Bonifazi Giuseppe, Barontini Paolo, Gasbarrone Riccardo, Gattabria Davide, Serranti Silvia
Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy.
Research Center for Biophotonics, Sapienza University of Rome, Corso della Repubblica 79, 04100 Latina, Italy.
J Imaging. 2024 Feb 21;10(3):53. doi: 10.3390/jimaging10030053.
In this manuscript, a method that utilizes classical image techniques to assess particle aggregation and segregation, with the primary goal of validating particle size distribution determined by conventional methods, is presented. This approach can represent a supplementary tool in quality control systems for powder production processes in industries such as manufacturing and pharmaceuticals. The methodology involves the acquisition of high-resolution images, followed by their fractal and textural analysis. Fractal analysis plays a crucial role by quantitatively measuring the complexity and self-similarity of particle structures. This approach allows for the numerical evaluation of aggregation and segregation phenomena, providing valuable insights into the underlying mechanisms at play. Textural analysis contributes to the characterization of patterns and spatial correlations observed in particle images. The examination of textural features offers an additional understanding of particle arrangement and organization. Consequently, it aids in validating the accuracy of particle size distribution measurements. To this end, by incorporating fractal and structural analysis, a methodology that enhances the reliability and accuracy of particle size distribution validation is obtained. It enables the identification of irregularities, anomalies, and subtle variations in particle arrangements that might not be detected by traditional measurement techniques alone.
在本手稿中，提出了一种利用经典图像技术评估颗粒聚集和分离的方法，其主要目的是验证传统方法确定的粒度分布。这种方法可以成为制造和制药等行业粉末生产过程质量控制系统中的一种补充工具。该方法包括获取高分辨率图像，然后对其进行分形和纹理分析。分形分析通过定量测量颗粒结构的复杂性和自相似性发挥着关键作用。这种方法允许对聚集和分离现象进行数值评估，为所涉及的潜在机制提供有价值的见解。纹理分析有助于表征颗粒图像中观察到的图案和空间相关性。对纹理特征的检查提供了对颗粒排列和组织的额外理解。因此，它有助于验证粒度分布测量的准确性。为此，通过结合分形和结构分析，获得了一种提高粒度分布验证可靠性和准确性的方法。它能够识别颗粒排列中可能仅靠传统测量技术无法检测到的不规则性、异常和细微变化。
