NIR SPECTROSCOPY APPLIED TO THE CHARACTERIZATION AND SELECTION OF PRE-TREATED MATERIALS FROM MULTIPLE LIGNOCELLULOSIC RESOURCES FOR BIOETHANOL PRODUCTION
Lignocellulosic biomass (LB) has been recognized as potential raw for bioethanol production. To facility LB bioconversion a pretreatment is applied, followed by simultaneous or separated saccharification and fermentation (SSF or SHF, respectively) steps. Characterization of pretreated materials, nee...
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| Autores principales: | , , , , , |
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| Lenguaje: | English |
| Publicado: |
Sociedad Chilena de Química
2014
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| Materias: | |
| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072014000100022 |
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| Sumario: | Lignocellulosic biomass (LB) has been recognized as potential raw for bioethanol production. To facility LB bioconversion a pretreatment is applied, followed by simultaneous or separated saccharification and fermentation (SSF or SHF, respectively) steps. Characterization of pretreated materials, needed to evaluate their ethanol yields, involves laborious and destructive methodologies. Therefore, saccharification is also time consuming and expensive step and some pretreated samples have not suitable characteristics to obtain high ethanol yields. Since bioethanol production aims to be a multivariable process respect to lignocellulosic resources, this work attempts to use NIR spectroscopy as alternative to wet chemical analysis to characterize samples from multiple pretreatments and lignocellulosic resources simultaneously and estimate their ethanol yield after a SSF process using multivariate calibration. Selection of suitable samples to obtain high ethanol yields using a classification method is also evaluated. Partial least squares (PLS) and discriminant partial least squares (PLS-DA) were used as calibration and classification techniques, respectively. Results showed ability of NIR spectroscopy to predict the chemical composition of samples and their ethanol yields, even if different lignocellulosic materials were used in the models, with low prediction errors and high correlation coefficients with reference methods (r>0,96) in PLS models and low misclassification rates (20- 30%) in classification models. Use of these models could facility the fast selection of high number of samples with suitable characteristics to obtain high ethanol yields and as predictive tool of these ethanol yields after a SSF process under controlled conditions. |
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