LAUSR

Graphical abstract Figure. No caption available. HighlightsPowder homogeneity is monitored by Raman spectroscopy in real case conditions.Raman technique allows the measurement of multi‐component blends.Raman spectroscopy is compared with the most traditional HPLC analysis. ABSTRACT Raman spectroscopy has been positively evaluated as a tool for the in‐line and real‐time monitoring of powder blending processes and it has been proved to be effective in the determination of the endpoint of the mixing, showing its potential role as process analytical technology (PAT). The aim of this study is to show advantages and disadvantages of Raman spectroscopy with respect to the most traditional HPLC analysis. The spectroscopic results, obtained directly on raw powders, sampled from a two‐axis blender in real case conditions, were compared with the chromatographic data obtained on the same samples. The formulation blend used for the experiment consists of active pharmaceutical ingredient (API, concentrations 6.0% and 0.5%), lactose and magnesium stearate (as excipients). The first step of the monitoring process was selecting the appropriate wavenumber region where the Raman signal of API is maximal and interference from the spectral features of excipients is minimal. Blend profiles were created by plotting the area ratios of the Raman peak of API (AAPI) at 1598 cm−1 and the Raman bands of excipients (AEXC), in the spectral range between 1560 and 1630 cm−1, as a function of mixing time: the API content can be considered homogeneous when the time‐dependent dispersion of the area ratio is minimized. In order to achieve a representative sampling with Raman spectroscopy, each sample was mapped in a motorized XY stage by a defocused laser beam of a micro‐Raman apparatus. Good correlation between the two techniques has been found only for the composition at 6.0% (w/w). However, standard deviation analysis, applied to both HPLC and Raman data, showed that Raman results are more substantial than HPLC ones, since Raman spectroscopy enables generating data rich blend profiles. In addition, the relative standard deviation calculated from a single map (30 points) turned out to be representative of the degree of homogeneity for that blend time.