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首页> 外文期刊>Crystal growth & design >Image Analysis for In-line Measurement of Multidimensional Size, Shape, and Polymorphic Transformation of L-Glutamic Acid Using Deep Learning-Based Image Segmentation and Classification
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Image Analysis for In-line Measurement of Multidimensional Size, Shape, and Polymorphic Transformation of L-Glutamic Acid Using Deep Learning-Based Image Segmentation and Classification

机译：基于深度学习的图像分割和分类，L-谷氨酸的多维尺寸，形状和多态性转化的线路测量图像分析

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摘要

In situ tracking of the crystallization process through image segmentation has been developed and has encountered many challenges including improvement of in situ image quality, optimization of algorithms, and increased computation efficiency. In this study, a new method based on computer vision was proposed using the state-of-the-art deep learning technology to track crystal individuals. For the model compound L-glutamic acid, two polymorphic forms with different morphologies were segmented and classified during a seeded polymorphic transformation process. Information such as counts, size, surface area, crystal size distribution, and morphology of alpha- and beta-form crystals was extracted for the individual crystals during the process. A comparative analysis was conducted with traditional process analytical technologies such as in situ Raman and focus beam reflection measurement. Results show a high accuracy of segmentation and classification technique and a reliable tracking of crystals evolution. The image processing speed of up to 10 frames per second makes the proposed approach suitable for in situ tracking and control of crystallization and particulate processes. Our work in this study attempts to bridge the gap between the advanced imaging analysis technology that is available today and the specific needs of solution crystallization, to track, count, and measure the individual crystals.
机译：通过图像分割的原位跟踪结晶过程已经开发并遇到了许多挑战，包括改进原位图像质量，算法优化以及增加的计算效率。在这项研究中，提出了一种基于计算机愿景的新方法，采用最先进的深度学习技术来跟踪晶体个体。对于模型化合物L-谷氨酸，在种子多晶型转化过程中进行两种具有不同形态的多态形式，并分类。在该方法期间，为各个晶体提取诸如计数，尺寸，表面积，晶体尺寸分布和形态的信息，例如α-和β-晶体的形态。通过传统的过程分析技术进行比较分析，例如原位拉曼和焦点光束反射测量。结果表明，分割和分类技术的高精度以及晶体进化的可靠跟踪。每秒高达10帧的图像处理速度使得适用于原位跟踪和控制结晶和颗粒方法的所提出的方法。我们在本研究中的工作试图弥合今天可用的先进成像分析技术与解决方案结晶的具体需求之间的差距，以跟踪，计数和测量单个晶体。

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  • 来源
    《Crystal growth & design》 |2018年第8期|共7页
  • 作者

    Gao Zhenguo; Wu Yuanyi; Bao Ying; Gong Junbo; Wang Jingkang; Rohani Sohrab;

  • 作者单位

    Univ Western Ontario Dept Chem &

    Biochem Engn London ON N6A 5B9 Canada;

    Univ Western Ontario Dept Chem &

    Biochem Engn London ON N6A 5B9 Canada;

    Tianjin Univ Sch Chem Engn &

    Technol State Key Lab Chem Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Chem Engn &

    Technol State Key Lab Chem Engn Tianjin 300072 Peoples R China;

    Tianjin Univ Sch Chem Engn &

    Technol State Key Lab Chem Engn Tianjin 300072 Peoples R China;

    Univ Western Ontario Dept Chem &

    Biochem Engn London ON N6A 5B9 Canada;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 晶体学;
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