@article{MTMT:34771157,
	title = {Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets},
	url = {https://m2.mtmt.hu/api/publication/34771157},
	author = {Mészáros, Lilla Alexandra and Madarász, Lajos and Kádár, Szabina and Ficzere, Máté and Farkas, Attila and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.ijpharm.2024.124013},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {655},
	unique-id = {34771157},
	issn = {0378-5173},
	abstract = {Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by-design, and real-time release testing. The aim of this work is to develop a digital UV/VIS imaging-based system for predicting the in vitro dissolution of meloxicam-containing tablets. The alteration of the dissolution profiles of the samples required different levels of the critical process parameters, including compression force, particle size and content of the API. These process parameters were predicted non-destructively by multivariate analysis of UV/VIS images taken from the tablets. The dissolution profile prediction was also executed using solely the image data and applying artificial neural networks. The prediction error (RMSE) of the dissolution profile points was less than 5%. The alteration of the API content directly affected the maximum concentrations observed at the end of the dissolution tests. This parameter was predicted with a relative error of less than 10% by PLS models that are based on the color components of UV and VIS images. In conclusion, this paper presents a modern, non-destructive PAT solution for real-time testing of the dissolution of tablets. © 2024 The Author(s)},
	keywords = {meloxicam; Quality assessment; artificial neural network; Machine vision; Dissolution testing; Dissolution prediction},
	year = {2024},
	eissn = {1873-3476},
	orcid-numbers = {Farkas, Attila/0000-0002-8877-2587}
}

@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}
}

@article{MTMT:34004071,
	title = {AI-based analysis of in-line process endoscope images for real-time particle size measurement in a continuous pharmaceutical milling process},
	url = {https://m2.mtmt.hu/api/publication/34004071},
	author = {Madarász, Lajos and Mészáros, Lilla Alexandra and Köte, Á. and Farkas, Attila and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.ijpharm.2023.123060},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {641},
	unique-id = {34004071},
	issn = {0378-5173},
	year = {2023},
	eissn = {1873-3476},
	orcid-numbers = {Farkas, Attila/0000-0002-8877-2587}
}

@article{MTMT:33641694,
	title = {Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset},
	url = {https://m2.mtmt.hu/api/publication/33641694},
	author = {Nagy, Brigitta and Szabados-Nacsa, Ágnes and Fülöp, Gergő and Turák Nagyné, Anikó and Galata, Dorián László and Farkas, Attila and Mészáros, Lilla Alexandra and Nagy, Zsombor Kristóf and Marosi, György},
	doi = {10.1016/j.ijpharm.2023.122620},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {633},
	unique-id = {33641694},
	issn = {0378-5173},
	year = {2023},
	eissn = {1873-3476},
	orcid-numbers = {Farkas, Attila/0000-0002-8877-2587; Marosi, György/0000-0002-4774-2023}
}

@article{MTMT:32912550,
	title = {Real-time coating thickness measurement and defect recognition of film coated tablets with machine vision and deep learning},
	url = {https://m2.mtmt.hu/api/publication/32912550},
	author = {Ficzere, Máté and Mészáros, Lilla Alexandra and Kállai-Szabó, Nikolett and Kovács, Andrea and Antal, István and Nagy, Zsombor Kristóf and Galata, Dorián László},
	doi = {10.1016/j.ijpharm.2022.121957},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {623},
	unique-id = {32912550},
	issn = {0378-5173},
	abstract = {This paper presents a system, where images acquired with a digital camera are coupled with image analysis and deep learning to identify and categorize film coating defects and to measure the film coating thickness of tablets. There were 5 different classes of defective tablets, and the YOLOv5 algorithm was utilized to recognize defects, the accuracy of the classification was 98.2%. In order to characterize coating thickness, the diameter of the tablets in pixels was measured, which was used to measure the coating thickness of the tablets. The proposed system can be easily scaled up to match the production capability of continuous film coaters. With the developed technique, the complete screening of the produced tablets can be achieved in real-time resulting in the improvement of quality control.},
	year = {2022},
	eissn = {1873-3476},
	orcid-numbers = {Kállai-Szabó, Nikolett/0000-0002-8164-3993; Kovács, Andrea/0000-0003-4547-494X; Antal, István/0000-0002-5434-201X}
}

