@article{MTMT:34759143,
	title = {Effects of omitting titanium dioxide from the film coating of a pharmaceutical tablet – An industrial case study of attempting to comply with EU regulation 2022/63},
	url = {https://m2.mtmt.hu/api/publication/34759143},
	author = {Galata, Dorián László and Sinka Lázárné, Melinda and Kiss-Kovács, Dorottya and Fülöp, Gergő and Dávid, Barnabás and Bogáti, Botond and Ficzere, Máté and Péterfi, Orsolya and Nagy, Brigitta and Marosi, György and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.ejps.2024.106750},
	journal-iso = {EUR J PHARM SCI},
	journal = {EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES},
	volume = {196},
	unique-id = {34759143},
	issn = {0928-0987},
	year = {2024},
	eissn = {1879-0720},
	orcid-numbers = {Marosi, György/0000-0002-4774-2023}
}

@article{MTMT:34759117,
	title = {UV–VIS imaging-based investigation of API concentration fluctuation caused by the sticking behaviour of pharmaceutical powder blends},
	url = {https://m2.mtmt.hu/api/publication/34759117},
	author = {Péterfi, Orsolya and Mészáros, Lilla Alexandra and Szabó-Szőcs, Bence and Ficzere, Máté and Sipos, Emese and Farkas, Attila and Galata, Dorián László and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.ijpharm.2024.124010},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {655},
	unique-id = {34759117},
	issn = {0378-5173},
	abstract = {Surface powder sticking in pharmaceutical mixing vessels poses a risk to the uniformity and quality of drug formulations. This study explores methods for evaluating the amount of pharmaceutical powder mixtures adhering to the metallic surfaces. Binary powder blends consisting of amlodipine and microcrystalline cellulose (MCC) were used to investigate the effect of the mixing order on the adherence to the vessel wall. Elevated API concentrations were measured on the wall and within the dislodged material from the surface, regardless of the mixing order of the components. UV imaging was used to determine the particle size and the distribution of the API on the metallic surface. The results were compared to chemical maps obtained by Raman chemical imaging. The combination of UV and VIS imaging enabled the rapid acquisition of chemical maps, covering a substantially large area representative of the analysed sample. UV imaging was also applied in tablet inspection to detect tablets that fail to meet the content uniformity criteria. The results present powder adherence as a possible source of poor content uniformity, highlighting the need for 100% inspection of pharmaceutical products to ensure product quality and safety.},
	year = {2024},
	eissn = {1873-3476},
	orcid-numbers = {Péterfi, Orsolya/0000-0002-1921-1452; Farkas, Attila/0000-0002-8877-2587}
}

@inproceedings{MTMT:34476285,
	title = {Droplet Based Prediction of Viscosity of Water-PVP Solutions Using Convolutional Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34476285},
	author = {Mrad, Mohamed Azouz and Csorba, Kristóf and Galata, Dorián László and Nagy, Zsombor Kristóf and Nagy, Brigitta},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2023 (AACS'23)},
	unique-id = {34476285},
	abstract = {The viscosity of a liquid is the property that measures the liquid internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields including chemical, pharmaceutical, food, and energy-related industries. The most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for industries where accurate and real-time viscosity information is vital. In this work, we prepared thirteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a Convolutional neural network model to estimate the viscosity values of the WaterPVP solutions. The proposed model was able to predict the viscosity values of the samples using images of their droplets with a high accuracy on the test dataset.},
	year = {2023},
	pages = {1-15},
	orcid-numbers = {Mrad, Mohamed Azouz/0000-0001-5223-3680}
}

@article{MTMT:34453979,
	title = {Flow chemical laboratory practice for undergraduate students: synthesis of paracetamol},
	url = {https://m2.mtmt.hu/api/publication/34453979},
	author = {Rávai, Bettina and Orosz, János Máté and Péterfi, Orsolya and Galata, Dorián László and Bálint, Erika},
	doi = {10.1007/s41981-023-00303-y},
	journal-iso = {J FLOW CHEM},
	journal = {JOURNAL OF FLOW CHEMISTRY},
	unique-id = {34453979},
	issn = {2062-249X},
	abstract = {Generally, chemical engineering students get well acquainted with the batch synthesis of various active pharmaceutical ingredients, however, only tiny focus is provided to undergraduates on the topic of flow chemistry. In this paper, we report that students participating in the chemical engineering BSc course at the Budapest University of Technology and Economics were encouraged to perform the flow synthesis of paracetamol, a common pain painkiller. Two different synthetic routes for the continuous production of paracetamol were investigated and compared the batch and flow methods. Thus, these experiments allowed the students to discover flow chemistry for themselves under supervision: how to set up a flow system, how to carry out a reaction continuously, and to experience the advantages of flow chemistry over batch synthesis. In addition, students also got familiar with in-line Fourier transform infrared spectroscopy, as one of the reactions was monitored in real-time.},
	keywords = {PARACETAMOL; infrared spectroscopy; Flow chemistry; organic chemistry; chemical engineering},
	year = {2023},
	eissn = {2063-0212},
	orcid-numbers = {Bálint, Erika/0000-0002-5107-7089}
}

