Optical coefficient‐based multivariate calibration on near‐infrared spectroscopy

Abstract

The time and expense of calibration development limit the feasibility of NIR spectroscopy for many industrial applications, with a major portion of the costs being related to creation of a sufficient set of calibration samples. Net analyte signal (NAS) and generalized least squares (GLS) pre-processing have been proposed in the literature as methods to simplify multivariate calibration by reducing the quantity of calibration samples by orthogonalizing or shrinking interference signals. Synthetic calibration has also been reported as a method to combine interference signals with pure component spectra to generate virtual calibration models, thereby reducing the number of real calibration samples required. The goals of this paper were to (1) compare theoretical and practical differences between NAS and GLS pre-processing and (2) explore the potential of simplified NIR calibrations, both empirical and synthetic, constructed using optical coefficient-based signal processing on predicting chemical compositions of pharmaceutical powder mixtures. A reduced calibration dataset including only one pharmaceutical powder mixture composition and pure component spectra was used for both empirical and synthetic calibrations. Absorption and reduced scattering coefficients, obtained from spatially-resolved spectroscopy, were used herein as interference signals in NAS/GLS pre-processing for both calibrations. As a result, NAS and GLS were shown to be equivalent in both theoretical and practical senses. After optical coefficient-based signal processing, simplified calibrations, both empirical and synthetic, were demonstrated to have similar model performance as generic pre-processing methods such as SNV and derivative, while requiring fewer principal components and achieving a lower prediction error.

Pharmaceutical Applications of Separation of Absorption and Scattering in Near-Infrared Spectroscopy (NIRS)

“…Shi and Anderson were the first to explore the potential applications of SRS in the pharmaceutical field. They, along with other researchers, published a series of reports that focused on SRS method development for pharmaceutical samples,5 the enhanced understanding that separated optical coefficients offer to practical uses of NIRS6, 7 and the application of optical coefficients to spectroscopic analyses under practical conditions 5, 8…”

“…Since pure component materials are typically available in the pharmaceutical industry, both µ a and $\mu '_{\rm s} $ of a pure component raw material can be used to represent interfering signals when predicting the chemical concentrations of other components within a powder or tablet mixture. In a recent paper, µ a and $\mu '_{\rm s} $ were integrated into specific chemometric algorithms 8. For example, net analyte signal (NAS) and generalized least squares (GLS) were used to simplify a NIRS multivariate calibration model using only pure component spectra and concentration values from one formulation mixture.…”

“…The two main purposes of the separation are to improve the understanding of individual roles played by absorption and scattering in NIRS, and to utilize the separated absorption and scattering spectra for qualitative and quantitative applications. To date, five techniques to separate NIR absorption and scattering of pharmaceutical related materials have been reported: spatially resolved spectroscopy,5–8 frequency‐resolved spectroscopy,9–21 time‐resolved spectroscopy,22–27 the integrating sphere based reflectance and transmittance measurements,28–31 and measurements of remission, absorption and transmission fractions through layers of material of different thicknesses 32, 33. A review of how these techniques are applied specifically to pharmaceutical application has yet to be reported.…”

NIR and PLS discriminant analysis for predicting the processability of malt during lautering

“…The latter could be achieved for example by empirical or synthetic calibration Table 3 Final configuration of prediction model for industrial processes; size of the used data set, pre-processing of data matrix, number of used components for regression and prediction accuracy Fig. 11 Result of calibration and validation of industrial data; circles indicate correct, crosses false predictions; in total, 20 out of 234 data points were predicted incorrect (11 out of 188 in calibration, 9 out of 46 in validation); graphical presentation of results based on STA analysis (see Shi et al [41]) as well as more intense investigations on the relation to the reference methods presented.…”

Multivariate figures of merit (FOM) investigation on the effect of instrument parameters on a Fourier transform-near infrared spectroscopy (FT-NIRS) based content uniformity method on core tablets

“…Given such a need, a considerable amount of chemometric literature focused on harnessing state-of-the-art algorithms to enhance the signal of the analyte of interest from raw spectra, such as orthogonal signal correction [6,7], net analyte signal [8,9], Wiener filtering [10,11], etc. In comparison, limited efforts have been spent to characterize and optimize the effect of instrument parameters on method performance [12][13][14][15][16][17].…”