Rapid quantification of NaDCC for water purification tablets in commercial production using ATR-FTIR spectroscopy based on machine learning techniques

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dc.contributor.authorAsadi, Hamzehen
dc.contributor.authorO'Mahony, Tomen
dc.contributor.authorLambert, Julieen
dc.contributor.authorBrown, Kenneth N.en
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2023-03-31T13:42:27Z
dc.date.available2023-03-31T13:42:27Z
dc.date.issued2023-02-23en
dc.description.abstractAccurate, fast and simple quantitative analysis of solid dosage forms is required for efficient pharmaceutical manufacturing. A spectroscopic analysis in ATR-FTIR (Attenuated Total Reflection-Fourier Transform Infrared) mode was developed for NaDCC (Sodium dichloroisocyanurate) quantification. This fast and low-cost method can be used to quantify NaDCC solid dosage forms using ATR-FTIR in absorbance mode in conjunction with partial least squares. A simple sampling procedure is included in the proposed experiment by just dissolving the samples in deionized water. An algorithm pipeline is also included for data cleaning, such as outlier removal, scatter correction, scaling, and mapping of the sample’s spectrum to a NaDCC concentration. In addition, a simple model based on Beer’s law was evaluated on a sub-range of 1220−1830cm−1. Furthermore, a variable selection algorithm shows minimum excipient interference from the sample matrix in addition to visual analysis. A statistical analysis of the proposed method shows that it demonstrates a promising result with a regression coefficient of 0.996 (R2=0.996) and recovery range of 95.5%–107%. As a result of the positive correlation of ATR-FTIR with NaDCC concentration, and in conjunction with the proposed method, this can serve as a clean, fast, affordable and eco-friendly method for pharmaceutical analysis.en
dc.description.sponsorshipScience Foundation Ireland under Grant number 16/RC/3918 which is co-funded under the European Regional Development Fund.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAsadi, H., O'Mahony, T., Lambert, J. and Brown, K.N. (2023) ‘Rapid quantification of nadcc for water purification tablets in commercial production using atr-ftir spectroscopy based on machine learning techniques’, AICS 2022, in L. Longo and R. O’Reilly (eds) Artificial Intelligence and Cognitive Science, Communications in Computer and Information Science, vol 1662, Cham: Springer Nature Switzerland, pp. 106–120. https://doi.org/10.1007/978-3-031-26438-2_9en
dc.identifier.doi10.1007/978-3-031-26438-2_9en
dc.identifier.endpage120en
dc.identifier.isbn9783031264375en
dc.identifier.isbn9783031264382en
dc.identifier.issn1865-0929en
dc.identifier.issn1865-0937en
dc.identifier.issued1662en
dc.identifier.startpage106en
dc.identifier.urihttps://hdl.handle.net/10468/14348
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofCommunications in Computer and Information Scienceen
dc.relation.ispartofArtificial Intelligence and Cognitive Scienceen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were madeen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMachine learningen
dc.subjectATR-FTIRen
dc.subjectChemometricen
dc.titleRapid quantification of NaDCC for water purification tablets in commercial production using ATR-FTIR spectroscopy based on machine learning techniquesen
dc.typebook-chapteren
dc.typeConference itemen
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