@article{MTMT:34342443,
	title = {Image-based simultaneous particle size distribution and concentration measurement of powder blend components with deep learning and machine vision},
	url = {https://m2.mtmt.hu/api/publication/34342443},
	author = {Ficzere, Máté and Péterfi, Orsolya and Farkas, Attila and Nagy, Zsombor Kristóf and Galata, Dorián László},
	doi = {10.1016/j.ejps.2023.106611},
	journal-iso = {EUR J PHARM SCI},
	journal = {EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES},
	volume = {191},
	unique-id = {34342443},
	issn = {0928-0987},
	year = {2023},
	eissn = {1879-0720},
	orcid-numbers = {Farkas, Attila/0000-0002-8877-2587}
}

@article{MTMT:34131207,
	title = {Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34131207},
	author = {Galata, Dorián László and Gergely, Szilveszter and Nagy, Rebeka and Slezsák, János and Ronkay, Ferenc György and Nagy, Zsombor Kristóf and Farkas, Attila},
	doi = {10.3390/ph16091243},
	journal-iso = {PHARMACEUTICALS-BASE},
	journal = {PHARMACEUTICALS},
	volume = {16},
	unique-id = {34131207},
	abstract = {In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.},
	year = {2023},
	eissn = {1424-8247},
	orcid-numbers = {Gergely, Szilveszter/0000-0003-1945-526X; Ronkay, Ferenc György/0000-0003-0525-1493; Farkas, Attila/0000-0002-8877-2587}
}

@article{MTMT:34101349,
	title = {In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging},
	url = {https://m2.mtmt.hu/api/publication/34101349},
	author = {Péterfi, Orsolya and Madarász, Lajos and Ficzere, Máté and Lestyán-Goda, Katalin and Záhonyi, Petra and Erdei, Gábor and Sipos, Emese and Nagy, Zsombor Kristóf and Galata, Dorián László},
	doi = {10.1016/j.ejps.2023.106563},
	journal-iso = {EUR J PHARM SCI},
	journal = {EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES},
	volume = {189},
	unique-id = {34101349},
	issn = {0928-0987},
	abstract = {This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100-2000 μm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real-time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool.},
	year = {2023},
	eissn = {1879-0720},
	orcid-numbers = {Erdei, Gábor/0000-0003-1584-3142; Nagy, Zsombor Kristóf/0000-0003-2651-7756}
}

@article{MTMT:34045465,
	title = {Droplet Based Estimation of Viscosity of Water–PVP Solutions Using Convolutional Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/34045465},
	author = {Mrad, Mohamed Azouz and Csorba, Kristóf and Galata, Dorián László and Nagy, Zsombor Kristóf and Charaf, Hassan},
	doi = {10.3390/pr11071917},
	journal-iso = {PROCESSES},
	journal = {PROCESSES},
	volume = {11},
	unique-id = {34045465},
	issn = {2227-9717},
	abstract = {The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water–PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations with the same composition with a low MSE score of 0.0243 and R2 score of 0.9576. The proposed method has potential applications in scenarios where real-time monitoring of liquid viscosity is required.},
	year = {2023},
	eissn = {2227-9717},
	orcid-numbers = {Mrad, Mohamed Azouz/0000-0001-5223-3680}
}

@article{MTMT:33804039,
	title = {Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles},
	url = {https://m2.mtmt.hu/api/publication/33804039},
	author = {Galata, Dorián László and Zsiros, Boldizsár and Knyihár, Gábor and Péterfi, Orsolya and Mészáros, Lilla Alexandra and Ronkay, Ferenc György and Nagy, Brigitta and Szabó, Edina and Nagy, Zsombor Kristóf and Farkas, Attila},
	doi = {10.1016/j.ijpharm.2023.123001},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {640},
	unique-id = {33804039},
	issn = {0378-5173},
	year = {2023},
	eissn = {1873-3476},
	orcid-numbers = {Zsiros, Boldizsár/0000-0003-1606-1441; Knyihár, Gábor/0000-0002-1915-1867; Ronkay, Ferenc György/0000-0003-0525-1493; Szabó, Edina/0000-0001-9616-5122; Farkas, Attila/0000-0002-8877-2587}
}

@article{MTMT:33672106,
	title = {Improvement of drug processability in a connected continuous crystallizer system using formulation additive},
	url = {https://m2.mtmt.hu/api/publication/33672106},
	author = {Tacsi, Kornélia and Stoffán, György Nimród and Galata, Dorián László and Pusztai, Éva and Gyürkés, Martin and Nagy, Brigitta and Szilágyi, Botond and Nagy, Zsombor Kristóf and Marosi, György and Pataki, Hajnalka},
	doi = {10.1016/j.ijpharm.2023.122725},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {635},
	unique-id = {33672106},
	issn = {0378-5173},
	year = {2023},
	eissn = {1873-3476},
	orcid-numbers = {Tacsi, Kornélia/0000-0001-8506-5062; Pusztai, Éva/0000-0002-1997-2630; Szilágyi, Botond/0000-0001-7777-9612; Pataki, Hajnalka/0000-0002-8103-0601}
}