Application of NIR Reflectance Spectroscopy on Rapid Determination of Moisture Content of Wood Pellets

Jaya Sundaram^1*, Sudhagar Mani¹, Chari V. K. Kandala², Ronald A. Holser³

¹College of Engineering, University of Georgia, Athens, USA.
²National Peanut Research Laboratory, Dawson, USA.
³Russell Research Center, Athens, USA.

DOI: 10.4236/ajac.2015.612088                

Abstract

NIR spectroscopy was used to measure the moisture concentration of wood pellets. Pellets were conditioned to various moisture levels between 0.63% and 14.16% (wet basis) and the moisture concentration was verified using a standard oven method. Samples from various moisture levels were separated into two groups, as calibration and validation sets. NIR absorption spectral data from 400 nm to 2500 nm with 0.5 nm intervals were collected using pellets within the calibration and validation sample sets. Spectral wavelength ranges were taken as independent variables and the MC of the pellets as the dependent variable for the analysis. Measurements were obtained on 30 replicates within each moisture level. Partial Least Square (PLS) analysis was performed on both raw and preprocessed spectral data of calibration set to determine the best calibration model based on Standard Error of Calibration (SEC) and coefficient of multiple determinations (R²). The PLS model that yielded the best fit was used to predict the moisture concentration of validation group pellets. Relative Percent Deviation (RPD) and Standard Error of Prediction (SEP) were calculated to validate goodness of fit of the prediction model. Baseline and Multiple Scatter Corrected (MSC) reflectance spectra with 1^st derivative model gave the highest RPD value of 4.46 and R² of 0.95. Also it’s SEP (0.670) and RMSEP (0.782) were less than the other models those had RPD value more than 3.0 with less number of factors. Therefore, this model was selected as the best model for moisture content prediction of wood pellets.

Keywords

Wood Pellets, NIR Reflectance Spectroscopy, Moisture Content, Partial Least Square, Relative Percent Deviation

