Influence of moisture variation on the performance of Raman spectroscopy in quantitative pharmaceutical analyses

[Display omitted] •Raman spectral variations of the pharmaceutical materials were observed due to the presence of water.•Spectral baseline change caused spectral inconsistency across calibration and test samples which degraded model performance.•Including moisture variations into the calibration set significantly improved Raman spectroscopic model performance. To develop a robust quantitative calibration model for spectroscopy, different sources of variability that are not directly related to the components of interest should be included in the calibration samples; this variability should be similar to that which is anticipated during validation and routine operation. Moisture content of pharmaceutical samples can vary as a function of supplier, storage conditions, geographic origin or seasonal variation. Additionally, some pharmaceutical operations (e. g., wet granulation) cause exposure of excipients and API to water. Although water is a weak Raman scatterer, moisture variability has an indirect effect on analytical model performance. Because many pharmaceutical components have intrinsic fluorescent characteristics (with broad spectral features), moisture variability may cause spectral artifacts in the form of baseline variation associated with fluorescence quenching. This work investigates the deleterious effects of water quenching on quantitative prediction accuracy of a multivariate calibration algorithm for Raman spectroscopy. To demonstrate this, a formulation composed of acetaminophen, lactose, microcrystalline cellulose, HPMC and magnesium stearate was used. Tablets were manufactured using laboratory scale equipment. A full-factorial design was used to vary acetaminophen (5 levels), and excipient ratios (3 levels) to generate tablets for calibration and testing. Tablet moisture variation was introduced by placing samples in different humidity chambers. Significant spectral effects arising from fluorescence were identified in the Raman spectra and due to moisture variation: the fluorescence related spectral variability caused substantial degradation of the prediction performance for API. The work demonstrated that accounting for moisture variation during method development reduced the prediction error of the multivariate prediction model.