Spectral Pre-Treatments of Hyperspectral near Infrared Images: Analysis of Diffuse Reflectance Scattering

Abstract

Scattering effects are often encountered when measuring diffuse reflectance near infrared (NIR) spectra of solid and semi-solid materials. How does this phenomenon effect hyperspectral imaging of powders? A series of hyperspectral NIR images of particle size fractions of commercial grade salt and sugar were acquired. Spectral pre-processing techniques, including Kubelka–Munk, standard normal variate and absorbance transforms, unit length or unit area normalisation, first and second derivative transforms, and several variants of multiplicative scatter corrections (MSC) were applied to the images and examined for their effectiveness at reducing or eliminating scatter effects. Principal component analysis (PCA) scoreplots produced expected results: derivative transforms reduced variance, but did not eliminate the particle size dependencies; piecewise MSC transforms reduced the data to two clusters, one for salt and one for sugar. Partial least squares (PLS) regression was applied to examine the impact of the pre-processing transforms on prediction of particle size. RMSEP values between 10 and 50 μm were determined for particle fractions ranging between 140 and 315 μm for all transforms except the piecewise MSC; in spite of the reduction in additive and multiplicative effects, enough correlated variance remained after application of the pre-processing transforms to allow prediction of particle size ranges from PLS models. Additional scatter effect information was obtained by examining particle size distribution histograms and spatial particle size mappings facilitated by the hyperspectral images.