Advances and Challenges in Machine Learning-based Image Analysis for Monitoring and Predicting Organic Crystal Formation

Abstract

Manual crystallization experiments have always been challenging, requiring extensive process development expertise and often resulting in unpredictable results. The crystallization process plays a critical role in the development of high-quality organic materials, which are essential for various industries such as pharmaceuticals, materials science, and electronics. Therefore, crystallization experiments are in urgent need of innovative methods to ensure consistency, efficiency, and scalability. Recent studies have shown that machine learning can effectively assist crystal detection and segmentation, thus providing a new way to optimize organic crystallization processes, improving both the speed and precision of crystal formation. However, a comprehensive review of machine learning-based approaches for organic crystallization process monitoring remains elusive. It is therefore necessary to review the machine learning technologies involved, their current applications, technical challenges, and development blueprints. In this work, we focus on the application scenarios, basic principles, and common tools of machine learning methods based on image detection and segmentation in effectively monitoring the crystallization process of organic crystals, especially the research on artificial intelligence technology in the detection of crystal size and morphology, monitoring, and optimization of crystallization processes. Through this work, we aim to provide the oretical references and practical guidance for researchers in related fields.

Keywords

computer vision / crystallization process monitor / machine learning / organic crystals

References

[1] K. Hunger, M. U. Schmidt, T. Heber, F. Reisinger, and S. Wannemacher, Industrial Organic Pigments: Production, Crystal Structures, Properties, Applications, Fourth, Completely Revised Edition (Wiley, 2018).

[2] D. Jérome and H. J. Schulz, “Organic Conductors and Superconductors,” Advances in Physics 31 (1982): 299-490.

[3] A. Kadyshevitch and R. Naaman, “Electron Transmission Through Thin Organized Organic Films,” Surface and Interface Analysis 25 (1997): 71-75.

[4] E. Beaugnon and R. Tournier, “Levitation of organic materials,” Nature 349 (1991): 470.

[5] D. W. Chang, H. J. Choi, A. Filer, and J. B. Baek, “Graphene in Photovoltaic Applications: Organic Photovoltaic Cells (OPVs) and Dye-sensitized Solar Cells (DSSCs),” Journal of Materials Chemistry A 2 (2014): 12136-12149.

[6] H. Uoyama, K. Goushi, K. Shizu, H. Nomura, and C. Adachi, “Highly Efficient Organic Light-emitting Diodes From Delayed Fluorescence,” Nature 492 (2012): 234-238.

[7] N. Blagden, M. de Matas, P. T. Gavan, and P. York, “Crystal Engineering of Active Pharmaceutical Ingredients to Improve Solubility and Dissolution Rates,” Advanced Drug Delivery Reviews 59 (2007): 617-630.

[8] S. Miyazaki, M. Nakano, and T. Arita, “Effect of Crystal Forms on the Dissolution Behavior and Bioavailability of Tetracycline, Chlortetracycline, and Oxytetracycline Bases,” Chemical and Pharmaceutical Bulletin 23 (1975): 552-558.

[9] R. Censi and P. Di Martino, “Polymorph Impact on the Bioavailability and Stability of Poorly Soluble Drugs,” Molecules 20 (2015): 18759-18776.

[10] N. Li and L. S. Taylor, “Microstructure Formation for Improved Dissolution Performance of Lopinavir Amorphous Solid Dispersions,” Molecular Pharmaceutics 16 (2019): 1751-1765.

[11] Y. Huo, T. Liu, Z. Jiang, and J. Fan, “U-net Based Deep-Learning Image Monitoring of Crystal Size Distribution During L-Glutamic Acid Crystallization,” in 2021 40th Chinese Control Conference (CCC), Shanghai, China (2021), 2555-2560.

[12] H. H. Tung, E. L. Paul, M. Midler, and J. A. McCauley, Crystallization of Organic Compounds: An Industrial Perspective (John Wiley and Sons, 2008).

