Near infrared spectroscopy model for analyzing chemical composition of biomass subjected to Fenton oxidation and hydrothermal treatment
| Autor: | So-Yeon Jeong, Se-Eun Ban, Jae-Won Lee, Eun Ju Lee |
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| Rok vydání: | 2021 |
| Předmět: |
Materials science
060102 archaeology Correlation coefficient Renewable Energy Sustainability and the Environment 020209 energy Near-infrared spectroscopy Analytical chemistry Biomass 06 humanities and the arts 02 engineering and technology chemistry.chemical_compound chemistry Partial least squares regression 0202 electrical engineering electronic engineering information engineering Lignin 0601 history and archaeology Particle size Spectroscopy Chemical composition |
| Zdroj: | Renewable Energy. 172:1341-1350 |
| ISSN: | 0960-1481 |
| Popis: | Herein, near infrared (NIR) spectroscopy, rapid, accurate, and non-destructive method, was employed to analyze biomass composition. Calibration and prediction models for various types of biomass were developed from NIR data by applying the partial least squares method. Cellulose, hemicellulose, and lignin in a total of 75 samples were analyzed by a wet chemical method and NIR spectroscopy. The NIR model developed for hardwood accurately predicted the lignin content with a particle size of 20–80 mesh with a correlation coefficient (R2) of >0.95, low root mean square error (0.68), high ratio of error range (22.23), and high residual predictive deviation (6.07). On the other hand, the models for other compositions exhibited relatively low prediction accuracy. Different biomass particle sizes (20–80 mesh, >40 mesh, and |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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