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

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

Alternative measurement configurations for extracting bulk optical properties using an integrating sphere setup

The usual approach for estimating bulk optical properties using an integrating sphere measurement setup is by acquiring spectra from three measurement modes namely collimated transmittance (Tc), total transmittance (Td), and total diffuse reflectance (Rd), followed by the inversion of these measurements using the adding–doubling method. At high scattering levels, accurate acquisition of Tc becomes problematic due to the presence of significant amounts of forward-scattered light in this measurement which is supposed to contain only unscattered light. In this paper, we propose and investigate the effectiveness of using alternative sets of integrating sphere measurements that avoid the use of Tc and could potentially increase the upper limit of concentrations of suspensions at which bulk optical property measurements can be obtained in the visible–near-infrared (Vis-NIR) region of the spectrum. We examine the possibility of replacing Tc with one or more reflectance measurements at different sample thicknesses. We also examine the possibility of replacing both the collimated (Tc) and total transmittance (Td) measurements with reflectance measurements taken from different sample thicknesses. The analysis presented here indicates that replacing Tc with a reflectance measurement can reduce the errors in the bulk scattering properties when scattering levels are high. When only multiple reflectance measurements are used, good estimates of the bulk optical properties can be obtained when the absorption levels are low. In addition, we examine whether there is any advantage in using three measurements instead of two to obtain the reduced bulk scattering coefficient and the bulk absorption coefficient. This investigation is made in the context of chemical and biological suspensions which have a much larger range of optical properties compared to those encountered with tissue

Insights into information contained in multiplicative scatter correction parameters and the potential for estimating particle size from these parameters

This paper investigates the nature of information contained in scatter correction parameters. The study had two objectives. The first objective was to examine the nature and extent of information contained in scatter correction parameters. The second objective is to examine whether this information can be effectively extracted by proposing a method to obtain particularly the mean particle diameter from the scatter correction parameters. By using a combination of experimental data and simulated data generated using fundamental light propagation theory, a deeper and more fundamental insight of what information is removed by the multiplicative scatter correction (MSC) method is obtained. It was found that the MSC parameters are strongly influenced not only by particle size but also by particle concentration as well as refractive index of the medium. The possibility of extracting particle size information in addition to particle concentration was considered by proposing a two-step method which was tested using a 2-component and 4-component data set. This method can in principle, be used in conjunction with any scatter correction technique provided that the scatter correction parameters exhibit a systematic dependence with respect to particle size and concentration. It was found that the approach which uses the MSC parameters gave a better estimate of the particle diameter compared to using partial least squares (PLS) regression for the 2-component data. For the 4 component data it was found that PLS regression gave better results but further examination indicated this was due to chance correlations of the particle diameter with the two of the absorbing species in the mixture

Optical properties of apple skin and flesh in the wavelength range from 350 to 2200 nm

Optical measurement of fruit quality is challenging due to the presence of a skin around the fruit flesh and the multiple scattering by the structured tissues. To gain insight in the light-tissue interaction, the optical properties of apple skin and flesh tissue are estimated in the 350-2200nm range for three cultivars. For this purpose, single integrating sphere measurements are combined with inverse adding- doubling. The observed absorption coefficient spectra are dominated by water in the near infrared and by pigments and chlorophyll in the visible region, whose concentrations are much higher in skin tissue. The scattering coefficient spectra show the monotonic decrease with increasing wavelength typical for biological tissues with skin tissue being approximately three times more scattering than flesh tissue. Comparison to the values from time-resolved spectroscopy reported in literature showed comparable profiles for the optical properties, but overestimation of the absorption coefficient values, due to light losses

A comparative investigation of the combined effects of pre-processing, wavelength selection and regression methods on near infrared calibration model performance

Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant role in the calibration while wavelength selection plays a marginal role and the combination of certain pre-processing, wavelength selection, and nonlinear regression methods can achieve superior performance over traditional linear regression-based calibration

Spatially and angularly resolved spectroscopy for in-situ estimation of concentration and particle size in colloidal suspensions

Successful implementation of process analytical technology (PAT) hinges on the ability to make continuous or frequent measurements in-line or at-line of critical product attributes such as composition and particle size, the latter being an important parameter for particulate processes such as suspensions and emulsions. A novel probe-based spatially and angularly-resolved diffuse reflectance measurement (SAR-DRM) system is proposed. This instrument, along with appropriate calibration models, is designed for online monitoring of concentration of chemical species and particle size of the particulate species in process systems involving colloidal suspensions. This measurement system was investigated using polystyrene suspensions of various particle radius and concentration to evaluate its performance in terms of the information obtained from the novel configuration which allows the measurement of a combination of incident light at different angles and collection fibres at different distances from the source fibres. Different strategies of processing and combining the SAR-DRM measurements were considered in terms of the impact on partial least squares (PLS) model performance. The results were compared with those obtained using a bench-top instrument which was used as the reference (off-line) instrument for comparison purposes. The SAR-DRM system showed similar performance to the bench top reference instrument for estimation of particle radius, and outperforms the reference instrument in estimating particle concentration. The investigation shows that the improvement in PLS regression model performance using the SAR-DRM system is related to the extra information captured by the SAR-DRM configuration. The differences in SAR-DRM spectra collected by the different collection fibres from different angular source fibres are the dominant reason for the significant improvement in the model performance. The promising results from this study suggest the potential of the SAR-DRM system as an online monitoring tool for processes involving suspensions

Automated weighted outlier detection technique for multivariate data

In the chemical and petrochemical industries, spectroscopy-based online analysers are becoming common for process monitoring and control applications. A significant challenge in using these analysers as part of process monitoring and control loops is the large amount of personnel time required for calibration and maintenance of models which involve decision inputs such as whether an observation is an outlier, the number of latent variables in a model, type of pre-processing and when a calibration model has to be updated. Since no one measure works well for all applications, supervision by the process data analyst is required which invariably involves some level of subjectivity. In this paper, we focus on the detection of multivariate outliers in a calibration set. We propose a method which combines multiple outlier detection techniques to identify a set of outlying observations without operator input. Apart from the overall methodology, this work introduces several novelties. The system uses partial least squares (PLS) instead of principal component analysis (PCA) which is normally used for detecting multivariate outliers. A simple modification to the Mahalanobis distance was also proposed which appears to be more sensitive to outliers than the conventional Mahalanobis distance. The methodology also introduces the concept of a desirability function to enable automatic decision making based on multiple statistical measures for outlier detection. The methodology is demonstrated using Raman spectroscopy data collected from an industrial distillation process