[13] B. Kovačič, F. Vrečer, and O. Planinšek, “Spherical Crystallization of Drugs,” Acta Pharmaceutica 62 (2012): 1-14.

[14] M. Giulietti, M. M. Seckler, S. Derenzo, M. I. Ré, and E. Cekinski, “Industrial Crystallization and Precipitation From Solutions: State of the Technique,” Brazilian Journal of Chemical Engineering 18 (2001): 423-440.

[15] H. H. Tung, “Industrial Perspectives of Pharmaceutical Crystallization,” Organic Process Research & Development 17 (2013): 445-454.

[16] M. Lu, S. Rao, H. Yue, J. Han, and J. Wang, “Recent Advances in the Application of Machine Learning to Crystal Behavior and Crystallization Process Control,” Crystal Growth & Design 24 (2024): 5374-5396.

[17] S. A. Bini, “Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do these Terms Mean and How Will They Impact Health Care?,” Journal of Arthroplasty 33 (2018): 2358-2361.

[18] J. A. Choi and K. Lim, “Identifying Machine Learning Techniques for Classification of Target Advertising,” Information and Communications Technology Express 6 (2020): 175-180.

[19] H. D. Shah, A. Sundas, and S. Sharma, “Controlling Email System Using Audio With Speech Recognition and Text to Speech,” in 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India (2021), 1-7.

[20] M. N. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Machine Learning towards Intelligent Systems: Applications, Challenges, and Opportunities,” Artificial Intelligence Review 54 (2021): 3299-3348.

[21] E. W. T. Ngai and Y. Wu, “Machine Learning in Marketing: A Literature Review, Conceptual Framework, and Research Agenda,” Journal of Business Research 145 (2022): 35-48.

[22] T. Baltrusaitis, C. Ahuja, and L. P. Morency, “Multimodal Machine Learning: A Survey and Taxonomy,” IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2019): 423-443.

[23] A. Mutemi and F. Bacao, “E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review,” Big Data Mining and Analytics 7 (2024): 419-444.

[24] C. Xiouras, F. Cameli, G. L. Quilló, M. E. Kavousanakis, D. G. Vlachos, and G. D. Stefanidis, “Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization,” Chemical Reviews 122 (2022): 13006-13042.

[25] Y. Kang, Z. Duan, T. Tong, et al., “An Enhanced Deep Learning-Based Pharmaceutical Crystal Detection With Regional Filtering,” Crystals 14 (2024): 709.

[26] O. N. F. King, K. E. Levik, J. Sandy, and M. Basham, “CHiMP: Deep-learning Tools Trained on Protein Crystallization Micrographs to Enable Automation of Experiments,” Acta Crystallographica Section D: Structural Biology 80 (2024): 744-764.

[27] A. Kardoost, R. Schönherr, C. Deiter, et al., “Convolutional Neural Network Approach for the Automated Identification of in Cellulo Crystals,” Journal of Applied Crystallography 57 (2024): 266-275.

[28] J. Fan, T. Liu, Y. Shuang, B. Song, J. Chen, and Y. Tan, “Deep Learning-Based Binocular Image Analysis for in Situ Measurement of Particle Length Distribution During Crystallization Process,” IEEE Transactions on Instrumentation and Measurement 72 (2023): 1-14.

[29] Y. Wu, Z. Gao, and S. Rohani, “Deep Learning-based Oriented Object Detection for in Situ Image Monitoring and Analysis: A Process Analytical Technology (PAT) Application for Taurine Crystallization,” Chemical Engineering Research and Design 170 (2021): 444-455.

[30] M. Li, J. Liu, T. Yao, Z. Gao, and J. Gong, “Deep-learning Based in-situ Micrograph Analysis of High-density Crystallization Slurry Using Image and Data Enhancement Strategy,” Powder Technology 437 (2024): 119582.

