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Understanding Matrix-Assisted Continuous Co-crystallization Using a Data Mining Approach in Quality by Design (QbD)
Crystal Growth & Design ( IF 3.8 ) Pub Date : 2020-06-08 , DOI: 10.1021/acs.cgd.0c00338
Billy Chabalenge 1 , Sachin Korde 1, 2 , Adrian L Kelly 2, 3 , Daniel Neagu 3 , Anant Paradkar 1, 2
Affiliation  

The present study demonstrates the application of decision tree algorithms to the co-crystallization process. Fifty four (54) batches of carbamazepine–salicylic acid co-crystals embedded in poly(ethylene oxide) were manufactured via hot melt extrusion and characterized by powder X-ray diffraction, differnetial scanning calorimetry, and near-infrared spectroscopy. This dataset was then applied in WEKA, which is an open-sourced machine learning software to study the effect of processing temperature, screw speed, screw configuration, and poly(ethylene oxide) concentration on the percentage of co-crystal conversion. The decision trees obtained provided statistically meaningful and easy-to-interpret rules, demonstrating the potential to use the method to make rational decisions during the development of co-crystallization processes.

中文翻译:

通过设计质量(QbD)的数据挖掘方法了解基质辅助连续共结晶

本研究证明了决策树算法在共结晶过程中的应用。通过热熔挤出生产了五十四(54)批嵌入在聚环氧乙烷中的卡马西平-水杨酸共晶体,并通过粉末X射线衍射,差示扫描量热法和近红外光谱进行了表征。然后将此数据集应用到WEKA,这是一个开源的机器学习软件,用于研究加工温度,螺杆速度,螺杆构型和聚环氧乙烷浓度对共晶转化率的影响。获得的决策树提供了统计上有意义且易于解释的规则,表明了在共结晶过程开发过程中使用该方法做出合理决策的潜力。
更新日期:2020-07-01
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