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

José Luis Fernández, Felicia Sáez, Eulogio Castro, Paloma Manzanares, Mercedes Ballesteros and María José Negro
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José Luis Fernández: Biofuels Unit, Energy Department, Research Centre for Energy, Environment and Technology (CIEMAT), Complutense Av, 22, 28040 Madrid, Spain
Felicia Sáez: Biofuels Unit, Energy Department, Research Centre for Energy, Environment and Technology (CIEMAT), Complutense Av, 22, 28040 Madrid, Spain
Eulogio Castro: Department of Chemical, Environmental and Materials Engineering, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain
Paloma Manzanares: Biofuels Unit, Energy Department, Research Centre for Energy, Environment and Technology (CIEMAT), Complutense Av, 22, 28040 Madrid, Spain
Mercedes Ballesteros: Biofuels Unit, Energy Department, Research Centre for Energy, Environment and Technology (CIEMAT), Complutense Av, 22, 28040 Madrid, Spain
María José Negro: Biofuels Unit, Energy Department, Research Centre for Energy, Environment and Technology (CIEMAT), Complutense Av, 22, 28040 Madrid, Spain

Energies, 2019, vol. 12, issue 13, 1-10

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 offers 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 diffuse 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 R 2 cv values over 0.5, except for lignin which showed lowest prediction accuracy.

Keywords: lignocellulosic components; feedstock analysis; near-infrared spectroscopy; olive tree pruning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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