Quantitative determination of content in binary powder mixtures using diffuse reflectance near infrared spectrometry and multivariate analysis

Abstract

A simplified methodology for development of calibrations for binary powder mixtures using near infrared (NIR) spectroscopy is presented. It is demonstrated that multivariate calibration can be performed using only a small calibration dataset of two to five samples. The intrinsic heterogeneity of the powder mixtures at the sample size effectively measured using standard fibre-optic probes prevents the assignment of a true reference content value to each obtained spectrum. Instead, the nominal mixture content of the sample is used as reference value. To obtain a spectrum more representative of each sample, a mean spectrum is computed from several spectra collected from different sample sub-fractions. Two principally different powder mixtures were used in this study: one composed of two fine powders ( d<300 μm) and the other mixture composed of one fine ( d 50=170 μm) and one coarse ( d 50=590 μm) powder. In the case of mixing fine powders, the calibration model is obtained by PLS regression using two PLS components after spectral data pre-treatment with multiplicative signal correction (MSC). Furthermore, it is demonstrated that the powder content variation determined by NIR is wavelength dependent due to the dependence of effective sample size on radiation wavelength. In the case of mixing one fine and one coarse powder, the large difference in particle size between the two powder components cause a non-linear X– Y relationship, which can be handled by means of spectral averaging and a non-linear (QPLS) regression. In both cases, the first PLS loading vector was interpreted as describing the difference between the spectra of the two pure components. The presented methodology should be useful for applications of NIR spectroscopy in processes involving powder mixtures.