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Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies

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Abstract

Because plant cell walls vary in their polysaccharide compositions and lignin contents, their monosaccharide compositions and lignin contents are often determined, but these analyses are time consuming and laborious. We therefore investigated Fourier transform infrared (FTIR) spectroscopy coupled with partial least squares (PLS) regression analysis as a way of rapidly predicting the monosaccharide compositions and lignin contents of the cell walls of compression wood (CW) and opposite wood (OW) of the gymnosperm Pinus radiata. The effects were investigated of sample moisture content (ambient or dry) and sample particle size (large particles, < 0.422 mm or small particles, < 0.178 mm) of milled wood on attenuated total reflectance (ATR) and transmission FTIR spectra, as well as the PLS-1 models and subsequent predictions. PLS-1 models were built using mixtures of CW and OW as the training set, to provide a linear range of monosaccharide compositions and lignin contents. Models were externally validated by predicting another set of wood mixtures before predicting CW and OW of a separate test set. Most of the monosaccharide amounts in the separate test set were best predicted by ATR spectroscopy of ambient large particles, achieving the lowest standard error values for the monosaccharides arabinose (0.36%), xylose (1.05%), galactose (1.79%), glucose (6.32%), and 4-O-methylglucuronic acid (0.20%). The results show the feasibility of using ATR spectroscopy of ambient large particles for the rapid prediction of monosaccharide compositions and lignin contents of plant cell walls.

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Abbreviations

ATR FTIR:

Attenuated total reflectance Fourier transform infrared

CW:

Compression wood

NIPALS:

Non-linear iterative partial least squares

OW:

Opposite wood

PLS:

Partial least squares

RMSECV:

Root mean squared error of cross validation

RMSEP:

Root mean squared error of prediction

References

  • Åkerholm M, Salmén L (2001) Interactions between wood polymers studied by dynamic FT-IR spectroscopy. Polymer 42:963–969

    Article  Google Scholar 

  • Apiolaza LA, Butterfield B, Chauhan SS, Walker JCF (2011) Characterization of mechanically perturbed young stems: can it be used for wood quality screening? Ann For Sci 68:407–414

    Article  Google Scholar 

  • Araque E, Parra C, Freer J, Contreras D, Rodríguez J, Mendonça R, Baeza J (2008) Evaluation of organosolv pretreatment for the conversion of Pinus radiata D. Don to ethanol. Enzym Microb Technol 43:214–219

    Article  CAS  Google Scholar 

  • Baker MJ et al (2014) Using Fourier transform IR spectroscopy to analyze biological materials. Nat Protoc 9:1771–1791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Blakeney AB, Harris PJ, Henry RJ, Stone BA (1983) A simple and rapid preparation of alditol acetates for monosaccharide analysis. Carbohydr Res 113:291–299

    Article  CAS  Google Scholar 

  • Brennan M, McLean JP, Altaner CM, Ralph J, Harris PJ (2012) Cellulose microfibril angles and cell-wall polymers in different wood types of Pinus radiata. Cellulose 19:1385–1404

    Article  CAS  Google Scholar 

  • Chavan RR, Fahey LM, Harris PJ (2015) Quantification of (1 → 4)-β-d-galactans in compression wood using an immuno-dot assay. Plants 4:29–43. doi:10.3390/plants4010029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Currie HA, Perry CC (2006) Resolution of complex monosaccharide mixtures from plant cell wall isolates by high pH anion exchange chromatography. J Chromatogr 1128:90–96

    Article  CAS  Google Scholar 

  • Dence CW (1992) The determination of lignin. In: Lin SY, Dence CW (eds) Methods in lignin chemistry. Springer, Berlin, pp 33–61

    Chapter  Google Scholar 

  • Duca D, Pizzi A, Rossini G, Mengarelli C, Foppa Pedretti E, Mancini M (2016) Prediction of hardwood and softwood contents in blends of wood powders using mid-infrared spectroscopy. Energy Fuels 30:3038–3044

    Article  CAS  Google Scholar 

  • Faix O (1991) Classification of lignins from different botanical origins by FT-IR spectroscopy. Holzforschung 45:21–27

    Article  CAS  Google Scholar 

  • Faix O, Böttcher JH (1992) The influence of particle size and concentration in transmission and diffuse reflectance spectroscopy of wood. Holz Roh Werkst 50:221–226

    Article  CAS  Google Scholar 

  • Fengel D, Ludwig M (1991) Möglichkeiten und grenzen der FTIR-spektroskopie bei der charakterisierung von cellulose. Teil 1. Vergleich von verschiedenen cellulose-fasern und bakterien-cellulose. Das Papier 45:45–51

