Department of Chemical Engineering, University of Puerto Rico at Mayaguez, Puerto Rico 00681, United States.
Department of Chemistry, University of Puerto Rico at Mayaguez, Puerto Rico 00681, United States.
Int J Pharm. 2023 May 25;639:122934. doi: 10.1016/j.ijpharm.2023.122934. Epub 2023 Apr 13.
This study describes the first implementation of Raman spectrometer in a stream sampler for the in-line monitoring of low drug concentration in poor flowability powder blends. Raman spectra were continuously acquired as the powder blends flowed through the stream sampler operating with a paddle wheel speed of 10 RPM and used to develop the calibration models. A calibration model was developed to quantify caffeine concentration from 1.50 to 4.50% w/w using Partial Least Squares Regression (PLS-R). Three test set blends were used to assess the prediction errors of the calibration model. Caffeine concentration was predicted for the test set blends with a root mean square error of prediction of 0.21% w/w and a low bias of -0.03% w/w. The calibration model showed good prediction performance with an estimated sample mass of 83 mg. Variographic analysis demonstrated the low process variance of the real-time spectral acquisition through minimum practical error and sill values. The results showed the ability of the Raman spectrometer coupled with the stream sampler to monitor low drug concentration for poor flowability blends.
本研究首次在流体制样器中实现了拉曼光谱仪,用于在线监测低药物浓度和流动性差的粉末混合物。当粉末混合物以 10 RPM 的桨轮速度流过流体制样器时,连续采集拉曼光谱,并用于开发校准模型。使用偏最小二乘回归(PLS-R)建立了一个从 1.50 到 4.50% w/w 定量咖啡因浓度的校准模型。使用三个测试集混合物来评估校准模型的预测误差。用预测均方根误差为 0.21% w/w 和低偏差-0.03% w/w 对测试集混合物进行了咖啡因浓度预测。该校准模型具有良好的预测性能,估计的样本质量为 83mg。变异分析表明,通过最小实际误差和基差值,实时光谱采集的过程方差较低。结果表明,拉曼光谱仪与流体制样器相结合,能够监测低药物浓度和流动性差的混合物。