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带色涂层的片剂包衣过程监测。

Monitoring of tablet coating processes with colored coatings.

机构信息

Heinrich Heine University Duesseldorf, Institute of Pharmaceutics and Biopharmaceutics, Universitaetsstr. 1, 40225 Duesseldorf, Germany.

Heinrich Heine University Duesseldorf, Institute of Pharmaceutics and Biopharmaceutics, Universitaetsstr. 1, 40225 Duesseldorf, Germany.

出版信息

Talanta. 2018 Feb 1;178:686-697. doi: 10.1016/j.talanta.2017.10.008. Epub 2017 Oct 7.

Abstract

Endpoints of coating processes for colored tablets were determined using in-line Raman spectroscopy. Coatings were performed with six commercially available formulations of pink, yellow, red, beige, green and blue color. The coatings were comprising pigments and/or dyes, some causing fluorescence and interfering the Raman signal. Using non-contact optics, a Raman probe was used as process analytical technology (PAT) tool, and acquired spectra were correlated to the sprayed mass of aqueous coating suspension. Process endpoints were determined using univariate (UV) data analysis and three multivariate analysis methods, namely Projection to Latent Structures (PLS)-regression, Science-Based Calibration (SBC) and Multivariate Curve Resolution (MCR). The methods were compared regarding model performance parameters. The endpoints of all coating experiments could be predicted until a total coating time of 50min corresponding to coating thicknesses between 21 and 38µm, depending on the density of the coat formulation. With the exception of SBC, all calibration methods resulted in R values higher than 0.9. Additionally, the methods were evaluated regarding their capability for in-line process monitoring. For each color, at least two methods were feasible to do this. Overall, PLS-regression led to best model performance parameters.

摘要

使用在线拉曼光谱法确定彩色片剂包衣过程的终点。使用六种市售的粉红色、黄色、红色、米色、绿色和蓝色配方进行包衣。涂层由颜料和/或染料组成,其中一些会产生荧光并干扰拉曼信号。使用非接触式光学技术,将拉曼探头用作过程分析技术(PAT)工具,并将采集到的光谱与喷涂的水性涂层悬浮液的质量相关联。使用单变量(UV)数据分析和三种多变量分析方法(投影到潜在结构(PLS)-回归、基于科学的校准(SBC)和多变量曲线分辨率(MCR))来确定过程终点。比较了这些方法的模型性能参数。在总包衣时间为 50 分钟的情况下,可以预测所有包衣实验的终点,相应的包衣厚度在 21 到 38μm 之间,具体取决于包衣配方的密度。除了 SBC 之外,所有校准方法的 R 值均高于 0.9。此外,还评估了这些方法在线过程监测的能力。对于每种颜色,至少有两种方法可以做到这一点。总体而言,PLS 回归的模型性能参数最佳。

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