    CAS  Google Scholar 

  • Fukushima RS, Hatfield RD (2004) Comparison of the acetyl bromide spectrophotometric method with other analytical lignin methods for determining lignin concentration in forage samples. J Agric Food Chem 52:3713–3720

    Article  CAS  PubMed  Google Scholar 

  • Hair JFJ, Black WC, Babin BJ, Anderson RE (2010) Multivariate data analysis. A global perspective, 7th edn. Pearson, Upper Saddle River

    Google Scholar 

  • Harrington KJ, Higgins HG, Michell AJ (1964) Infrared spectra of Eucalyptus regnans F. Muell. and Pinus radiata D. Don. Holzforschung 18:108–113

    Article  CAS  Google Scholar 

  • Harris PJ (2005) Diversity in plant cell walls. In: Henry RJ (ed) Plant diversity and evolution: genotypic and phenotypic variation in higher plants. CABI Publishing, Wallingford, pp 201–227

    Chapter  Google Scholar 

  • Harris PJ, Stone BA (2008) Chemistry and molecular organization of plant cell walls. In: Himmel ME (ed) Biomass recalcitrance: deconstructing the plant cell wall for bioenergy. Blackwell Publishing Ltd., Oxford, pp 61–93

    Chapter  Google Scholar 

  • Hergert HL (1960) Infrared spectra of lignin and related compounds. II. Conifer lignin and model compounds. J Org Chem 25:405–413

    Article  CAS  Google Scholar 

  • Hergert HL (1971) Infrared spectra. In: Sarkanen KV, Ludwig CH (eds) Lignins. Occurrence, formation, structure and reactions. John Wiley and Sons, New York, pp 267–297

  • Higgins HG, Stewart CM, Harrington KJ (1961) Infrared spectra of cellulose and related polysaccharides. J Polym Sci 51:59–84

    Article  CAS  Google Scholar 

  • Himmel ME, Ding S-Y, Johnson DK, Adney WS, Nimlos MR, Brady JW, Foust TD (2007) Biomass recalcitrance: engineering plants and enzymes for biofuels production. Science 315:804–807

    Article  CAS  PubMed  Google Scholar 

  • John MJ, Thomas S (2008) Biofibres and biocomposites. Carbohydr Polym 71:343–364

    Article  CAS  Google Scholar 

  • Kačuráková M, Capek C, Sasinková V, Wellner N, Ebringerová A (2000) FT-IR study of plant cell wall model compounds: pectic polysaccharides and hemicelluloses. Carbohydr Polym 43:195–203

    Article  Google Scholar 

  • Kačuráková M, Wilson RH (2001) Developments in mid-infrared FT-IR spectroscopy of selected carbohydrates. Carbohydr Polym 44:291–303

    Article  Google Scholar 

  • Kato K, Nitta M, Mizuno T (1973) Infrared spectroscopy of some mannans. Agric Biol Chem 37:433–435

    Article  CAS  Google Scholar 

  • Kim H, Ralph J, Akiyama T (2008) Solution-state 2D NMR of ball-milled plant cell wall gels in DMSO-d 6. Bioenergy Res 1:56–66

    Article  Google Scholar 

  • Krasznai DJ, Champagne P, Cunningham MF (2012) Quantitative characterization of lignocellulosic biomass using surrogate mixtures and multivariate techniques. Bioresour Technol 110:652–661

    Article  CAS  PubMed  Google Scholar 

  • Liang CY, Marchessault RH (1959) Infrared spectra of crystalline polysaccharides. II. Native celluloses in the region from 640 to 1700 cm-1. J Polym Sci 39:269–278

    Article  CAS  Google Scholar 

  • Lupoi JS, Singh S, Simmons BA, Henry RJ (2013) Assessment of lignocellulosic biomass using analytical spectroscopy: an evolution to high-throughput techniques. Bioenergy Res 7:1–23

    Article  Google Scholar 

  • Marchessault RH (1962) Application of infra-red spectroscopy to cellulose and wood polysaccharides. Pure Appl Chem 5:107–130

    Article  CAS  Google Scholar 

  • Marchessault RH, Liang CY (1962) The infrared spectra of crystalline polysaccharides. VIII. Xylans. J Polym Sci 59:357–378

    Article  CAS  Google Scholar 

  • Mathlouthi M, Koenig JL (1987) Vibrational spectra of carbohydrates. Adv Carbohydr Chem Biochem 44:7–89

    Article  Google Scholar 

  • McLean JP, Jin G, Brennan M, Nieuwoudt MK, Harris PJ (2014) Using NIR and ATR-FTIR spectroscopy to rapidly detect compression wood in Pinus radiata. Can J For Res 44:820–830

