Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy. Jiang, W., Han, G., Via, B. K., Tu, M., Liu, W., & Fasina, O. Wood Science and Technology, 48(1):109--122, January, 2014.
Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy [link]Paper  doi  abstract   bibtex   
The main objective of this research was to construct accurate near-infrared reflectance (NIR) models of wood chemistry. Wet chemistry procedures and high-performance liquid chromatography methods were employed to analyze the chemical composition of southern pine. The NIR spectra were collected from 21 wood samples, which were milled down to different particle size classes. NIR calibration and prediction models were established using two modeling methods with different pretreatments. Furthermore, the spectrum range used in the NIR models was refined to achieve higher prediction accuracy. Results showed that NIR model precision could be improved considerably by decreasing the particle size to a very fine powder coupled with a targeted spectrum range. Superior prediction models for lignin and holocellulose content were constructed, while models for extractives and cellulose contents were also strong.
@article{ jiang_rapid_2014,
  title = {Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy},
  volume = {48},
  issn = {0043-7719},
  url = {http://dx.doi.org/10.1007/s00226-013-0590-3},
  doi = {10.1007/s00226-013-0590-3},
  abstract = {The main objective of this research was to construct accurate near-infrared reflectance (NIR) models of wood chemistry. Wet chemistry procedures and high-performance liquid chromatography methods were employed to analyze the chemical composition of southern pine. The NIR spectra were collected from 21 wood samples, which were milled down to different particle size classes. NIR calibration and prediction models were established using two modeling methods with different pretreatments. Furthermore, the spectrum range used in the NIR models was refined to achieve higher prediction accuracy. Results showed that NIR model precision could be improved considerably by decreasing the particle size to a very fine powder coupled with a targeted spectrum range. Superior prediction models for lignin and holocellulose content were constructed, while models for extractives and cellulose contents were also strong.},
  language = {English},
  number = {1},
  journal = {Wood Science and Technology},
  author = {Jiang, Wei and Han, Guangting and Via, Brian K. and Tu, Maobing and Liu, Wei and Fasina, Oladiran},
  month = {January},
  year = {2014},
  keywords = {BIOFUELS, Cellulose, Chemical composition, Chemicals, Chemistry, Lignins, Models, Particle size, Standards, Wood},
  pages = {109--122}
}

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