NIR spectroscopy can evaluate the crystallinity and the tensile and burst strengths of nanocellulosic films
Keywords:
Crystallinity index, mechanical properties, nanocellulose, NIRS, Pinus sp.Abstract
The near infrared (NIR) spectroscopy presents itself as an interesting non-destructive test tool as it enables a fast, simple and reliable way for characterizing large samplings of biological materials in a short period of time. This work aimed to establish multivariate models to estimate the crystallinity indices and tensile and burst strength of cellulosic and nanocellulosic films through NIR spectroscopy. NIR spectra were recorded from the films before tensile and bursting strength, and crystallinity tests. Spectral information were correlated with reference values obtained by laboratory procedures through partial least square regression (PLS-R). The PLS-R model for estimating the crystallinity index presented a coefficient of determination in cross-validation (R2cv) of 0,94 and the ratio of performance to deviation (RPD) was 3,77. The mechanical properties of the films presented a high correlation with the NIR spectra: R2p = 0,85 (RPD = 2,23) for tensile and R2p = 0,93 (RPD = 3,40) for burst strength. The statistics associated to the models presented have shown that the NIR spectroscopy has the potential to estimate the crystallinity index and resistance properties of cellulose and nanocellulose films on in-line monitoring systems.Downloads
References
Alves, A.; Santos, A.; Perez, D.S.; Rodrigues, J.; Pereira, H.; Simões, R.; Schwanninger, M. 2007. NIR PLSR model selection for Kappa number prediction of maritime pine Kraft pulps. Wood Sci Technol 41:491-499.
Belini, U.L.; Hein, P.R.G.; Tomazello, M.F.O.; Rodrigues, J.C.; Chaix, G. 2011. Near infrared spectroscopy for estimating sugarcane bagasse content in medium density fiberboard. BioResources 6:1816-1829.
Blanco, M.; Villarroya, I. 2002. NIR spectroscopy: a rapid-response analytical tool. Trends anal. Chem 21:240-250.
Burns, D.A.; Ciurczak, E.W. 2008. Handbook of near-infrared analysis. 3.ed. CRC, Boca Raton. Déjardin, A.; Laurans, F.; Arnaud, D.; Breton, C.; Pilate, G.; Leple, J.C. 2010. Wood formation in Angiosperms. C. R. Biologies 333:325-334.
Esteves, B.; Marques, A.V.; Domingos, I.; Pereira, H. 2013. Chemical changes of heat treated Pine and Eucalypt wood monitored by FTIR. Maderas.Ciencia y tecnología 15(2): 245-258.
Fardim, P.; Ferreira, M.M.C.; Durán, N. 2002. Multivariate calibration for quantitative analysis of Eucalyptus Kraft pulp by NIR spectrometry. J Wood Chem Technol 22:67-81.
Fardim, P.; Ferreira, M.M.C.; Durán, N. 2005. Determination of Mechanical and Optical Properties of Eucalyptus Kraft Pulp by NIR Spectrometry and Multivariate Calibration. J Wood Chem Technol 25:267-279.
Fengel, D.; Wegener, G. 1984. Wood chemistry, ultrastructure, reactions. Walter de Gruyter, New York.
Fujimoto, T.; Kurata, Y.; Matsumoto, K.; Tsuchikawa, S. 2008. Application of near infrared spectroscopy for estimating wood mechanical properties of small clear and full length lumber specimens. J Near Infrared Spectrosc 16:529-537.
Fujimoto, T.; Yamamoto, H.; Tsuchikawa, S. 2007. Estimation of Wood Stiffness and Strength Properties of Hybrid Larch by Near-Infrared Spectroscopy. Appl Spectrosc 61:882-888.
Hein, P.R.G.; Campos, A.C.M.; Mendes, R.F.; Mendes, L.M.; Chaix, G. 2011. Evaluation of bio-based particleboards properties by near infrared spectroscopy. Eur J Wood Prod 69:431-442.
