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Investigating microcrystalline cellulose crystallinity using Raman spectroscopy
Microcrystalline cellulose (MCC) is a semi-crystalline material with inherent variable crystallinity due to raw material source and variable manufacturing conditions. MCC crystallinity variability can result in downstream process variability. The aim of this study was to develop models to determine...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550365/ https://www.ncbi.nlm.nih.gov/pubmed/34720465 http://dx.doi.org/10.1007/s10570-021-04093-1 |
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author | Queiroz, Ana Luiza P. Kerins, Brian M. Yadav, Jayprakash Farag, Fatma Faisal, Waleed Crowley, Mary Ellen Lawrence, Simon E. Moynihan, Humphrey A. Healy, Anne-Marie Vucen, Sonja Crean, Abina M. |
author_facet | Queiroz, Ana Luiza P. Kerins, Brian M. Yadav, Jayprakash Farag, Fatma Faisal, Waleed Crowley, Mary Ellen Lawrence, Simon E. Moynihan, Humphrey A. Healy, Anne-Marie Vucen, Sonja Crean, Abina M. |
author_sort | Queiroz, Ana Luiza P. |
collection | PubMed |
description | Microcrystalline cellulose (MCC) is a semi-crystalline material with inherent variable crystallinity due to raw material source and variable manufacturing conditions. MCC crystallinity variability can result in downstream process variability. The aim of this study was to develop models to determine MCC crystallinity index (%CI) from Raman spectra of 30 commercial batches using Raman probes with spot sizes of 100 µm (MR probe) and 6 mm (PhAT probe). A principal component analysis model separated Raman spectra of the same samples captured using the different probes. The %CI was determined using a previously reported univariate model based on the ratio of the peaks at 380 and 1096 cm(−1). The univariate model was adjusted for each probe. The %CI was also predicted from spectral data from each probe using partial least squares regression models (where Raman spectra and univariate %CI were the dependent and independent variables, respectively). Both models showed adequate predictive power. For these models a general reference amorphous spectrum was proposed for each instrument. The development of the PLS model substantially reduced the analysis time as it eliminates the need for spectral deconvolution. A web application containing all the models was developed. GRAPHIC ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10570-021-04093-1. |
format | Online Article Text |
id | pubmed-8550365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-85503652021-10-29 Investigating microcrystalline cellulose crystallinity using Raman spectroscopy Queiroz, Ana Luiza P. Kerins, Brian M. Yadav, Jayprakash Farag, Fatma Faisal, Waleed Crowley, Mary Ellen Lawrence, Simon E. Moynihan, Humphrey A. Healy, Anne-Marie Vucen, Sonja Crean, Abina M. Cellulose (Lond) Original Research Microcrystalline cellulose (MCC) is a semi-crystalline material with inherent variable crystallinity due to raw material source and variable manufacturing conditions. MCC crystallinity variability can result in downstream process variability. The aim of this study was to develop models to determine MCC crystallinity index (%CI) from Raman spectra of 30 commercial batches using Raman probes with spot sizes of 100 µm (MR probe) and 6 mm (PhAT probe). A principal component analysis model separated Raman spectra of the same samples captured using the different probes. The %CI was determined using a previously reported univariate model based on the ratio of the peaks at 380 and 1096 cm(−1). The univariate model was adjusted for each probe. The %CI was also predicted from spectral data from each probe using partial least squares regression models (where Raman spectra and univariate %CI were the dependent and independent variables, respectively). Both models showed adequate predictive power. For these models a general reference amorphous spectrum was proposed for each instrument. The development of the PLS model substantially reduced the analysis time as it eliminates the need for spectral deconvolution. A web application containing all the models was developed. GRAPHIC ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10570-021-04093-1. Springer Netherlands 2021-07-27 2021 /pmc/articles/PMC8550365/ /pubmed/34720465 http://dx.doi.org/10.1007/s10570-021-04093-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Queiroz, Ana Luiza P. Kerins, Brian M. Yadav, Jayprakash Farag, Fatma Faisal, Waleed Crowley, Mary Ellen Lawrence, Simon E. Moynihan, Humphrey A. Healy, Anne-Marie Vucen, Sonja Crean, Abina M. Investigating microcrystalline cellulose crystallinity using Raman spectroscopy |
title | Investigating microcrystalline cellulose crystallinity using Raman spectroscopy |
title_full | Investigating microcrystalline cellulose crystallinity using Raman spectroscopy |
title_fullStr | Investigating microcrystalline cellulose crystallinity using Raman spectroscopy |
title_full_unstemmed | Investigating microcrystalline cellulose crystallinity using Raman spectroscopy |
title_short | Investigating microcrystalline cellulose crystallinity using Raman spectroscopy |
title_sort | investigating microcrystalline cellulose crystallinity using raman spectroscopy |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550365/ https://www.ncbi.nlm.nih.gov/pubmed/34720465 http://dx.doi.org/10.1007/s10570-021-04093-1 |
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