[31] L. Neuendorf, S. Höving, L. Bennemann, and N. Kockmann, “Detecting Crystals in Suspensions: Convolutional Neural Networks vs. Gravity-Based Approach for Size Distribution Detection,” Chemie-Ingenieur-Technik 95 (2023): 1146-1153.

[32] Z. Jiang, T. Liu, Y. Huo, and J. Fan, “Image Analysis of Crystal Size Distribution and Agglomeration for β Form L-Glutamic Acid Crystallization Based on YOLOv4 Deep Learning,” in 2021 China Automation Congress (CAC), Beijing, China (2021), 3017-3022.

[33] H. Niu, T. Liu, J. Fan, and H. Wang, “Image-Based Crystal Size Analysis for β-form L-Glutamic Acid Crystallization via Deep Learning-Based Object Detection,” in 2023 China Automation Congress (CAC), Chongqing, China (2023), 6483-6488.

[34] Y. Huo and F. Zhang, “In-situ Detection of Micro Crystals During Cooling Crystallization Based on Deep Image Super-Resolution Reconstruction,” IEEE Access 9 (2021): 31618-31626.

[35] Y. Chen, H. Yu, T. Liu, Y. Shuang, and J. Fan, “In-situ Measurement of Crystal Length and Width Based on Binocular Vision With Oriented Object Detection,” in 2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC), Dalian, China (2024), 324-329.

[36] D. Bischoff, B. Walla, and D. Weuster-Botz, “Machine Learning-based Protein Crystal Detection for Monitoring of Crystallization Processes Enabled With Large-scale Synthetic Data Sets of Photorealistic Images,” Analytical and Bioanalytical Chemistry 414 (2022): 6379-6391.

[37] C. Jiang, T. Liu, T. Yang, Z. Jiang, and H. Liu, “Mask R-CNN Based Deep Learning Analysis on in-situ Measured Crystal Images With Automatic Dataset Labelling,” in 2022 41st Chinese Control Conference (CCC), Hefei, China (2022), 6261-6266.

[38] T. Yang, C. Jiang, and Q. Meng, “Optimized Methods for Online Monitoring of L-Glutamic Acid Crystallization,” in 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) (2021), 93-97.

[39] J. Qin, Y. Zhang, H. Zhou, F. Yu, B. Sun, and Q. Wang, “Protein Crystal Instance Segmentation Based on Mask R-CNN,” Crystals 11 (2021): 157.

[40] J. Lins, U. Ebeling, and K. Wohlgemuth, “Agglomeration Kernel Determination by Combining in-Process Image Analysis and Modeling,” Crystal Growth & Design 22 (2022): 5363-5374.

[41] D. R. Ochsenbein, T. Vetter, S. Schorsch, M. Morari, and M. Mazzotti, “Agglomeration of Needle-Like Crystals in Suspension: I. Measurements,” Crystal Growth & Design 15 (2015): 1923-1933.

[42] C. Jiang, C. Y. Ma, T. A. Hazlehurst, et al., “Automated Growth Rate Measurement of the Facet Surfaces of Single Crystals of the β-Form of L-Glutamic Acid Using Machine Learning Image Processing,” Crystal Growth & Design 24 (2024): 3277-3288.

[43] L. Yan, J. Fan, T. Liu, H. Wang, and Z. Zhao, “Automatic Labeling of in-Situ Crystal Images Based on Lightweight Network Bisenetv2,” in 2024 43rd Chinese Control Conference (CCC), Kunming, China (2024), 7888-7893.

[44] J. Calderon De Anda, X. Z. Wang, X. Lai, and K. J. Roberts, “Classifying Organic Crystals via in-process Image Analysis and the Use of Monitoring Charts to Follow Polymorphic and Morphological Changes,” Journal of Process Control 15 (2005): 785-797.

[45] Z. M. Lu, L. Zhang, D. M. Fan, N. M. Yao, and C. X. Zhang, “Crystal Texture Recognition System Based on Image Analysis for the Analysis of Agglomerates,” Chemometrics and Intelligent Laboratory Systems 200 (2020): 103985.

