Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innrain 52a, 6020 Innsbruck, Austria.
J Pharm Biomed Anal. 2011 Apr 5;54(5):1059-64. doi: 10.1016/j.jpba.2010.12.019. Epub 2010 Dec 22.
A successful application of NIR spectroscopy (NIRS) in combination with multivariate data analysis (MVA) for the simultaneous identification and particle size determination of amoxicillin trihydrate particles was developed. Particle size analysis was ascertained by NIRS in diffuse reflection mode on different particle size fractions of amoxicillin trihydrate with D90 particle diameters ranging from 6.9 to 21.7 μm. The present problem of fractionating the powder into good enough size fractions to achieve a stable calibration model was solved. By probing dried suspensions measurement parameters were optimized and further combined with the best suitable chemometric operations. Thereby the quality of established regression models could be improved considerably. A linear coherence between particle size and absorbance signal was found at specific wavenumbers. Satisfactory clustering by particle size was achieved by principal component analysis (PCA) whereas partial least squares regression (PLSR) and principal component regression (PCR) was compared for quantitatively calibrating the NIRS data. PLSR turned out to predict unknown test samples slightly better than PCR.
成功地将近红外光谱 (NIRS) 与多元数据分析 (MVA) 结合应用于阿莫西林三水合物颗粒的同时识别和粒度测定。在不同的阿莫西林三水合物粒径的不同粒径分数上,通过漫反射模式的 NIRS 进行粒径分析,D90 粒径范围为 6.9 至 21.7 μm。解决了将粉末分成足够好的粒径分数以实现稳定校准模型的当前问题。通过探测干燥悬浮液,优化了测量参数,并进一步与最佳化学计量操作相结合。由此可以大大提高建立的回归模型的质量。在特定的波数处发现粒径与吸光度信号之间存在线性相关性。主成分分析 (PCA) 实现了令人满意的粒径聚类,而偏最小二乘回归 (PLSR) 和主成分回归 (PCR) 被用于定量校准 NIRS 数据。PLSR 结果表明,其对未知测试样品的预测略优于 PCR。