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Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier
The ATR-FTIR quantitation of azithromycin in three products of commercial tablets was carried out on product specific quantitative regression models using powdered paracetamol as matrix modifier to overcome the variation of spectral response and influence of sample matrix. For each product, a PLS qu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040712/ https://www.ncbi.nlm.nih.gov/pubmed/36994414 http://dx.doi.org/10.1016/j.heliyon.2023.e14647 |
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author | Doan, Cao Son Bui, Van Trung Tong, Thi Thanh Vuong Le, Dinh Chi |
author_facet | Doan, Cao Son Bui, Van Trung Tong, Thi Thanh Vuong Le, Dinh Chi |
author_sort | Doan, Cao Son |
collection | PubMed |
description | The ATR-FTIR quantitation of azithromycin in three products of commercial tablets was carried out on product specific quantitative regression models using powdered paracetamol as matrix modifier to overcome the variation of spectral response and influence of sample matrix. For each product, a PLS quantitative regression model was established using training infrared spectra obtained from reference mixtures (reference powders with known mass content (%, w/w) of azithromycin mixed homogenously with paracetamol to have mass percentage of azithromycin over total mass of azithromycin and paracetamol (P(A)) from 30% to 70%). The spectral data were collected in wavenumber range depending on commercial product within the wavenumber zone from 1300 cm(−1) to 1750 cm(−1) to build quantitative regression models. To quantify azithromycin in any commercial batch of the same product, the homogenized sample powder was mixed with paracetamol to have mixtures with P(A) value about 50% to record infrared spectrum. The actual amount of azithromycin would then be calculated from spectral response of unknown sample and the pre-established quantitative regression model. Each quantitative regression model was validated according to the current requirements of ICH guideline Q2R1 and those of AOAC International in term of specificity, accuracy, precision, long-term robustness and reliability. The validation results proved that the quantitative regression models were accurate, precise, reliable and robust, able to provide quantitative results of azithromycin in tablets equivalent to those provided by official HPLC method of USP44. |
format | Online Article Text |
id | pubmed-10040712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100407122023-03-28 Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier Doan, Cao Son Bui, Van Trung Tong, Thi Thanh Vuong Le, Dinh Chi Heliyon Research Article The ATR-FTIR quantitation of azithromycin in three products of commercial tablets was carried out on product specific quantitative regression models using powdered paracetamol as matrix modifier to overcome the variation of spectral response and influence of sample matrix. For each product, a PLS quantitative regression model was established using training infrared spectra obtained from reference mixtures (reference powders with known mass content (%, w/w) of azithromycin mixed homogenously with paracetamol to have mass percentage of azithromycin over total mass of azithromycin and paracetamol (P(A)) from 30% to 70%). The spectral data were collected in wavenumber range depending on commercial product within the wavenumber zone from 1300 cm(−1) to 1750 cm(−1) to build quantitative regression models. To quantify azithromycin in any commercial batch of the same product, the homogenized sample powder was mixed with paracetamol to have mixtures with P(A) value about 50% to record infrared spectrum. The actual amount of azithromycin would then be calculated from spectral response of unknown sample and the pre-established quantitative regression model. Each quantitative regression model was validated according to the current requirements of ICH guideline Q2R1 and those of AOAC International in term of specificity, accuracy, precision, long-term robustness and reliability. The validation results proved that the quantitative regression models were accurate, precise, reliable and robust, able to provide quantitative results of azithromycin in tablets equivalent to those provided by official HPLC method of USP44. Elsevier 2023-03-18 /pmc/articles/PMC10040712/ /pubmed/36994414 http://dx.doi.org/10.1016/j.heliyon.2023.e14647 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Doan, Cao Son Bui, Van Trung Tong, Thi Thanh Vuong Le, Dinh Chi Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier |
title | Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier |
title_full | Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier |
title_fullStr | Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier |
title_full_unstemmed | Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier |
title_short | Novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier |
title_sort | novel approach for infrared spectroscopic quantitation of azithromycin in commercial tablets employing paracetamol as matrix modifier |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040712/ https://www.ncbi.nlm.nih.gov/pubmed/36994414 http://dx.doi.org/10.1016/j.heliyon.2023.e14647 |
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