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dc.contributor.authorTahmasbian Ghahfarokhi, Imanen_US
dc.contributor.authorBai, Shahlaen_US
dc.contributor.authorWang, Yuzheen_US
dc.contributor.authorBoyd, Sueen_US
dc.contributor.authorZhou, Junen_US
dc.contributor.authorEsmaeilani, Royaen_US
dc.contributor.authorXu, Zhihongen_US
dc.date.accessioned2019-05-29T13:04:33Z
dc.date.available2019-05-29T13:04:33Z
dc.date.issued2018en_US
dc.identifier.issn0341-8162en_US
dc.identifier.doi10.1016/j.catena.2018.04.023en_US
dc.identifier.urihttp://hdl.handle.net/10072/379898
dc.description.abstractStudying C functional group distributions in decomposing litterfall samples is one of the common methods of studying litterfall decomposition processes. However, the methods of studying the C functional group distributions, such as 13C NMR spectroscopy, are expensive and time consuming and new rapid and inexpensive technologies should be sought. Therefore, this study examined whether laboratory-based hyperspectral image analysis can be used to predict C functional group distributions in decomposing litterfall samples. Hyperspectral images were captured from ground decomposing litterfall samples in the visible to near infrared (VNIR) spectral range of 400–1000 nm. Partial least-square regression (PLSR) and artificial neural network (ANN) models were used to correlate the VNIR reflectance data measured from the litterfall samples with their C functional group distributions determined using 13C NMR spectroscopy. The results showed that alkyl-C, O,N-alkyl-C, di-O-alkyl-C1, di-O-alkyl-C2, aryl-C1, aryl-C2 and carboxyl derivatives could be acceptably predicted using the PLSR model, with R2 values of 0.72, 0.73, 0.71, 0.74, 0.76, 0.75 and 0.63 and ratio of prediction to deviation (RPD) values of 1.86, 1.82, 1.78, 1.71, 1.90, 1.76 and 1.43, respectively. Predicted O,N-alkyl-C, di-O-alkyl-C1, di-O-alkyl-C2, aryl-C1 and aryl-C2 using the ANN model provided R2 values of 0.62, 0.68, 0.69, 0.82 and 0.67 and the RPDs of 1.54, 1.76, 1.52, 2.10 and 1.72, respectively. With the exception of aryl-C1, the PLSR model was more reliable than the ANN model for predicting C functional group distributions given limited amount of training data. Neither the PLSR nor the ANN model could predict the carbohydrate-C and O-aryl-C acceptably. Overall, laboratory-based hyperspectral imaging in combination with the PLSR modelling can be recommended for the analysis of C functional group distribution in the decomposing forest litterfall samples.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevieren_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofpagefrom18en_US
dc.relation.ispartofpageto27en_US
dc.relation.ispartofjournalCatenaen_US
dc.relation.ispartofvolume167en_US
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience not elsewhere classifieden_US
dc.subject.fieldofresearchGeologyen_US
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscienceen_US
dc.subject.fieldofresearchcode040699en_US
dc.subject.fieldofresearchcode0403en_US
dc.subject.fieldofresearchcode0406en_US
dc.titleUsing laboratory-based hyperspectral imaging method to determine carbon functional group distributions in decomposing forest litterfallen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dc.type.codeC - Journal Articlesen_US
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.description.versionPost-printen_US
gro.facultyGriffith Sciences, School of Environment and Scienceen_US
gro.rights.copyright© 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.en_US
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