Online Estimation of Diastereomer Composition Using Raman: Differentiation in High and Low Slurry Density Partial Least Square Models

详细信息    查看全文

• 作者：Sze-Wing Wong ; Christos Georgakis ; Gregory D. Botsaris ; Kostas Saranteas ; Roger Bakale
• 刊名：Crystal Growth & Design
• 出版年：2008
• 出版时间：December 3, 2008
• 年：2008
• 卷：8
• 期：12
• 页码：4398-4408
• 全文大小：413K
• 年卷期：v.8,no.12(December 3, 2008)
• ISSN：1528-7505

文摘

This paper addresses the estimation of fractional solid composition of two diastereomers during crystallization. The estimation is obtained through a partial least square (PLS) regression model that utilizes online Raman spectroscopy and additional process information such as temperature and slurry density. Twelve PLS models were constructed with the same 95 calibration standards. The models differ from each other on whether they model all the data or that from one of two subsets and on whether they involve temperature or slurry density or both along with the spectral data. It is shown that in situ Raman spectroscopy is capable of differentiating diastereomers in a crystallization slurry, provided the changing process parameters of temperature and slurry density are also included in the estimation. Furthermore, models developed from one of the two subsets, which were classified by high and low slurry density, were more accurate than the corresponding model developed from the whole data set.