It is well known that spectral variability in near infrared (NIR) spectroscopy can be attributed to the analyst, sample, sample positioning, instrument configuration and software (in both algorithm formats and structures used as well as in the execution of data pre-treatment and analysis). It is often acknowledged that the single largest factor impacting NIR results is sample presentation. However, what is obvious but not often acknowledged is that there are instrumental and software differences as well. These differences, evident in the quality of the spectra, may impact the chemometrics that are subsequently performed and, possibly, the results obtained from the multivariate statistical models. In order to investigate just what are these sources of variability, and just how much these variations may impact the results of the multivariate models for predicting the identification of pharmaceutical dosage forms, a study has been conducted. To the authors' knowledge, no other systematic study of this kind has been published. In this study, we are interested in learning what variability, if any, the choices for instrument and software have on the development of a NIR method for the identification of pharmaceutical dosage forms. Furthermore, we would like to learn what and how do the choices made early on in the experimental design impact the final quality of the spectra and the resulting multivariate models obtained from these spectra. A study protocol was designed, using a common data set consisting of four formulations of Ibuprofen, involving three investigating parties, namely, US FDA, USP and Irvine Pharmaceutical Services and using three NIR instruments, namely (listed in alphabetical order), a Bruker spectrometer, a Büchi spectrometer and a Foss spectrometer. Based on the results and despite differences in instrument configuration [dispersive or Fourier Transform (FT)], number of spectral data points, principal components analysis (PCA) or factorisation algorithms, and validation modelling approach, exact and accurate spectroscopic results can be achieved using NIR spectroscopy for discriminate analysis. More importantly, this study shows that the same NIR method spectral range and pre-treatment parameters can be used, and that nearly the same multivariate models can be obtained, despite instrumental and software differences, to accurately predict the identity of pharmaceutical dosage forms.
Lasso Modeling as an Alternative to PCA Based Multivariate Models to System with Heavy Sparsity: “Biodiesel Quality by Nir Spectroscopy”
