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

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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)

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.

Suggested Citation

  • José Luis Fernández & Felicia Sáez & Eulogio Castro & Paloma Manzanares & Mercedes Ballesteros & María José Negro, 2019. "Determination of the Lignocellulosic Components of Olive Tree Pruning Biomass by Near Infrared Spectroscopy," Energies, MDPI, vol. 12(13), pages 1-10, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2497-:d:243789
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    References listed on IDEAS

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    1. Xu, Feng & Yu, Jianming & Tesso, Tesfaye & Dowell, Floyd & Wang, Donghai, 2013. "Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review," Applied Energy, Elsevier, vol. 104(C), pages 801-809.
    2. Mata Sánchez, Jesús & Pérez Jiménez, Jose Antonio & Díaz Villanueva, Manuel Jesús & Serrano, Antonio & Núñez, Nieves & López Giménez, Jesús, 2015. "Assessment of near infrared spectroscopy for energetic characterization of olive byproducts," Renewable Energy, Elsevier, vol. 74(C), pages 599-605.
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    1. Jeong, So-Yeon & Lee, Eun-Ju & Ban, Se-Eun & Lee, Jae-Won, 2021. "Near infrared spectroscopy model for analyzing chemical composition of biomass subjected to Fenton oxidation and hydrothermal treatment," Renewable Energy, Elsevier, vol. 172(C), pages 1341-1350.
    2. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).

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