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dc.contributor.authorTahmasbian, Iman
dc.contributor.authorBai, Shahla Hosseini
dc.contributor.authorWang, Yuzhe
dc.contributor.authorBoyd, Sue
dc.contributor.authorZhou, Jun
dc.contributor.authorEsmaeilani, Roya
dc.contributor.authorXu, Zhihong
dc.date.accessioned2019-05-29T13:04:33Z
dc.date.available2019-05-29T13:04:33Z
dc.date.issued2018
dc.identifier.issn0341-8162
dc.identifier.doi10.1016/j.catena.2018.04.023
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofpagefrom18
dc.relation.ispartofpageto27
dc.relation.ispartofjournalCatena
dc.relation.ispartofvolume167
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience not elsewhere classified
dc.subject.fieldofresearchGeology
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchSoil Sciences
dc.subject.fieldofresearchcode040699
dc.subject.fieldofresearchcode0403
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0503
dc.titleUsing laboratory-based hyperspectral imaging method to determine carbon functional group distributions in decomposing forest litterfall
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionPost-print
gro.facultyGriffith Sciences, School of Environment and Science
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.
gro.hasfulltextFull Text
gro.griffith.authorBoyd, Sue E.
gro.griffith.authorXu, Zhihong
gro.griffith.authorZhou, Jun
gro.griffith.authorTahmasbian, Iman
gro.griffith.authorHosseini-Bai, Shahla


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