Journal Highlight: Assessment of crystallinity in processed sucrose by near-Infrared spectroscopy and application to lyophiles

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  • Published: Oct 6, 2014
  • Author: spectroscopyNOW
  • Channels: Infrared Spectroscopy
thumbnail image: Journal Highlight: Assessment of crystallinity in processed sucrose by near-Infrared spectroscopy and application to lyophiles
The capability of near-infrared spectroscopy to determine crystallinity in processed sucrose was studied using a common set of calibration standards derived from binary physical mixtures.



Assessment of crystallinity in processed sucrose by near-Infrared spectroscopy and application to lyophiles

Journal of Pharmaceutical Sciences, 2014, 103, 2884-2895
Paul E. Luner and Jeffery J. Seyer

Abstract: The objective of this study was to investigate the capability of near-infrared spectroscopy (NIRS) to determine crystallinity in processed sucrose using a common set of calibration standards derived from binary physical mixtures. NIRS was applied as a primary method using binary mixtures of amorphous and crystalline standards to predict crystallinity in sucrose that was either rendered partially amorphous by milling, partially recrystallized from the amorphous phase, or amorphous lyophiles annealed to induce recrystallization. Crystallinity prediction in the case of milled crystalline and recrystallized amorphous sucrose was feasible using the two-state binary calibration mixtures applying a univariate model. NIRS results for milled sucrose were comparable to those obtained using X-ray powder diffraction. The changes in crystallinity after milling and recrystallization showed expected trends. However, the same NIRS univariate calibration method could not be successfully applied for directly through the vial. To overcome this complication, NIRS was applied as a secondary method relative to water vapor sorption (WVS) where a set of processed samples were measured using both NIRS and WVS and a partial least-squares model applied. The NIRS secondary method was successfully applied and provided a standard error of calibration of 2.11% and standard error of prediction of 3.76%.

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