Determination of aliphatic and phenolic hydroxyl groups in lignin by chemometric analysis of FTIR spectroscopic data, TAPPI Journal October 2025

 
ABSTRACT: Lignin’s potential as a source of sustainable aromatic compounds is significant, but its utilization is
currently limited by its chemical reactivity. Chemical reactivity of lignin depends on the present functional groups, such as hydroxyl, methoxy, and carbonyl groups. Therefore, in this study, multivariate analysis-based chemometric models have been developed for rapid determination of aliphatic hydroxyl (Alp-OH) and phenolic hydroxyl (Ph-OH) groups in lignin samples.

Two chemometric models, principal component regression (PCR) and partial least squares regression (PLSR), were established with Fourier transform infrared spectroscopy (FTIR) spectral data of 28 lignin samples. Both the models were developed based on raw and pretreated spectroscopic data with Savitky-Golay (S-G) filtering and standard normal variate (SNV) and multiplicative scatter correction (MSC). The predictive performance of the PLSRmodel is better for predicting Alp-OH (R2 = 0.94%), syringyl-OH (R2 = 0.96%), guaiacyl-OH (R2 = 0.98%), p-hydroxyphenyl (R2 = 0.93%), and total Ph-OH groups (R2 = 0.97%) with the data pretreated by MSC. Finally, the predicted results of these parameters for three new samples for the developed models are found to be very close to the estimated values by NMR.

 
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Determination of aliphatic and phenolic hydroxyl groups in l
ABSTRACT: Lignin’s potential as a source of sustainable aromatic compounds is significant, but its utilization is currently limited by its chemical reactivity. Chemical reactivity of lignin depends on the present functional groups, such as hydroxyl, methoxy, and carbonyl groups. Therefore, in this study, multivariate analysis-based chemometric models have been developed for rapid determination of aliphatic hydroxyl (Alp-OH) and phenolic hydroxyl (Ph-OH) groups in lignin samples. Two chemometric models, principal component regression (PCR) and partial least squares regression (PLSR), were established with Fourier transform infrared spectroscopy (FTIR) spectral data of 28 lignin samples. Both the models were developed based on raw and pretreated spectroscopic data with Savitky-Golay (S-G) filtering and standard normal variate (SNV) and multiplicative scatter correction (MSC). The predictive performance of the PLSR model is better for predicting Alp-OH (R2 = 0.94%), syringyl-OH (R2 = 0.96%), guaiacyl-OH (R2 = 0.98%), p-hydroxyphenyl (R2 = 0.93%), and total Ph-OH groups (R2 = 0.97%) with the data pretreated by MSC. Finally, the predicted results of these parameters for three new samples for the developed models are found to be very close to the estimated values by NMR.
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