Estimation of cellulose crystallinity of sugarcane biomass using near infrared spectroscopy and multivariate analysis methods

dc.creatorCaliari, Ítalo P.
dc.creatorBarbosa, Márcio H.P.
dc.creatorFerreira, Sukarno O.
dc.creatorTeófilo, Reinaldo F.
dc.date2018-03-26T14:50:29Z
dc.date2018-03-26T14:50:29Z
dc.date2016-12-05
dc.date.accessioned2023-09-27T21:27:15Z
dc.date.available2023-09-27T21:27:15Z
dc.identifier01448617
dc.identifierhttps://doi.org/10.1016/j.carbpol.2016.12.005
dc.identifierhttp://www.locus.ufv.br/handle/123456789/18483
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8960883
dc.descriptionA method for estimation of sugarcane (Saccharum spp.) biomass crystallinity using near infrared spectroscopy (NIR) and partial least squares regression (PLS) as an alternative to the standard method using X-ray diffractometry (XRD) is proposed. Crystallinity was obtained using XRD from sugarcane bagasse. NIR spectra were obtained of the same material. PLS models were built using the NIR and crystallinity values. Cellulose crystallinity ranged from 50 to 81%. Two variable selection algorithms were applied to improve the predictive ability of models, i.e. (a) Ordered Predictors Selection (OPS) and (b) Genetic Algorithm. The best model, obtained with the OPS algorithm, presented values of correlation coefficient of prediction, root mean squared error of prediction and ratio of performance deviation equals to 0.92, 3.01 and 1.71, respectively. A scatter matrix among lignin, α-cellulose, hemicellulose, ash and crystallinity was built that showed that there was no correlation among these properties for the samples studied.
dc.formatpdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCarbohydrate Polymers
dc.relationv. 158, p. 20-28, February 2017
dc.rightsElsevier Ltd. All rights reserved.
dc.subjectCrystallinity
dc.subjectSugarcane
dc.subjectPLS
dc.subjectNIR
dc.subjectXRD
dc.subjectOPS
dc.titleEstimation of cellulose crystallinity of sugarcane biomass using near infrared spectroscopy and multivariate analysis methods
dc.typeArtigo


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