Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant AnalysisReportar como inadecuado


Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis


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1

Forest Products Development Center, School of Forestry and Wildlife Sciences, Auburn University, 520 Devall Drive, Auburn, AL 36849, USA

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Department of Mathematics and Statistics, Auburn University, Auburn, AL 36849, USA

3

Center for Bioenergy and Bioproducts, Department of Biosystems Engineering, Auburn University, 350 Mell Street, Auburn, AL 36849, USA

4

Forest Health Dynamics Laboratory, School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL 36849, USA





*

Author to whom correspondence should be addressed.



Academic Editor: Simon X. Yang

Abstract As new markets, technologies and economies evolve in the low carbon bioeconomy, forest logging residue, a largely untapped renewable resource will play a vital role. The feedstock can however be variable depending on plant species and plant part component. This heterogeneity can influence the physical, chemical and thermochemical properties of the material, and thus the final yield and quality of products. Although it is challenging to control compositional variability of a batch of feedstock, it is feasible to monitor this heterogeneity and make the necessary changes in process parameters. Such a system will be a first step towards optimization, quality assurance and cost-effectiveness of processes in the emerging biofuel-chemical industry. The objective of this study was therefore to qualitatively classify forest logging residue made up of different plant parts using both near infrared spectroscopy NIRS and Fourier transform infrared spectroscopy FTIRS together with linear discriminant analysis LDA. Forest logging residue harvested from several Pinus taeda loblolly pine plantations in Alabama, USA, were classified into three plant part components: clean wood, wood and bark and slash i.e., limbs and foliage. Five-fold cross-validated linear discriminant functions had classification accuracies of over 96% for both NIRS and FTIRS based models. An extra factor-principal component PC was however needed to achieve this in FTIRS modeling. Analysis of factor loadings of both NIR and FTIR spectra showed that, the statistically different amount of cellulose in the three plant part components of logging residue contributed to their initial separation. This study demonstrated that NIR or FTIR spectroscopy coupled with PCA and LDA has the potential to be used as a high throughput tool in classifying the plant part makeup of a batch of forest logging residue feedstock. Thus, NIR-FTIR could be employed as a tool to rapidly probe-monitor the variability of forest biomass so that the appropriate online adjustments to parameters can be made in time to ensure process optimization and product quality. View Full-Text

Keywords: process optimization; bioeconomy; forest biomass; fourier transform infrared spectroscopy; near infrared spectroscopy; linear discriminant analysis; principal component analysis process optimization; bioeconomy; forest biomass; fourier transform infrared spectroscopy; near infrared spectroscopy; linear discriminant analysis; principal component analysis





Autor: Gifty E. Acquah 1,* , Brian K. Via 1, Nedret Billor 2, Oladiran O. Fasina 3 and Lori G. Eckhardt 4

Fuente: http://mdpi.com/



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