[46] S. Heisel, M. Rolfes, and K. Wohlgemuth, “Discrimination Between Single Crystals and Agglomerates During the Crystallization Process,” Chemical Engineering and Technology 41 (2018): 1218-1225.

[47] S. Chen, T. Liu, D. Xu, Y. Huo, and Y. Yang, “Image Based Measurement of Population Growth Rate for L-Glutamic Acid Crystallization,” in 2019 Chinese Control Conference (CCC), Guangzhou, China (2019), 7933-7938.

[48] Y. Huo, X. Li, and B. Tu, “Image Measurement of Crystal Size Growth During Cooling Crystallization Using High-Speed Imaging and a U-Net Network,” Crystals 12 (2022): 1690.

[49] H. Salami, M. A. McDonald, A. S. Bommarius, R. W. Rousseau, and M. A. Grover, “In Situ Imaging Combined With Deep Learning for Crystallization Process Monitoring: Application to Cephalexin Production,” Organic Process Research & Development 25 (2021): 1670-1679.

[50] J. Lins, T. Harweg, F. Weichert, and K. Wohlgemuth, “Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization,” Applied Sciences 12 (2022): The 2465.

[51] Y. Thielmann, T. Luft, N. Zint, and J. Koepke, “Crystal Search—Feasibility Study of a Real-Time Deep Learning Process for Crystallization Well Images,” Acta Crystallographica Section A: Foundations and Advances 79 (2023): 331-338.

[52] Y. Huo, D. Guan, and L. Dong, “Online Defect Detection in LGA Crystallization Imaging Using MANet-Based Deep Learning Image Analysis,” Crystals 14 (2024): 298.

[53] X. Zhu, S. Sun, and M. Bern, “Classification of Protein Crystallization Imagery,” in 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS '04) (2004), 1628-1631.

[54] K. Kawabata, M. Takahashi, K. Saitoh, et al., “Evaluation of Crystalline Objects in Crystallizing Protein Droplets Based on Line-segment Information in Greyscale Images,” Acta Crystallographica Section D: Biological Crystallography 62 (2006): 239-245.

[55] R. Liu, Y. Freund, and G. Spraggon, “Image-based Crystal Detection: A Machine-learning Approach,” Acta Crystallographica Section D: Biological Crystallography 64 (2008): 1187-1195.

[56] Z. Su, J. He, P. Zhou, L. Huang, and J. Zhou, “A High-throughput System Combining Microfluidic Hydrogel Droplets With Deep Learning for Screening the Antisolvent-crystallization Conditions of Active Pharmaceutical Ingredients,” Lab on a Chip 20 (2020): 1907-1916.

[57] C. Cumbaa and I. Jurisica, “Automatic Classification and Pattern Discovery in High-throughput Protein Crystallization Trials,” Journal of Structural and Functional Genomics 6 (2005): 195-202.

[58] S. Wu, K. Roberts, S. Datta, et al., “Deep Learning in Clinical Natural Language Processing: A Methodical Review,” Journal of the American Medical Informatics Association 27 (2020): 457-470.

[59] A. A. Khan, A. A. Laghari, and S. A. Awan, “Machine Learning in Computer Vision: A Review,” EAI Endorsed Transactions on Scalable Information Systems 8 (2021): 1-11.

[60] V. Fehst, H. C. La, T.-D. Nghiem, B. E. Mayer, P. Englert, and K.-H. Fiebig, “Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality,” in GECCO '18 Companion: Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan (2018), 5-6.

[61] S. Zhang, X. Liang, X. Huang, K. Wang, and T. Qiu, “Precise and Fast Microdroplet Size Distribution Measurement Using Deep Learning,” Chemical Engineering Science 247 (2022): 116926.

