کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2487165 | 1114406 | 2006 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity
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کلمات کلیدی
SIMCABlend uniformity analysisUV/Vis spectroscopy - UV / Vis spectroscopyPrincipal component analysis - تحلیل مولفههای اصلی یا PCAMultivariate analysis - تحلیل چندمتغیرهNear-infrared spectroscopy - طیف شناسی فروسرخ نزدیکProcess analytical technology (PAT) - فن آوری تحلیلی فرآیند (PAT)Mixing - مخلوط کردنglobal model - مدل جهانی
موضوعات مرتبط
علوم پزشکی و سلامت
داروسازی، سم شناسی و علوم دارویی
اکتشاف دارویی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity A Process Analytical Technology approach to near-infrared process control of pharmaceutical powder blending: Part II: Qualitative near-infrared models for prediction of blend homogeneity](/preview/png/2487165.png)
چکیده انگلیسی
The successful implementation of near-infrared spectroscopy (NIRS) in process control of powder blending requires constructing an inclusive spectral database that reflects the anticipated voluntary or involuntary changes in processing conditions, thereby minimizing bias in prediction of blending behavior. In this study, experimental design was utilized as an efficient way of generating blend experiments conducted under varying processing conditions such as humidity, blender speed and component concentration. NIR spectral data, collected from different blending experiments, was used to build qualitative models for prediction of blend homogeneity. Two pattern recognition algorithms: Soft Independent Modeling of Class Analogies (SIMCA) and Principal Component Modified Bootstrap Error-adjusted Single-sample Technique (PC-MBEST) were evaluated for qualitative analysis of NIR blending data. Optimization of NIR models, for the two algorithms, was achieved by proper selection of spectral processing, and training set samples. The models developed were successful in predicting blend homogeneity of independent blend samples under different processing conditions. © 2005 Wiley-Liss, Inc. and the American Pharmacists Association
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Pharmaceutical Sciences - Volume 95, Issue 2, February 2006, Pages 407-421
Journal: Journal of Pharmaceutical Sciences - Volume 95, Issue 2, February 2006, Pages 407-421
نویسندگان
Arwa S. El-Hagrasy, Miriam Delgado-Lopez, James K. III,