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Microwave resonance technology (MRT) is known as a process analytical technology (PAT) tool for moisture measurements in fluid-bed granulation. It offers a great potential for wet granulation processes even where the suitability of near-infrared (NIR) spectroscopy is limited, e.g. colored granules, large variations in bulk density. However, previous sensor systems operating around a single resonance frequency showed limitations above approx. 7.5% granule moisture. This paper describes the application of a novel sensor working with four resonance frequencies. In-line data of all four resonance frequencies were collected and further processed. Based on calculation of density-independent microwave moisture values multiple linear regression (MLR) models using Karl-Fischer titration (KF) as well as loss on drying (LOD) as reference methods were build. Rapid, reliable in-process moisture control (RMSEP≤0.5%) even at higher moisture contents was achieved.

Microwave sensor systems using resonance technology at a single resonance in the range of 2–3 GHz have been shown to be a rapid and reliable tool for moisture determination in solid materials including pharmaceutical granules. So far, their application is limited to lower moisture ranges or limitations above certain moisture contents had to be accepted. Aim of the present study was to develop a novel multi-resonance sensor system in order to expand the measurement range. Therefore, a novel sensor using additional resonances over a wide frequency band was designed and used to investigate inherent limitations of first generation sensor systems and material-related limits. Using granule samples with different moisture contents, an experimental protocol for calibration and validation of the method was established. Pursuant to this protocol, a multiple linear regression (MLR) prediction model built by correlating microwave moisture values to the moisture determined by Karl Fischer titration was chosen and rated using conventional criteria such as coefficient of determination (R2) and root mean square error of calibration (RMSEC). Using different operators, different analysis dates and different ambient conditions the method was fully validated following the guidance of ICH Q2(R1). The study clearly showed explanations for measurement uncertainties of first generation sensor systems which confirmed the approach to overcome these by using additional resonances. The established prediction model could be validated in the range of 7.6–19.6%, demonstrating its fit for its future purpose, the moisture content determination during wet granulations.