[62] M. Hort, B. D. Marsh, R. G. Resmini, and M. K. Smith, “Convection and Crystallization in a Liquid Cooled From Above: An Experimental and Theoretical Study,” Journal of Petrology 40 (1999): 1271-1300.

[63] V. Erukhimovitch and J. Baram, “Crystallization Kinetics,” Physical Review B 50 (1994): 5854-5856.

[64] A. E. Lewis, M. M. Seckler, H. Kramer, and G. Van Rosmalen, Industrial Crystallization: Fundamentals and Applications (Cambridge University Press, 2015).

[65] C. Y. Jong, A. Mittal, G. Tristan, et al., “ANFIS-Driven Machine Learning Automated Platform for Cooling Crystallization Process Development,” Organic Process Research & Development 28 (2024): 1129-1144.

[66] F. Oba and Y. Kumagai, “Design and Exploration of Semiconductors From First Principles: A Review of Recent Advances,” Applied Physics Express 11 (2018): 060101.

[67] J. P. Janet, F. Liu, A. Nandy, et al., “Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry,” Inorganic Chemistry 58 (2019): 10592-10606.

[68] G. G. C. Peterson and J. Brgoch, “Materials Discovery Through Machine Learning Formation Energy,” Journal of Physics: Energy 3 (2021), 022002.

[69] X. Yin and C. E. Gounaris, “Search Methods for Inorganic Materials Crystal Structure Prediction,” Current Opinion in Chemical Engineering 35 (2022): 100726.

[70] J. Recatala-Gomez, A. Suwardi, I. Nandhakumar, A. Abutaha, and K. Hippalgaonkar, “Toward Accelerated Thermoelectric Materials and Process Discovery,” ACS Applied Energy Materials 3 (2020): 2240-2257.

[71] Z. Ye, N. Wang, J. Zhou, and D. Ouyang, “Organic Crystal Structure Prediction via Coupled Generative Adversarial Networks and Graph Convolutional Networks,” The Innovation 5 (2024), 100562.

[72] S. Zong, G. Zhou, M. Li, and X. Wang, “Deep Learning-based On-line Image Analysis for Continuous Industrial Crystallization Processes,” Particuology 74 (2023): 173-183.

[73] B. Qi, H. Qin, Y. Li, and M. Wang, “Dynamic Crystal Monitoring Method for Crystallization Process Based on Deep Learning,” Ferroelectrics 608 (2023): 153-162.

[74] Y. Huo, T. Liu, H. Liu, C. Y. Ma, and X. Z. Wang, “In-situ Crystal Morphology Identification Using Imaging Analysis With Application to the L-glutamic Acid Crystallization,” Chemical Engineering Science 148 (2016): 126-139.

[75] Z. Gao, Y. Wu, Y. Bao, J. Gong, J. Wang, and S. Rohani, “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,” Crystal Growth & Design 18 (2018): 4275-4281.

[76] L. Fang, J. Liu, D. Han, Z. Gao, and J. Gong, “Revealing the Role of Polymer in the Robust Preparation of the 2,4-dichlorophenoxyacetic Acid Metastable Crystal Form by AI-based Image Analysis,” Powder Technology 413 (2023): 118077.

[77] J. Wei, J. Liang, J. Song, and P. Zhou, “YOLO-PBESW: A Lightweight Deep Learning Model for the Efficient Identification of Indomethacin Crystal Morphologies in Microfluidic Droplets,” Micromachines 15 (2024): 1136.

[78] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy (2017), 2980-2988.

[79] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015, Lecture Notes in Computer Science (Springer, 2015), 234-241.

[80] A. M. Roy, R. Bose, and J. Bhaduri, “A Fast Accurate Fine-grain Object Detection Model Based on YOLOv4 Deep Neural Network,” Neural Computing and Applications 34 (2022): 3895-3921.

[81] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA (2016), 779-788.

