Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy

Abstract

The determination of chemical composition of lignocellulose biomass by wet chemistry

analysis is labor-intensive, expensive, and time consuming. Near infrared (NIR) spectroscopy coupled

with multivariate calibration o ers a rapid and no-destructive alternative method. The objective of

this work is to develop a NIR calibration model for olive tree lignocellulosic biomass as a rapid tool

and alternative method for chemical characterization of olive tree pruning over current wet methods.

In this study, 79 milled olive tree pruning samples were analyzed for extractives, lignin, cellulose,

hemicellulose, and ash content. These samples were scanned by reflectance di use near infrared

techniques and a predictive model based on partial least squares (PLS) multivariate calibration

method was developed. Five parameters were calibrated: Lignin, cellulose, hemicellulose, ash, and

extractives. NIR models obtained were able to predict main components composition with R2

values over 0.5, except for lignin which showed lowest prediction accuracy.