Hein, P.R.G.; Clair, B.; Brancheriau, L.; Chaix, G. 2010b. Predicting microfibril angle in Eucalyptus wood from different wood faces and surface qualities using near infrared spectra. J Near Infrared Spectrosc 18:455-464.
Hein, P.R.G.; Lima, J.T.; Chaix, G. 2010a. Effects of sample preparation on NIR spectroscopic estimation of chemical properties of Eucalyptus urophylla S.T. Blake wood. Holzforschung 64:45-54.
Hein, P.R.G.; Sá, V.A.; Bufalino, L.; Mendes, L.M. 2009. Calibrations based on near infrared spectroscopic data to estimate wood-cement panel properties. BioResources 4:1620-1634.
Henriksen, H.C.; Naes, T.; Rodbotten, R.; Aastveit, A. 2005. Simultaneous modelling of process variables and raw material properties as measured by NIR. A case study from cellulose production. Chemometr Intell Lab Systems 77:238-246.
Huang, J.; Xia, T.; Li A, Y.U.B.; Qing, L.; Tu, Y.; Zhang, W.; YI, Z.; Peng, L. 2012. A rapid and consistent near infrared spectroscopic assay for biomass enzymatic digestibility upon various physical and chemical pretreatments in Miscanthus. Bioresource Technology 121:274-281.
Iwamoto, S.; Kentaro, A.; Yano, H. 2008. The effect of hemicelluloses on wood pulp nanofibrillation and nanofiber network characteristics. Biomacromolecules 9:1022-1026.
Kelley, S.S.; Rials, T.G.; Snell, R.; Groom, L.H.; Sluiter, A. 2004. Use of near infrared spectroscopy to measure the chemical and mechanical properties of solid wood. Wood Sci Technol 38:257-276.
Kent, S.M.; Leichti, R.J.; Morrell, J.J.; Rosowsky, D.V.; Kelley, S.S. 2006. Analytical tools to predict changes in properties of oriented strandboard exposed to the fungus Postia placenta. Holzforschung 60:332-338.
Kollmann, F.F.P.; Coté, W.A. 1968. Principles of Wood science and technology. Springer-Verlag, New York.
Missoum, K.; Belgacem, M.N.; Bras, B. 2013. Nanofibrillated Cellulose Surface Modification: A Review. Materials 6:1745-1766.
Mitsui, K.; Inagaki, T.; Tsuchikawa, S. 2007. Near infrared spectroscopy assay for thermal treatment of wood. in: European conference on wood modification, Cardiff. Proceedings. Gwynedd: B.C. 3:335-343.
Nisgoski, S.; Carneiro, M.E.; Muñiz, G.I.B. 2015. Influencia de la granulometria de la muestra en la discriminación de especies de Salix por infrarrojo cercano influence of sample granulometry on discrimination of Salix species by near infrared. Maderas.Ciencia y tecnología 17(1):195-204.
Okan, O.T.; Deniz, I.; Tiryaki, S. 2015. Application of artificial neural networks for predicting tensile index and brightness in bleaching pulp. Maderas.Ciencia y tecnología 17(3):571-584.
Osborne, B.G; Fearn, T. 1986. Near Infrared Spectroscopy in Food Analysis. Longman Scientific and Technical, Harlow.
Pasquini, C. 2003. Near infrared spectroscopy: fundamentals, practical aspects and analytical applications. J Braz Chem Soc 14:198-219.
Pereira, H.; Santos, A.J.A.; Anjos, O. 2015. Fibre Morphological Characteristics of Kraft Pulps of Acacia melanoxylon Estimated by NIR-PLS-R Models. Materials 9(1):1-9.
Rials, T.G.; Kelley, S.S.; So, C.L. 2002. Use of advanced spectroscopic techniques for predicting the mechanical properties of wood composites. Wood Fiber Sci 34:398-407.
Rosso, S.; Muniz, G.I.B.; Matos , J.L.M.; Haselein, C.R.; Hein, P.R.G.; Lopes, M.C. 2013. Estimate of the density of Eucalyptus grandis W. Hill ex Maiden using near infrared spectroscopy. Cerne 19:647-652.
