滚筒压缩:通过太赫兹光谱和机器学习测量带孔度
Runqiao Dong1, Daniel J Goodwin2, Joelle Nassar2
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, CB3 0AS, Cambridge, UK.
International journal of pharmaceutics
|November 3, 2024
概括
卷积神经网络分析太赫兹 (THz) 频谱,以分类制药卷轴压缩带拓. 这种方法提高了孔径测量精度,克服了传统方法的局限性.
科学领域:
- 制药制造业 制药制造业 制药制造业
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 卷轴紧缩在制药制造中至关重要,带孔度是关键的质量属性.
- 传统的孔隙性分析方法很慢,只能提供平均批量值.
- 太赫兹 (THz) 光谱仪提供快速,非破坏性的孔径测量,但对表面地形学非常敏感.
研究的目的:
- 开发和训练机器学习模型,使用THz光谱对带拓进行分类.
- 为了提高基于THz的孔径测量在制药制造中的准确性和精度.
- 为了使卷轴压缩带内密度分布的分辨率.
主要方法:
- 卷积神经网络 (CNN) 模型被开发和训练.
- THz光谱被用作CNN模型的输入.
- 模型被训练分类四个带状地形:山脊,山谷,平面和边缘点.
主要成果:
- 分类器在识别异常值和区分光滑表面和曲表面方面实现了91%的验证准确性.
- 在曲表面上区分山脊和山谷的测试精度达到了81%.
- 开发的方法允许解决样本内的密度分布.
结论:
- CNN模型有效地从THz光谱分类了带状地形,增强了孔径分析.
- 这种方法解决了传统方法的局限性,通过提供空间解析的孔径数据.
- 将地形和光谱数据结合起来,可以准确地确定与传统方法相容的平均散孔度.
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