Optimising Terahertz Waveform Selection of a Pharmaceutical Film Coating Process Using Recurrent Network

Li, Xiaoran and Williams, Bryan and May, Robert K. and Zhong, Shuncong and Evans, Michael J. and Gladden, Lynn F. and Shen, Yao chun and Zeitler, J. Axel and Lin, Hungyen (2022) Optimising Terahertz Waveform Selection of a Pharmaceutical Film Coating Process Using Recurrent Network. IEEE Transactions on Terahertz Science and Technology. ISSN 2156-342X

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Abstract

In-line terahertz pulsed imaging (TPI) has been utilised to measure the film coating thickness of individual tablets during the coating process in a production-scale pan coater. A criteria-based waveform selection algorithm (WSA) was developed to select terahertz signals reflected from the surface of coating tablets and determine the coating thickness. Since the WSA uses many criteria thresholds to select terahertz waveforms of sufficiently high quality, it could reject some potential candidate tablet waveforms that are close but do not reach the threshold boundary. On the premise of the availability of large datasets, we aim to improve the efficiency of WSA with machine learning. This paper presents a recurrent neural network approach to optimise waveform selection. In comparison with the conventional method of WSA, our approach allows more than double the number of waveforms to be selected while maintain great agreement with off-line thickness measurement. Moreover, the processing time of waveform selection decreases so that it can be applied for real-time coating monitoring in the pharmaceutical industry, which leads more advancement on the quality control for the pharmaceutical film coating.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Terahertz Science and Technology
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
169047
Deposited By:
Deposited On:
20 Apr 2022 12:50
Refereed?:
Yes
Published?:
Published
Last Modified:
21 May 2022 00:55