Skip to main navigation Skip to search Skip to main content

Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions

  • University of Limerick

Research output: Contribution to journalArticlepeer-review

Abstract

Analytical data and quantitative near infrared (NIR) spectroscopy models for various lignocellulosic components (including Klason lignin and the constituent sugars glucose, xylose, mannose, arabinose, galactose, and rhamnose), moisture, and ash were obtained for 53 peat samples. These included samples with high, medium, and low degrees of humification. Klason lignin was the main constituent and was greatest in the samples classified as being highly humified, with structural sugars the lowest in this class. The total sugars contents of all samples were considered to be insufficient to allow for their use in biorefining hydrolysis processes for the production of chemicals and biofuels. NIR models were developed for spectral datasets obtained from the samples in their unprocessed (wet), dry and unground, and dry and ground states. Typically the most accurate models were based on the spectra of dry and ground samples. However the NIR models for the wet samples still offered reasonable predictive capabilities. All models were suitable at least for sample screening, with the models for total sugars, glucose, xylose, galactose, and moisture suitable for quantitative analyses.

Original languageEnglish
Pages (from-to)261-268
Number of pages8
JournalFuel
Volume150
DOIs
Publication statusPublished - 15 Jun 2015

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Biorefining
  • Cellulose
  • Lignin
  • Near infrared
  • Peat
  • Rapid analysis

Fingerprint

Dive into the research topics of 'Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions'. Together they form a unique fingerprint.

Cite this