Scattering Correction by Use of a Priori Information

Summary

In this paper we demonstrate how a limited amount of a priori knowledge about spectral variability can be used in extended multiplicative scattering correction (ENISC) to remove disturbing effects such as light scattering variation in visible and near-infrared spectra prior to data modeling. Two different datasets were studied. In the first dataset, pigment concentrations (astaxanthin) were estimated in a model system with different concentrations of the scattering agent intralipid. Different cases were created by including varying levels of intralipid in the calibration set and then applying the models on sample sets with scattering properties both within and outside the calibration range. Including the most accurate estimate of light scattering in the EMSC model gave root mean square errors of prediction (RMSEP) that were similar to a cross-validated global model including all samples, even for extreme extrapolation with regard to scattering properties. Less accurate estimates gave on average RMSEPs half of what could be achieved using EMSC without any a priori knowledge, suggesting that the method also has potential in cases where the accurate light scattering spectrum is difficult to obtain. In the second dataset carbohydrate concentrations (sucrose, fructose, and glucose) were estimated in orange-juice mixtures where unwanted spectral variation was caused by a change in distance between transmittance fiber-optic probes. This caused two different interfering phenomena due to path length variation and saturation in the detection system. The prediction results for a model based on spectra collected at one specific probe distance treated with EMSC with a correction spectrum were comparable to what could be achieved by a global model including spectra collected at three different distances. The corresponding RMSEPs for models using EMSC with no correction term were in the worst cases 4, 22, and 36 times higher for sucrose, fructose, and glucose, respectively.