Crystal texture recognition system based on image analysis for the analysis of agglomerates.

In the process of chemical production and biopharmaceutical, with the complex reaction, the products will overlap or adhere to each other. Effectively distinguishing the overlap and adhesion of crystals is of great significance for the statistics of different morphological characteristics such as the number and size of crystals. This paper proposes a crystal texture recognition system based on image analysis, which mainly includes image pre-processing, feature extraction and texture classification. Firstly, the crystal images are pre-processed to eliminate the influence of water droplets, particle shadows and uneven illumination. Secondly, the Improved-Basic Gray Level Aura matrix (I-BGLAM) is used to extract texture features of the crystals to determine the focus state of crystals. Finally, the texture features are classified by back propagation neural network (BPNN) to effectively distinguish agglomerates and pseudo-agglomerates. The case study and experimental results of cooling crystallization of l-glutamic acid show that the texture recognition system can effectively distinguish the adhesion and overlap of crystals, and effectively analyze the agglomerates, and has good experimental accuracy. • We propose an image preprocessing method which can effectively remove various interferences caused by invasive imaging system. • We use image texture to analyze the focus state of the crystals and to determine the adhesion and overlap of the crystals. • We propose using BPNN to classify the texture and determine the crystal type. • We teste the discriminating effect of various feature extractors, and finally used I-BGLAM to identify agglomerations according to the experimental results. [ABSTRACT FROM AUTHOR]