Quantifying the Abundance of Alkane Moieties in Lignins with FTIR Spectroscopy and PLS Regression; Estimating Grafting Degree of Esterification

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Main Authors: Fredrik Heen Blindheim, Jost Ruwoldt
Format: Artículo Open Access
Published: Wiley 2024
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author Fredrik Heen Blindheim
Jost Ruwoldt
author_facet Fredrik Heen Blindheim
Jost Ruwoldt
Fredrik Heen Blindheim
Jost Ruwoldt
collection Wiley Open Access
contents Quantifying the Abundance of Alkane Moieties in Lignins with FTIR Spectroscopy and PLS Regression; Estimating Grafting Degree of Esterification Fredrik Heen Blindheim Jost Ruwoldt ChemSusChem AbstractAs society is rapidly converting from fossil‐based materials to greener alternatives, the valorization of lignin through chemical modification has been given considerable attention. Characterizing this highly heterogeneous biopolymer is a constant challenge, and an emerging strategy for dealing with variations in material characteristics is combining traditional analytical techniques with chemometrics, such as Fourier‐transform infrared (FTIR) spectroscopy with partial least squares regression (PLSR). Here, a calibration data set was built based on FTIR spectra and the total carbon‐hydrogen bond (CHB) content of mixtures of technical lignins and alkanes, meant to emulate esterified samples. From this data, a PLSR model was built which predicted the CHB content of esterified lignin reaction products with an RMSECV=5.685 mmol/g and RMSEPred=5.827 mmol/g, and from which the weight percentage of ester‐to‐lignin was determined. When compared to wet‐chemical analysis, good agreement between the techniques was found with an obtained RMSEPred=8.3 % and a R2Train=0.9752 for the degree of esterification. This indicates high model predictability and goodness of fit, and that the calibration data set successfully emulated esterified lignin samples. 10.1002/cssc.202400938 http://creativecommons.org/licenses/by-nc/4.0/
doi_str_mv 10.1002/cssc.202400938
format Artículo Open Access
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institution Wiley Open Access
license_str_mv http://creativecommons.org/licenses/by-nc/4.0/
publishDate 2024
publisher Wiley
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spellingShingle Quantifying the Abundance of Alkane Moieties in Lignins with FTIR Spectroscopy and PLS Regression; Estimating Grafting Degree of Esterification
Fredrik Heen Blindheim
Jost Ruwoldt
ChemSusChem
Quantifying the Abundance of Alkane Moieties in Lignins with FTIR Spectroscopy and PLS Regression; Estimating Grafting Degree of Esterification Fredrik Heen Blindheim Jost Ruwoldt ChemSusChem AbstractAs society is rapidly converting from fossil‐based materials to greener alternatives, the valorization of lignin through chemical modification has been given considerable attention. Characterizing this highly heterogeneous biopolymer is a constant challenge, and an emerging strategy for dealing with variations in material characteristics is combining traditional analytical techniques with chemometrics, such as Fourier‐transform infrared (FTIR) spectroscopy with partial least squares regression (PLSR). Here, a calibration data set was built based on FTIR spectra and the total carbon‐hydrogen bond (CHB) content of mixtures of technical lignins and alkanes, meant to emulate esterified samples. From this data, a PLSR model was built which predicted the CHB content of esterified lignin reaction products with an RMSECV=5.685 mmol/g and RMSEPred=5.827 mmol/g, and from which the weight percentage of ester‐to‐lignin was determined. When compared to wet‐chemical analysis, good agreement between the techniques was found with an obtained RMSEPred=8.3 % and a R2Train=0.9752 for the degree of esterification. This indicates high model predictability and goodness of fit, and that the calibration data set successfully emulated esterified lignin samples. 10.1002/cssc.202400938 http://creativecommons.org/licenses/by-nc/4.0/
title Quantifying the Abundance of Alkane Moieties in Lignins with FTIR Spectroscopy and PLS Regression; Estimating Grafting Degree of Esterification
topic ChemSusChem
url https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cssc.202400938