Modeling of Temperature-Induced Near-Infrared and Low-Field Time-Domain Nuclear Magnetic Resonance Spectral Variation: Chemometric Prediction of Limonene and Water Content in Spray-Dried Delivery Systems

Abstract

The influence of temperature on near-infrared (NIR) and nuclear magnetic resonance (NMR) spectroscopy complicates the industrial applications of both spectroscopic methods. The focus of this study is to analyze and model the effect of temperature variation on NIR spectra and NMR relaxation data. Different multivariate methods were tested for constructing robust prediction models based on NIR and NMR data acquired at various temperatures. Data were acquired on model spray-dried limonene systems at five temperatures in the range from 20 °C to 60 °C and partial least squares (PLS) regression models were computed for limonene and water predictions. The predictive ability of the models computed on the NIR spectra (acquired at various temperatures) improved significantly when data were preprocessed using extended inverted signal correction (EISC). The average PLS regression prediction error was reduced to 0.2%, corresponding to 1.9% and 3.4% of the full range of limonene and water reference values, respectively. The removal of variation induced by temperature prior to calibration, by direct orthogonalization (DO), slightly enhanced the predictive ability of the models based on NMR data. Bilinear PLS models, with implicit inclusion of the temperature, enabled limonene and water predictions by NMR with an error of 0.3% (corresponding to 2.8% and 7.0% of the full range of limonene and water). For NMR, and in contrast to the NIR results, modeling the data using multi-way N-PLS improved the models' performance. N-PLS models, in which temperature was included as an extra variable, enabled more accurate prediction, especially for limonene (prediction error was reduced to 0.2%). Overall, this study proved that it is possible to develop models for limonene and water content prediction based on NIR and NMR data, independent of the measurement temperature.

Near Infrared Spectroscopy—A Unique Window of Opportunities

“…Our primary focus has been on the evaluation and development of spectral pre-processing algorithms 9,10 On the more exotic side, we also have an eye on the potential of adding a new dimension to NIR spectroscopy which will allow the use of multiway chemometric methods and utilise the so-called second order data analytical advantages: unique spectral resolution and only scaling is required for calibration. 13,14 If realised in a general manner, such a development will have the potential to transform NIR spectroscopy dramatically, as it will strongly reduce the demands on calibrations.…”

Application of Support Vector Regression for Simultaneous Modelling of near Infrared Spectra from Multiple Process Steps

“…This problem has been studied extensively, and in particular there has been some focus on temperature-induced nonlinearities in NIR spectra. 8,9,[11][12][13][14][15][16][17][18][19][20][21] Earlier approaches for handling non-linearities involved minimising the effect of non-linearities by preprocessing of data, 14,18 selecting unaffected variables 17 or using locally weighted regression. 22 More recent approaches have addressed the non-linearities directly using non-linear modelling techniques.…”