[82] S. Norkobil Saydirasulovich, A. Abdusalomov, M. K. Jamil, R. Nasimov, D. Kozhamzharova, and Y. I. Cho, “A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments,” Sensors 23 (2023): 3161.

[83] S. Dharmayat, R. B. Hammond, X. Lai, et al., “An Examination of the Kinetics of the Solution-Mediated Polymorphic Phase Transformation Between α- and β-Forms of l -Glutamic Acid as Determined Using Online Powder X-ray Diffraction,” Crystal Growth & Design 8 (2008): 2205-2216.

[84] C. J. Burns and G. M. H. Swaen, “Review of 2,4-dichlorophenoxyacetic Acid (2,4-D) Biomonitoring and Epidemiology,” Critical Reviews in Toxicology 42 (2012): 768-786.

[85] L. Carpentier, R. Decressain, S. Desprez, and M. Descamps, “Dynamics of the Amorphous and Crystalline α-, γ-Phases of Indomethacin,” Journal of Physical Chemistry B 110 (2006): 457-464.

[86] M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment Anything Model for Medical Image Analysis: An Experimental Study,” Medical Image Analysis 89 (2023): 102918.

[87] C. A. Offiler, A. J. Cruz-Cabeza, R. J. Davey, and T. Vetter, “Complex Growth of Benzamide Form I: Effect of Additives, Solution Flow, and Surface Rugosity,” Crystal Growth & Design 22 (2022): 6248-6261.

[88] C. A. Offiler, R. J. Davey, A. J. Cruz-Cabeza, and T. Vetter, “Investigating Additive Effects on α-Glycine Growth Through the Measurement of Facet Specific Growth Rates,” Crystal Growth & Design 25 (2025): 1644-1652.

[89] J. Liu, T. Yao, M. Li, et al., “Computer Vision-Assisted High-Throughput Screening of Crystallization Additives for Crystal Size, Shape, and Agglomeration Regulation,” Engineering 54 (2025): 308-319.

[90] M. Sigdel, M. L. Pusey, and R. S. Aygun, “Real-time Protein Crystallization Image Acquisition and Classification System,” Crystal Growth & Design 13 (2013): 2728-2736.

[91] S. Pan, G. Shavit, M. Penas-Centeno, et al., “Automated Classification of Protein Crystallization Images Using Support Vector Machines With Scale-invariant Texture and Gabor Features,” Acta Crystallographica Section D: Biological Crystallography 62 (2006): 271-279.

[92] K. Saitoh, K. Kawabata, H. Asama, T. Mishima, and M. Sugahara, “Design of Classifier to Automate the Evaluation of Protein Crystallization States,” in Proceedings 2006 IEEE International Conference on Robotics and Automation (ICRA 2006), Orlando, FL, USA (2006), 1800-1805.

[93] M. J. Po and A. F. Laine, “Leveraging Genetic Algorithm and Neural Network in Automated Protein Crystal Recognition,” in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2008), Vancouver, BC, Canada (2008), 1926-1929.

[94] C. A. Cumbaa and I. Jurisica, “Protein Crystallization Analysis on the World Community Grid,” Journal of Structural and Functional Genomics 11 (2010): 61-69.

[95] J. Hung, J. Collins, and M. Weldetsion, “Protein Crystallization Image Classification With Elastic Net,” in Medical Imaging 2014: Image Processing, Proc. SPIE 9034, San Diego, CA, USA (2014), 90341X.

[96] M. L. J. Yann and Y. Tang, “Learning Deep Convolutional Neural Networks for X-Ray Protein Crystallization Image Analysis,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ, USA (2016), 1373-1379.

[97] A. E. Bruno, P. Charbonneau, J. Newman, et al., “Classification of Crystallization Outcomes Using Deep Convolutional Neural Networks,” PLOS ONE 13 (2018): e0198883.

[98] D. W. Edwards II and I. Dinc, “Classification of Protein Crystallization Images Using EfficientNet With Data Augmentation,” in CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics (2020), 54-60.