@article{MTMT:32803411,
	title = {UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks},
	url = {https://m2.mtmt.hu/api/publication/32803411},
	author = {Mészáros, Lilla Alexandra and Farkas, Attila and Madarász, Lajos and Bicsár, Rozália and Galata, Dorián László and Nagy, Brigitta and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.ijpharm.2022.121773},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {620},
	unique-id = {32803411},
	issn = {0378-5173},
	year = {2022},
	eissn = {1873-3476},
	orcid-numbers = {Farkas, Attila/0000-0002-8877-2587}
}

@article{MTMT:32674803,
	title = {Raman mapping-based non-destructive dissolution prediction of sustained-release tablets},
	url = {https://m2.mtmt.hu/api/publication/32674803},
	author = {Galata, Dorián László and Zsiros, Boldizsár and Mészáros, Lilla Alexandra and Nagy, Brigitta and Szabó, Edina and Farkas, Attila and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.jpba.2022.114661},
	journal-iso = {J PHARMACEUT BIOMED ANAL},
	journal = {JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS},
	volume = {212},
	unique-id = {32674803},
	issn = {0731-7085},
	abstract = {In this paper, the applicability of Raman chemical imaging for the non-destructive prediction of the in vitro dissolution profile of sustained-release tablets is demonstrated for the first time. Raman chemical maps contain a plethora of information about the spatial distribution and the particle size of the components, compression force and even polymorphism. With proper data analysis techniques, this can be converted into simple numerical information which can be used as input in a machine learning model. In our work, sustained-release tablets using hydroxypropyl methylcellulose (HPMC) as matrix polymer are prepared, the concentration and particle size of this component varied between samples. Chemical maps of HPMC are converted into histograms with two different methods, an approach based on discretizing concentration values and a wavelet analysis technique. These histograms are then subjected to Principal Component Analysis, the score value of the first two principal components was found to represent HPMC content and particle size. These values are used as input in Artificial Neural Networks which are trained to predict the dissolution profile of the tablets. As a result, accurate predictions were obtained for the test tablets (the average f2 similarity value is higher than 59 with both methods). The presented methodology lays the foundations of the analysis of far more extensive datasets acquired with the emerging fast Raman imaging technology.},
	year = {2022},
	eissn = {1873-264X},
	orcid-numbers = {Zsiros, Boldizsár/0000-0003-1606-1441; Szabó, Edina/0000-0001-9616-5122; Farkas, Attila/0000-0002-8877-2587}
}

@article{MTMT:32242466,
	title = {Indirect monitoring of ultralow dose API content in continuous wet granulation and tableting by machine vision},
	url = {https://m2.mtmt.hu/api/publication/32242466},
	author = {Ficzere, Máté and Mészáros, Lilla Alexandra and Madarász, Lajos and Novák, Márk and Nagy, Zsombor Kristóf and Galata, Dorián László},
	doi = {10.1016/j.ijpharm.2021.121008},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {607},
	unique-id = {32242466},
	issn = {0378-5173},
	abstract = {This paper presents new machine vision–based methods for indirect real-time quantification of ultralow drug content during continuous twin-screw wet granulation and tableting. Granulation was performed with a solution containing carvedilol (CAR) as API in the ultralow dose range (0.05 w/w% in the granule) and the addition of riboflavin (RI) as a coloured tracer. An in-line calibration in the range of 0.047–0.058 w/w% was prepared for the measurement of CAR concentration using colour analysis (CA) and particle size analysis (PSA), and the validation with HPLC resulted in respective relative errors of 2.62% and 2.30% showing great accuracy. To improve the technique, a second in-line calibration was conducted in a broader CAR concentration range of 0.039–0.063 w/w% utilizing only half the amount of RI (0.045 w/w%), while doubling the output of the granulation line to 2 kg/h, producing a relative error of 4.51% and 4.29%, respectively. Finally, it was shown that the CA technique can also be carried on to monitor the CAR content of tablets in the 42–62 μg dose range with a relative error of 5.20%. Machine vision was proven to be a potent indirect method for the in-line, determination and monitoring of ultralow API content during continuous manufacturing.},
	year = {2021},
	eissn = {1873-3476}
}

