Development of near infrared spectroscopy models for the quantitative prediction of the lignocellulosic components of wet Miscanthus samples

[Display omitted] ▸ Models developed for lignocellulosic/elemental constituents based on NIR spectra. ▸ Different models covered wet and dry samples, different particle sizes. ▸ Models on dry, ground and sieved samples were the most accurate. ▸ Wet models suitable for quantification: total sugars, g...

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Bibliographic Details
Published in:Bioresource technology 2012-09, Vol.119, p.393-405
Main Author: Hayes, Daniel J.M.
Format: Article
Language:English
Subjects:
Algorithms
Ashes
Biorefining
Chemometrics
Computer Simulation
Constituents
Drying
Grounds
Lignin - analysis
Lignin - chemistry
Lignocellulose
Mathematical models
Miscanthus
Models, Chemical
Near infrared spectroscopy
Poaceae - chemistry
Refining
Spectra
Spectroscopy, Near-Infrared - methods
Wavelengths
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Summary:[Display omitted] ▸ Models developed for lignocellulosic/elemental constituents based on NIR spectra. ▸ Different models covered wet and dry samples, different particle sizes. ▸ Models on dry, ground and sieved samples were the most accurate. ▸ Wet models suitable for quantification: total sugars, glucose, xylose, Klason lignin. ▸ Other wet models suitable for classification or sample screening. Miscanthus samples were scanned over the visible and near infrared wavelengths at several stages of processing (wet-chopped, air-dried, dried and ground, and dried and sieved). Models were developed to predict lignocellulosic and elemental constituents based on these spectra. The dry and sieved scans gave the most accurate models; however the wet-chopped models for glucose, xylose, and Klason lignin provided excellent accuracies with root mean square error of predictions of 1.27%, 0.54%, and 0.93%, respectively. These models can be suitable for most applications. The wet models for arabinose, Klason lignin, acid soluble lignin, ash, extractives, rhamnose, acid insoluble residue, and nitrogen tended to have lower R2 values (0.80+) for the validation sets and the wet models for galactose, mannose, and acid insoluble ash were less accurate, only having value for rough sample screening. This research shows the potential for online analysis at biorefineries for the major lignocellulosic constituents of interest.
ISSN:0960-8524
1873-2976
DOI:10.1016/j.biortech.2012.05.137