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Linkage Abundance and Molecular Weight Characteristics of Technical Lignins by Attenuated Total Reflection‐FTIR Spectroscopy Combined with Multivariate Analysis

Authors
  • Lancefield, Christopher S.1
  • Constant, Sandra1
  • de Peinder, Peter2
  • Bruijnincx, Pieter C. A.1, 1
  • 1 Utrecht University, The Netherlands , (Netherlands)
  • 2 VibSpec, The Netherlands , (Netherlands)
Type
Published Article
Journal
ChemSusChem
Publisher
Wiley (John Wiley & Sons)
Publication Date
Feb 01, 2019
Volume
12
Issue
6
Pages
1139–1146
Identifiers
DOI: 10.1002/cssc.201802809
PMID: 30641616
PMCID: PMC6563701
Source
PubMed Central
Keywords
License
Unknown
External links

Abstract

Lignin is an attractive material for the production of renewable chemicals, materials and energy. However, utilization is hampered by its highly complex and variable chemical structure, which requires an extensive suite of analytical instruments to characterize. Here, we demonstrate that straightforward attenuated total reflection (ATR)‐FTIR analysis combined with principle component analysis (PCA) and partial least squares (PLS) modelling can provide remarkable insight into the structure of technical lignins, giving quantitative results that are comparable to standard gel‐permeation chromatography (GPC) and 2D heteronuclear single quantum coherence (HSQC) NMR methods. First, a calibration set of 54 different technical (fractionated) lignin samples, covering kraft, soda and organosolv processes, were prepared and analyzed using traditional GPC and NMR methods, as well as by readily accessible ATR‐FTIR spectroscopy. PLS models correlating the ATR‐FTIR spectra of the broad set of lignins with GPC and NMR measurements were found to have excellent coefficients of determination ( R 2 Cal.>0.85) for molecular weight ( M n, M w) and inter‐unit abundances (β‐ O ‐4, β‐5 and β‐β), with low relative errors (6.2–14 %) as estimated from cross‐validation results. PLS analysis of a second set of 28 samples containing exclusively (fractionated) kraft lignins showed further improved prediction ability, with relative errors of 3.8–13 %, and the resulting model could predict the structural characteristics of an independent validation set of lignins with good accuracy. The results highlight the potential utility of this methodology for streamlining and expediting the often complex and time consuming technical lignin characterization process.

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