@article{MTMT:31861632,
	title = {Real-time release testing of dissolution based on surrogate models developed by machine learning algorithms using NIR spectra, compression force and particle size distribution as input data},
	url = {https://m2.mtmt.hu/api/publication/31861632},
	author = {Galata, Dorián László and Könyves, Zsófia and Nagy, Brigitta and Novák, Márk and Mészáros, Lilla Alexandra and Szabó, Edina and Farkas, Attila and Marosi, György and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.ijpharm.2021.120338},
	journal-iso = {INT J PHARM},
	journal = {INTERNATIONAL JOURNAL OF PHARMACEUTICS},
	volume = {597},
	unique-id = {31861632},
	issn = {0378-5173},
	year = {2021},
	eissn = {1873-3476},
	orcid-numbers = {Szabó, Edina/0000-0001-9616-5122; Farkas, Attila/0000-0002-8877-2587; Marosi, György/0000-0002-4774-2023}
}

@article{MTMT:31831440,
	title = {Continuous blending monitored and feedback controlled by machine vision-based PAT tool},
	url = {https://m2.mtmt.hu/api/publication/31831440},
	author = {Galata, Dorián László and Mészáros, Lilla Alexandra and Ficzere, Máté and Vass, Panna and Nagy, Brigitta and Szabó, Edina and Domokos, András and Farkas, Attila and Csontos, István and Marosi, György and Nagy, Zsombor Kristóf},
	doi = {10.1016/j.jpba.2021.113902},
	journal-iso = {J PHARMACEUT BIOMED ANAL},
	journal = {JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS},
	volume = {196},
	unique-id = {31831440},
	issn = {0731-7085},
	abstract = {In a continuous powder blending process machine vision is utilized as a Process Analytical Technology (PAT) tool. While near-infrared (NIR) and Raman spectroscopy are reliable methods in this field, measurements become challenging when concentrations below 2 w/w% are quantified. However, an active pharmaceutical ingredient (API) with an intense color might be quantified in even lower quantities by images recorded with a digital camera. Riboflavin (RI) was used as a model API with orange color, its Limit of Detection was found to be 0.015 w/w% and the Limit of Quantification was 0.046 w/w% using a calibration based on the pixel value of images. A calibration for in-line measurement of RI concentration was prepared in the range of 0.2-0.45 w/w%, validation with UV/VIS spectrometry showed great accuracy with a relative error of 2.53 %. The developed method was then utilized for a residence time distribution (RTD) measurement in order to characterize the dynamics of the blending process. Lastly, the technique was applied in real-time feedback control of a continuous powder blending process. Machine vision based direct or indirect API concentration determination is a promising and fast method with a great potential for monitoring and control of continuous pharmaceutical processes. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license},
	keywords = {POWDERS; near-infrared spectroscopy; Feedback control; Chemistry, Analytical; Machine vision; PAT; Process analytical technology; Continuous blending; API content measurement; Colored API},
	year = {2021},
	eissn = {1873-264X},
	orcid-numbers = {Ficzere, Máté/0000-0002-0024-7375; Vass, Panna/0000-0003-2206-565X; Nagy, Brigitta/0000-0002-5252-0468; Szabó, Edina/0000-0001-9616-5122; Domokos, András/0000-0003-1968-4679; Farkas, Attila/0000-0002-8877-2587; Csontos, István/0000-0002-6858-1388; Marosi, György/0000-0002-4774-2023}
}