Department of Chemical Engineering, University of Puerto Rico at Mayaguez, Mayaguez, Puerto Rico.
AAPS PharmSciTech. 2012 Sep;13(3):1005-12. doi: 10.1208/s12249-012-9825-0. Epub 2012 Jul 24.
Three different approaches have been evaluated for monitoring ribbon density through real-time near-infrared spectroscopy measurements. The roll compactor was operated to produce microcrystalline cellulose (MCC) ribbons of varying densities. The first approach used the slope of the spectra which showed a variation through the ribbon that could be attributed to density. A second qualitative approach was also developed with a principal component analysis (PCA) model with spectra taken in-line during the production of ribbons in an ideal roll pressure range. The PCA (i.e., real-time) density scans show that the model was able to qualitatively capture the density responses resulting from variation in process parameters. The third approach involved multivariate partial least squares (PLS) calibration models developed at wavelength regions of 1,120-1,310 and 1,305-2,205 nm. Also, various PLS models were developed using three reference methods: caliper, pycnometer, and in-line laser. The third approach shows a quantitative difference between the model-predicted and the measured densities. Models developed at high-wavelength region showed highest accuracy compared with models at low-wavelength region. All the PLS models showed a high accuracy along the spectra collected throughout the production of the ribbons. The three methods showed applicability to process control monitoring by describing the changes in density during in-line sampling.
已经评估了三种不同的方法来通过实时近红外光谱测量来监测带状密度。操作辊式压实机以生产不同密度的微晶纤维素 (MCC) 带状物。第一种方法使用光谱的斜率,该斜率显示带状物中存在可以归因于密度的变化。还开发了第二种定性方法,该方法具有主成分分析 (PCA) 模型,在理想的辊压范围内生产带状物时在线采集光谱。PCA(即实时)密度扫描表明,该模型能够定性地捕获由于工艺参数变化而导致的密度响应。第三种方法涉及在波长区域 1,120-1,310 和 1,305-2,205nm 处开发的多元偏最小二乘 (PLS) 校准模型。此外,还使用三种参考方法(测径规、比重瓶和在线激光)开发了各种 PLS 模型。与低波长区域的模型相比,高波长区域的模型显示出定量差异。与在带状物生产过程中收集的整个光谱相比,所有 PLS 模型都显示出高精度。所有 PLS 模型都显示出高精度,适用于在线采样过程控制监测,描述了密度的变化。这三种方法通过描述在线采样过程中的密度变化,显示出在过程控制监测中的适用性。