[99] J. Zhang, Y. Meng, J. Wu, J. Qin, T. Yao, and S. Yu, “Monitoring Sugar Crystallization With Deep Neural Networks,” Journal of Food Engineering 280 (2020): 109965.

[100] T. X. Tran, M. L. Pusey, and R. S. Aygun, “Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images,” Journal of Fluorescence 30 (2020): 637-656.

[101] T. Tran, M. Pusey, and R. Aygun, “Mobile Fluorescence Imaging and Protein Crystal Recognition,” in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Virtual, Online (2020), 83-88.

[102] S. Chayatummagoon and P. Chongstitvatana, “Image Classification of Sugar Crystal With Deep Learning,” in 2021 13th International Conference on Knowledge and Smart Technology (KST) (2021), 118-122.

[103] J. Milne, C. Qian, D. Hargreaves, Y. Wang, and J. Wilson, “Not Getting in Too Deep: A Practical Deep Learning Approach to Routine Crystallisation Image Classification,” PLOS ONE 18 (2023): e0282562.

[104] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery 2 (1998): 121-167.

[105] S. J. D. Prince and J. H. Elder, “Probabilistic Linear Discriminant Analysis for Inferences About Identity,” in 2007 IEEE 11th International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil (2007), 1-8.

[106] J. M. Keller and J. A. Givens, “A Fuzzy K-Nearest Neighbor Algorithm,” IEEE Transactions on Systems, Man, and Cybernetics 15 (1985): 580-585.

[107] L. Breiman, “Random Forests,” Machine learning 45 (2001): 5-32.

[108] R. Agrawal, T. Imieliński, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” ACM SIGMOD Record 22 (1993): 207-216.

[109] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA (2016), 785-794.

[110] L. G. Hemkens, E. I. Benchimol, S. M. Langan, et al., “The Reporting of Studies Using Routinely Collected Health Data Was Often Insufficient,” Journal of Clinical Epidemiology 79 (2016): 104-111.

[111] T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Supervised Neural Network Modeling: An Empirical Investigation Into Learning From Imbalanced Data With Labeling Errors,” IEEE Transactions on Neural Networks 21 (2010): 813-830.

[112] J. Zou, M. Huss, A. Abid, P. Mohammadi, A. Torkamani, and A. Telenti, “A Primer on Deep Learning in Genomics,” Nature Genetics 51 (2019): 12-18.

[113] S. He, L. G. Leanse, and Y. Feng, “Artificial Intelligence and Machine Learning Assisted Drug Delivery for Effective Treatment of Infectious Diseases,” Advanced Drug Delivery Reviews 178 (2021): 113922.

[114] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A Survey on Deep Transfer Learning,” in Artificial Neural Networks and Machine Learning—ICANN 2018, Lecture Notes in Computer Science (Springer, 2018), 270-279.

[115] J. Fan, T. Liu, Y. Shuang, et al., “Noninvasive Stereoscopic Backlight Imaging Design for in Situ Measurement of Particle Size Distribution during Continuous Crystallization via COBC,” IEEE Transactions on Instrumentation and Measurement 74 (2025): 1-14.

[116] K. Liu and A. Bellet, “Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds,” Neurocomputing 333 (2019): 185-199.

[117] H. Liu, “Evolving Feature Selection,” IEEE Intelligent Systems 20 (2005): 64-76.

[118] M. Greenacre, P. J. F. Groenen, T. Hastie, A. I. D'Enza, A. Markos, and E. Tuzhilina, “Principal Component Analysis,” Nature Reviews Methods Primers 2 (2022): 100.

[119] C.-Y. Liou, W.-C. Cheng, J.-W. Liou, and D.-R. Liou, “Autoencoder for Words,” Neurocomputing 139 (2014): 84-96.

[120] H. Eldeeb and R. Elshawi, “Empowering Machine Learning with Scalable Feature Engineering and Interpretable AutoML,” IEEE Transactions on Artificial Intelligence 6 (2025): 432-447.

