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


Metadata FieldValueLanguage
dc.contributorLori Eckhardt, eckhalg@auburn.eduen_US
dc.creatorAcquah, Gifty E.
dc.creatorVia, Brian K.
dc.creatorBillor, Nedret
dc.creatorFasina, Oladiran O.
dc.creatorEckhardt, Lori G.
dc.date.accessioned2020-05-26T00:04:57Z
dc.date.available2020-05-26T00:04:57Z
dc.date.created2016
dc.identifier10.3390/s16091375en_US
dc.identifier.urihttp://hdl.handle.net/11200/49811
dc.description.abstractAs 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.en_US
dc.formatPDFen_US
dc.relation.ispartofSensorsen_US
dc.relation.ispartofseries1424-8220en_US
dc.rights© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectprocess optimizationen_US
dc.subjectbioeconomyen_US
dc.subjectforest biomassen_US
dc.subjectfourier transform infrared spectroscopyen_US
dc.subjectnear infrared spectroscopyen_US
dc.subjectlinear discriminant analysisen_US
dc.subjectprincipal component analysisen_US
dc.titleIdentifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysisen_US
dc.typeCollectionen_US
dc.type.genreJournal Article, Academic Journalen_US
dc.citation.volume16en_US
dc.citation.issue9en_US
dc.citation.spage1375en_US
dc.description.peerreviewYesen_US
dc.creator.orcid0000-0003-4269-0903en_US

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