Samistraro, G.; Muniz, G.I.B. 2009. Previsão das propriedades físicas do papel Kraft por espectroscopia no infravermelho próximo (NIR) e regressão por mínimos quadrados parciais (PLS). Quím Nova 32:1422-1425.
Sánchez, I.G.; Ceca, J.L.G.; Olmo, J.G.; Buil, L.L.; Luque, R.L.; Prades,C. 2013. Aplicación de analisis de imagen y tecnología nirs a la evaluacion de la porosidad de planchas, láminas y tapones de corcho y su relacion con la calidad industrial. Maderas.Ciencia y tecnología 15(3):293-309.
Santos, A.J.A.; Anjos, O.; Simões, R.; Rodrigues, J.; Pereira, H. 2014. Kappa Number Prediction of Acacia melanoxylon Unbleached Kraft Pulps using NIR-PLSR Models with a Narrow interval of Variation. BioResources 9(4):6735-6744.
Savitzky, A.; Golay, M.J.E. 1964. Smoothing and differentiation of data by simplified leastsquares procedures. Anal Chem 36:1627-1639.
Schimleck, L.R.; Doran, J.C.; Rimbawanto, A. 2003. Near infrared spectroscopy for costeffective screening of foliar oil characteristics in a Melaleuca cajuputi breeding population. J Agric Food Chem 51:2433-2437.
Stelte, W.; Sanadi, A.R. 2009. Preparation and Characterization of Cellulose Nanofibers from Two Commercial Hardwood and Softwood Pulps. Ind Eng Chem Res 48:11211-11219.
Trafela, T.; Strlic, M.; Kolar, J.; Lichtblau, D.A.; Anders, M.; Mencigar, D.P.; Pihlar, B. 2007. Nondestructive Analysis and Dating of Historical Paper Based on IR Spectroscopy and Chemometric Data Evaluation. Anal Chem 79:6319-6323.
Tsuchikawa, S.; Schwanninger, M. 2013. A Review of Recent Near Infrared Research for Wood and Paper (Part 2). Appl. Spectrosc Rev 48:560-587.
Tsuchikawa, S.; Siesler, H.W. 2003a. Near-infrared spectroscopy monitoring of the diffusion process of deuterium-labeled molecules in wood. Part II: hardwood. Appl Spectrosc 57:675-681.
Tsuchikawa, S.; Siesler, H.W. 2003b. Near-infrared spectroscopy monitoring of the diffusion process of deuterium-labeled molecules in wood. Part I: softwood. Appl Spectrosc 57:667-674.
Venãs, T.M.; Rinnan, A. 2008. Determination of weight percent gain in solid wood modified with in situ cured furfuryl alcohol by near-infrared reflectance spectroscopy. Chemometr Intell Lab Systems 92:125-130.
Viana, L.C.; Trugilho, P.F.; Hein, P.R.G.; Lima, J.T.; Silva, J.R.M. 2009. Predicting morphological characteristics and basic density of Eucalyptus wood using the NIRS technique. Cerne 15:421-429.
Walker, J.C.F. 2006. Primary Wood Processing: Principles and Practice. 2.ed. Springer, New Zealand.
Westad, F.; Martens, H. 2000. Variable selection in near infrared spectroscopy based on significance testing in partial least square regression. J Near Infrared Spectrosc 8:117-124.
Williams, P.C. 2014. Tutorial: The RPD statistic: a tutorial note. NIR News 25: 22-26. doi: 10.1255/ nirn.1419.
Williams, P.C.; Sobering, D.C. 1993. Comparison of commercial near infrared transmittance and reflectance instruments for analysis of whole grains and seeds. J Near Infrared Spectrosc 1:25-33.
Workman, J.J.; Weyer, L. 2007. Practical Guide to Interpretive Near-Infrared Spectroscopy. CRC Press, Boca Raton.
