Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

Abstract

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.

Publication DOI: https://doi.org/10.3390/molecules27020335
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Chemical Engineering & Applied Chemistry
College of Engineering & Physical Sciences > Aston Institute of Materials Research (AIMR)
College of Engineering & Physical Sciences
Additional Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Uncontrolled Keywords: Lignocelluloses,Macroalgae,Near-infrared spectroscopy,Rapid measurement,Variables selection,Analytical Chemistry,Chemistry (miscellaneous),Molecular Medicine,Pharmaceutical Science,Drug Discovery,Physical and Theoretical Chemistry,Organic Chemistry
Publication ISSN: 1420-3049
Last Modified: 19 Jun 2024 07:20
Date Deposited: 10 Jan 2022 09:34
Full Text Link:
Related URLs: https://www.mdp ... 0-3049/27/2/335 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-01-06
Accepted Date: 2021-12-31
Authors: Ai, Ning
Jiang, Yibo
Omar, Sainab
Wang, Jiawei (ORCID Profile 0000-0001-5690-9107)
Xia, Luyue
Ren, Jie

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