Spectral fluctuation dividing for efficient wavenumber selection: Application to estimation of water and drug content in granules using near infrared spectroscopy

Abstract

In process analytical technology (PAT) based on near infrared (NIR) spectroscopy, wavenumber selection is crucial to develop an accurate and robust calibration model. The present research proposes new efficient spectral dividing and wavenumber selection methods to significantly reduce the computational load required by conventional wavenumber selection methods such as interval partial least squares (iPLS). The proposed method, named spectral fluctuation dividing (SFD), divides a whole spectrum into multiple spectral intervals at local minimum points of the spectral fluctuation profile, which consists of the standard deviation of absorbance at each wavenumber in a calibration set. SFD is combined with PLS (SFD–PLS) to select the spectral intervals at which input variables have significant influence on a target response. The usefulness of SFD–PLS was demonstrated through its application to the problems of estimating water and drug content in granules. PLS models based on SFD–PLS achieved higher estimation accuracy than those based on conventional methods including iPLS, PLS-beta, and variable influence on projection (VIP). In addition, SFD–PLS was more than 10 times faster than the conventional variable selection methods including PLS-beta and VIP; in particular, SFD–PLS was more than 25 times faster than iPLS. Consequently, the proposed SFD–PLS is a promising wavenumber selection method.