Záhonyi, Petra and Fekete, Dániel and Szabó, Edina and Nagy, Zsombor Kristóf and Nagy, Brigitta (2025) Explainable artificial neural network as a soft sensor to predict the moisture content in a continuous granulation line. EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES, 212. No.-107173. ISSN 0928-0987
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Abstract
The application of artificial neural networks (ANNs) has the potential to fundamentally change the pharmaceutical industry, making manufacturing more agile, robust, efficient and reliable. Although ANNs’ application as data-driven soft sensors has a particular potential, the black-box nature of most models creates mistrust and prevents their widespread application. Therefore, this study focuses on the development of an explainable ANN used as a soft sensor to monitor an integrated, continuous manufacturing process based on twin-screw granulation. Our goal was to estimate the moisture content, a critical quality attribute of granules only based on the applied process parameters without any direct measurements. Two separate ANNs – a multilayer perceptron (MLP) and a Nonlinear Autoregressive with Exogenous Inputs (NARX) – were built and compared with a near-infrared (NIR) spectra-based method. The validation of the methods – carried out by performing off-line loss-on-drying measurements – revealed that the accuracy of the ANNs and the NIR models was comparable, and the moisture content could be determined with a root mean square error of prediction below 1 % in all cases. Additionally, the explainability of an MLP was also investigated by SHAP analysis, revealing which parameters impacted the prediction and strength of their impact, making the technology transparent and providing valuable insight into the model. This study highlights the potential of ANNs applied as data-driven soft sensors, offering a viable, orthogonal alternative to traditional analytical methods that is cost-efficient and enhances process understanding.
| Item Type: | Article |
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| Additional Information: | This paper was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Science, the OTKA grant PD 142970, Doctoral Excellence Fellowship Programme (DCEP) and the EKÖP-24-3-BME-313 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. Project no. TKP-9-8/PALY-2021 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. The scientific research publicized in this article was reached with the sponsorship of Gedeon Richter Talentum Foundation in framework of Gedeon Richter Excellence PhD Scholarship of Gedeon Richter. |
| Uncontrolled Keywords: | Artificial neural networks; soft sensors; continuous manufacturing; Explainable AI (XAI); SHAP analysis; Twin-screw wet granulation; |
| Subjects: | R Medicine / orvostudomány > RM Therapeutics. Pharmacology / terápia, gyógyszertan |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 23 Sep 2025 13:10 |
| Last Modified: | 23 Sep 2025 13:10 |
| URI: | https://real.mtak.hu/id/eprint/224993 |
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