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

Author

Ítalo P.CaliariaMárcio H.P.BarbosabSukarno O.FerreiracReinaldo F.Teófiloa

a

Departament of Chemistry, Universidade Federal de Viçosa, 36570-900 Viçosa, Minas Gerais, Brazil

b

Departament of Plant Science, Universidade Federal de Viçosa, 36570-900 Viçosa, Minas Gerais, Brazil

c

Departament of Physics, Universidade Federal de Viçosa, 36570-900 Viçosa, Minas Gerais, Brazil

Received 4 September 2016, Revised 30 November 2016, Accepted 2 December 2016, Available online 5 December 2016.

Highlights

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The determination of sugarcane biomass crystallinity from near infrared is proposed.

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The crystallinity of sugarcane biomass samples varied in a wide range of values.

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Ordered predictors selection algorithm improved the predictive ability of model.

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Ordered predictors selection was faster and more efficient than genetic algorithm.

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There was no correlation among any chemical property evaluated and crystallinity.

Abstract

A 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.

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