[121] A. C. Belkina, C. O. Ciccolella, R. Anno, R. Halpert, J. Spidlen, and J. E. Snyder-Cappione, “Automated Optimized Parameters for T-distributed Stochastic Neighbor Embedding Improve Visualization and Analysis of Large Datasets,” Nature Communications 10 (2019): 5415.

[122] T. Smets, N. Verbeeck, M. Claesen, et al., “Evaluation of Distance Metrics and Spatial Autocorrelation in Uniform Manifold Approximation and Projection Applied to Mass Spectrometry Imaging Data,” Analytical Chemistry 91 (2019): 5706-5714.

[123] T. Kohonen, “The Self-Organizing Map,” Proceedings of the IEEE 78 (1990): 1464-1480.

[124] X. Zheng, C. Wang, C. Kong, C. Liu, K. Zhan, and R. Xu, “Deep Learning Approach for Hydraulic Support Anomaly Detection: Utilizing Convolutional Autoencoders and Dynamic Time Warping Technology,” Rock Mechanics and Rock Engineering 57 (2024): 11367-11379.

[125] Z. Zhu, Y. Zhang, Z. Wang, W. Tang, J. Wang, and J. Gong, “Artificial Intelligence Assisted Pharmaceutical Crystallization,” Crystal Growth & Design 24 (2024): 4245-4270.

[126] B. Y. Shekunov and P. York, “Crystallization Processes in Pharmaceutical Technology and Drug Delivery Design,” Journal of Crystal Growth 211 (2000): 122-136.

[127] F. Tian, N. Sandler, J. Aaltonen, et al., “Influence of Polymorphic Form, Morphology, and Excipient Interactions on the Dissolution of Carbamazepine Compacts,” Journal of Pharmaceutical Sciences 96 (2007): 584-594.

[128] A. Johnston and D. W. Holt, “Substandard Drugs: A Potential Crisis for Public Health,” British Journal of Clinical Pharmacology 78 (2014): 218-243.

[129] J. Cornel, C. Lindenberg, and M. Mazzotti, “Quantitative Application of in Situ ATR-FTIR and Raman Spectroscopy in Crystallization Processes,” Industrial & Engineering Chemistry Research 47 (2008): 4870-4882.

[130] A. Bluma, T. Höpfner, G. Rudolph, et al., “Adaptation of in-situ Microscopy for Crystallization Processes,” Journal of Crystal Growth 311 (2009): 4193-4198.

[131] J. Schneider and J. P. Handali, “Personalized Explanation for Machine Learning: A Conceptualization,” in 27th European Conference on Information Systems (ECIS 2019): Information Systems for a Sharing Society, Stockholm and Uppsala, Sweden (2019), 171, Research Paper.

[132] M. Du, N. Liu, and X. Hu, “Techniques for Interpretable Machine Learning,” Communications of the ACM 63 (2019): 68-77.

[133] C. Apté and S. Weiss, “Data Mining With Decision Trees and Decision Rules,” Future Generation Computer Systems 13 (1997): 197-210.

[134] L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, “Explaining Explanations: An Overview of Interpretability of Machine Learning,” in 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy (2018), 80-89.

[135] D. Garreau and U. Von Luxburg, “Explaining the Explainer: A First Theoretical Analysis of LIME,” in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Proceedings of Machine Learning Research, vol. 108, (2020): 1287-1296.

[136] Y. Kim and Y. Kim, “Explainable Heat-related Mortality With Random Forest and SHapley Additive exPlanations (SHAP) Models,” Sustainable Cities and Society 79 (2022): 103677.

[137] D. Vale, A. El-Sharif, and M. Ali, “Explainable Artificial Intelligence (XAI) Post-hoc Explainability Methods: Risks and Limitations in Non-discrimination Law,” AI and Ethics 2 (2022): 815-826.

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