfer theory to remove multiple scattering effects : application to a model multicomponent system

The effectiveness of a scatter correction approach based on decoupling absorption and scattering effects through the use of the radiative transfer theory to invert a suitable set of measurements is studied by considering a model multicomponent suspension. The method was used in conjunction with partial least-squares regression to build calibration models for estimating the concentration of two types of analytes: an absorbing (nonscattering) species and a particulate (absorbing and scattering) species. The performances of the models built by this approach were compared with those obtained by applying empirical scatter correction approaches to diffuse reflectance, diffuse transmittance, and collimated transmittance measurements. It was found that the method provided appreciable improvement in model performance for the prediction of both types of analytes. The study indicates that, as long as the bulk absorption spectra are accurately extracted, no further empirical preprocessing to remove light scattering effects is required

Extraction of chemical information of suspensions using radiative transfer theory to remove multiple scattering effects : application to a model two-component system

An approach for removing multiple light scattering effects using the radiative transfer theory (RTE) in order to improve the performance of multivariate calibration models is proposed. This approach is then applied to the problem of building calibration models for predicting the concentration of a scattering (particulate) component. Application of this 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 diffuse transmittance (Td) or reflectance (Rd) are used. The validity of the method was also tested experimentally using a two-component (Polystyrene-water) system. While the proposed method led to a model that performed better than that built using Rd, its performance was worse compared to when Td measurements were used. Analysis indicates that this is because the model built using Td benefits from the strong secondary correlation between particle concentration and pathlength travelled by the photons which occurs due to the system containing only two components. On the other hand, the model arising from the proposed methodology uses essentially only the chemical (polystyrene) signal. Thus this approach can be expected to work better in multi-component systems where the pathlength correlation would not exist

Full correction of scattering effects by using the radiative transfer theory for improved quantitative analysis of absorbing species in suspensions

Sample-to-sample photon path length variations that arise due to multiple scattering can be removed by decoupling absorption and scattering effects by using the radiative transfer theory, with a suitable set of measurements. For samples where particles both scatter and absorb light, the extracted bulk absorption spectrum is not completely free from nonlinear particle effects, since it is related to the absorption cross-section of particles that changes nonlinearly with particle size and shape. For the quantitative analysis of absorbing-only (i.e., nonscattering) species present in a matrix that contains a particulate species that absorbs and scatters light, a method to eliminate particle effects completely is proposed here, which utilizes the particle size information contained in the bulk scattering coefficient extracted by using the Mie theory to carry out an additional correction step to remove particle effects from bulk absorption spectra. This should result in spectra that are equivalent to spectra collected with only the liquid species in the mixture. Such an approach has the potential to significantly reduce the number of calibration samples as well as improve calibration performance. The proposed method was tested with both simulated and experimental data from a four-component model system

Estimation of chemical information in scattering media using radiative transfer theory to remove multiple scattering effects

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.EThOS - Electronic Theses Online ServiceMarie Curie FP6 (project INTROSPECT)GBUnited Kingdo

Quantitative spectroscopic analysis of heterogeneous mixtures: the correction of multiplicative effects caused by variations in physical properties of samples

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g. particle size and shape, sample packing and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum, and hence mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical component in the same sample mixture. Based on this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e. near-infrared total diffuse transmittance spectra of four-component suspension samples and near infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology

Quantitative Spectroscopic Analysis of Heterogeneous Mixtures: The Correction of Multiplicative Effects Caused by Variations in Physical Properties of Samples

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g., particle size and shape, sample packing, and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum and, hence, mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical components in the same sample mixture. On the basis of this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e., near-infrared total diffuse transmittance spectra of four-component suspension samples and near-infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology