Near-infrared spectroscopy for rapid compositional analysis of cellulose pulps after fractionation with ionic liquids
File(s)
Published version
Author(s)
Nisar, Suhaib
Barbará, Pedro Verdía
Chachuat, Benoît
Hallett, Jason P
Brandt-Talbot, Agnieszka
Type
Journal Article
Abstract
The composition of cellulose-enriched solids is typically monitored using a laborious and expensive wet-lab analytical method. Here, the development and application of an alternative tool that uses NIR spectroscopy and a software sensor is reported, drawing on a large data set (149 training samples) consisting of untreated grass, hardwood, and softwood biomass and cellulose pulps obtained after fractionation with the low-cost ionic liquids triethylammonium hydrogen sulfate ([TEA][HSO4]) or N,N-dimethylbutylammonium hydrogen sulfate ([DMBA][HSO4]) mixed with water. A partial least squares (PLS) model was trained on compositions determined with the traditional wet-lab procedure, followed by the application of an uncertainty quantification framework to estimate confidence in the predictions. Good agreement with the wet-lab experimental data (mean absolute errors on unseen samples below 5%) was found for ionic liquid fractionated cellulose and purified cellulose samples generated with non-ionoSolv approaches. Cellulose with low crystallinity and isolated lignins generated poor fits, suggesting that more specialised models are needed. The sugar-derived pseudo-lignin (humin) content in the cellulose pulp was estimated by comparing the model with a second PLS model that excluded charred (over-treated) pulps. The study shows that NIR soft-sensors can cost- and time-effectively estimate the composition of ionoSolv-based pulps, speeding up process and product development and facilitating process operation.
Date Issued
2025-10-01
Date Acceptance
2025-06-01
Citation
Biomass and Bioenergy, 2025, 201
ISSN
0961-9534
Publisher
Elsevier BV
Journal / Book Title
Biomass and Bioenergy
Volume
201
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.biombioe.2025.108056
Publication Status
Published
Article Number
108056
Date Publish Online
2025-06-23