    Article  CAS  Google Scholar 

  • Müller G, Schöpper C, Vos H, Kharazipour A, Polle A (2009) FTIR-ATR spectroscopic analyses of changes in wood properties during particle- and fibreboard production of hard- and softwood trees. BioResources 4:49–71

    Google Scholar 

  • Pauly M, Keegstra K (2008) Cell-wall carbohydrates and their modification as a resource for biofuels. Plant J 54:559–568

    Article  CAS  PubMed  Google Scholar 

  • Saeman JF, Moore WE, Millett MA (1963) Sugar units present. Hydrolysis and quantitative paper chromatography. In: Whistler RL (ed) Methods in carbohydrate chemistry, vol 3. Academic Press, New York, pp 54–69

    Google Scholar 

  • Scheller HV, Ulvskov P (2010) Hemicelluloses. Annu Rev Plant Biol 61:263–289

    Article  CAS  PubMed  Google Scholar 

  • Schwanninger M, Rodrigues JC, Pereira H, Hinterstoisser B (2004) Effects of short-time vibratory ball milling on the shape of FT-IR spectra of wood and cellulose. Vib Spectrosc 36:23–40

    Article  CAS  Google Scholar 

  • Smith BG, Harris PJ (1995) Polysaccharide composition of unlignified cell walls of pineapple [Ananas comosus (L.) Merr.] fruit. Plant Physiol 107:1399–1409

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sun B, Liu J, Liu S, Yang Q (2011) Application of FT-NIR-DR and FT-IR-ATR spectroscopy to estimate the chemical composition of bamboo (Neosinocalamus affinis Keng). Holzforschung 65:689–696

    Article  CAS  Google Scholar 

  • TAPPI T 222 om-98 (1998) Acid-insoluble lignin in wood and pulp

  • TAPPI T 249 cm-85 (1985) Carbohydrate composition of extractive-free wood and wood pulp by gas-liquid chromatography

  • TAPPI T 264 cm-97 (1997) Preparation of wood for chemical analysis

  • Timell TE (1986) Compression wood in gymnosperms. Springer, Berlin

    Book  Google Scholar 

  • Via BK, Fasina O, Pan H (2011) Assessment of pine biomass density through mid-infrared spectroscopy and multivariate modeling. BioResources 6:807–822

    CAS  Google Scholar 

  • Walker J (2013) Wood quality: a perspective from New Zealand. Forests 4:234–250. doi:10.3390/f4020234

    Article  Google Scholar 

  • Xu F, Yu J, Tesso T, Dowell F, Wang D (2013) Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: a mini-review. Appl Energy 104:801–809

    Article  CAS  Google Scholar 

  • Zhang M, Chavan RR, Smith BG, McArdle BH, Harris PJ (2016) Tracheid cell-wall structures and locations of (1 → 4)-β-d-galactans and (1 → 3)-β-d-glucans in compression woods of radiata pine (Pinus radiata D. Don). BMC Plant Biol 16:18. doi:10.1186/s12870-016-0884-3

    Article  CAS  Google Scholar 

  • Zhou G, Taylor G, Polle A (2011) FTIR-ATR-based prediction and modelling of lignin and energy contents reveals independent intra-specific variation of these traits in bioenergy poplars. Plant Methods 7:1–10. doi:10.1186/1746-4811-7-9

    Article  CAS  Google Scholar 

  • Zhou C, Jiang W, Via BK, Fasina O, Han G (2015) Prediction of mixed hardwood lignin and carbohydrate content using ATR-FTIR and FT-NIR. Carbohydr Polym 121:336–341

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We thank Professor John C. F. Walker (School of Forestry, University of Canterbury) for providing the wood samples, and Professor Christopher M. Triggs and Associate Professor Brian H. McArdle (Department of Statistics, University of Auckland) for statistical advice. This work was supported by the New Zealand Foundation for Research, Science and Technology (now Ministry of Business, Innovation and Employment) [PROJ-12401-PPS-UOC, “Compromised Wood Quality”].

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Authors and Affiliations

  1. School of Biological Sciences, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand

    Leona M. Fahey & Philip J. Harris

  2. School of Chemical Sciences, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand

    Michél K. Nieuwoudt

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  1. Leona M. Fahey
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  2. Michél K. Nieuwoudt
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  3. Philip J. Harris
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Correspondence to Philip J. Harris.

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Fahey, L.M., Nieuwoudt, M.K. & Harris, P.J. Predicting the cell-wall compositions of Pinus radiata (radiata pine) wood using ATR and transmission FTIR spectroscopies. Cellulose 24, 5275–5293 (2017). https://doi.org/10.1007/s10570-017-1506-4

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