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Pellet Fuels Institute (PFI) (2011) Pellet Fuels Institute Standard Specification for Residential/Commercial Densified Fuel. Report, Arlington. www.pelletheat.org
[2]	ASABE Standards (2008) S358.2: Moisture Measurement-Forages. ASABE, St. Joseph.
[3]	Cozzolino, D., Kwiatkowski, M.J., Dambergs, R.G., Cynkar, W.U., Janik, L.J., Skouroumounis, G. and Gishen, M. (2008) Analysis of Elements in Wine Using Near Infrared Spectroscopy and Partial Least Squares Regression. Talanta, 74, 711-716. http://dx.doi.org/10.1016/j.talanta.2007.06.045
[4]	Sundaram, J., Kandala, C.V.K., Butts, C.L. and Windham, W.R. (2010) Application of NIR Reflectance Spectroscopy on Determination of Moisture Content of In-Shell Peanuts: A Nondestructive Analysis. Transactions of ASABE, 53, 183-189. http://dx.doi.org/10.13031/2013.29485
[5]	Nimaiyar, S., Paulsen, M.R. and Nelson, R.L. (2004) Rapid Analysis of Fatty Acids in Soybeans Using FTNIR, ASABE Paper No. 046118, ASABE, St. Joseph, 15 p.
[6]	Pérez-Vich, B., Velasco, L. and Fernández-Martínez, J.M. (1998) Determination of Seed Oil Content and Fatty Acid Composition in Sunflower through the Analysis of Intact Seeds, Husked Seeds, Meal and Oil by Near-Infrared Reflectance Spectroscopy. JAOCS, 75, 547-555. http://dx.doi.org/10.1007/s11746-998-0064-1
[7]	Lestander, T.A. and Rhen, C. (2005) Multivariate NIR Spectroscopy Models for Moisture, Ash and Calorific Content in Biofuels Using Bi-Orthogonal Partial Least Squares Regression. Analyst, 130, 1182e9.
[8]	Daun, J.K., Clear, K.M. and Williams, P. (1994) Comparison of Three Whole Seed Near Infrared Analyzers for Measuring Quality Components of Canola Seed. Journal of the American Oil Chemists’ Society, 71, 1063-1068. http://dx.doi.org/10.1007/BF02675897
[9]	Bhatty, R.S. (1991) Measurement of Oil in Whole Flaxseed by Near-Infrared Reflectance Spectroscopy, JAOCS, 68, 34-38. http://dx.doi.org/10.1007/BF02660306
[10]	Fagan, C.C., Everard, C.D. and McDonnell, K. (2011) Prediction of Moisture, Calorific Value, Ash and Carbon Content of Two Dedicated Bioenergy Crops Using Near-Infrared Spectroscopy. Bioresource Technology, 102, 5200e6.
[11]	Velasco, L. and Becker, H.C. (1998) Analysis of Total Glucosinolate Content and Individual Glucosinolates in Brassica spp. by Near-Infrared Reflectance Spectroscopy. Plant Breeding, 117, 97-102. http://dx.doi.org/10.1111/j.1439-0523.1998.tb01459.x
[12]	Lestander, T.A., Johnsson, B. and Grothage, M. (2009) NIR Techniques Create Added Values for the Pellet and Biofuel Industry. Bioresource Technology, 100, 1589-1594. http://dx.doi.org/10.1016/j.biortech.2008.08.001
[13]	Yeh, T.F., Yamada, T., Capanema, E., Chang, H.M., Chiang, V. and Kadla, J.F. (2005) Rapid Screening of Wood Chemical Component Variations Using Transmittance Near-Infrared Spectroscopy. Journal of Agricultural and Food Chemistry, 53, 3328-3332. http://dx.doi.org/10.1021/jf0480647
[14]	So, C.L. and Eberhardt, T.L. (2010) Chemical and Calorific Characterization of Longleaf Pine Using near Infrared Spectroscopy. Journal of Near Infrared Spectroscopy, 18, 417-423. http://dx.doi.org/10.1255/jnirs.889
[15]	Uner, B., Karaman, I., Tanriverdi, H. and Ozdemir, D. (2011) Determination of Lignin and Extractive Content of Turkish Pine (Pinus brutia Ten.) Trees Using near Infrared Spectroscopy and Multivariate Calibration. Wood Science and Technology, 45, 121-134. http://dx.doi.org/10.1007/s00226-010-0312-z
[16]	Fearn, T. (2002) Assessing Calibrations: SEP, RPD, RER and R2. NIR News, 13, 12. http://dx.doi.org/10.1255/nirn.689
[17]	Williams, P.C. (2001) Implementation of Near-Infrared Technology. In: Williams, P.C. and Norris, K., Eds., Near-Infrared Technology in the Agricultural and Food Industries, 2nd Edition, American Association of Cereal Chemists, St. Paul, 145-169.
[18]	Buning-Pfaue, H. (2003) Analysis of Water in Food by Near-Infrared Spectroscopy. Food Chemistry, 82, 107-115. http://dx.doi.org/10.1016/S0308-8146(02)00583-6
[19]	Osborne, B.G., Fearn, T. and Hindle, P.H. (1993) Practical NIR Spectroscopy with Applications in Food and Beverage Analysis. Longman Scientific and Technical, Harlow, UK.
[20]	Burns, D.A. and Ciurczak, E.W. (2001) Handbook of Near-Infrared Analysis. Revised and Expanded, 2nd Edition, Marcel Dekker, Inc., New York.
[21]	Michell, A.J. and Schimleck, L.R. (1996) NIR Spectroscopy of Woods from Eucalyptus globules. Appita Journal, 49, 23-26.
[22]	Schwanninger, M., Rodrigues, J.C. and Fackler, K. (2011) A Review of Band Assignments in Near-Infrared Spectra of Wood and Wood Components. Journal of Near Infrared Spectroscopy, 19, 287-308. http://dx.doi.org/10.1255/jnirs.955
[23]	Ali, M., Emsley, A.M., Herman, H. and Heywood, R.J. (2001) Spectroscopic Studies of the Ageing of Cellulosic Paper. Polymer, 42, 2893-2900. http://dx.doi.org/10.1016/S0032-3861(00)00691-1
[24]	Sun, B.L., Liu, J.L., Liu, S.J. and 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. http://dx.doi.org/10.1515/hf.2011.075
[25]	Bassett, K.H., Liang, C.Y. and Marchessault, R.H. (1963) Infrared Spectrum of Crystalline Polysaccharides. IX. The Near-Infrared Spectrum of Cellulose. Journal of Polymer Science Part A, 1, 1687-1692. http://dx.doi.org/10.1002/pol.1963.100010520
[26]	Marvin, R.P. and Singh, M. (2004) Calibration of a Near-Infrared Transmission Grain Analyzer for Extractable Starch in Maize. Biosystems Engineering, 89, 79-83. http://dx.doi.org/10.1016/j.biosystemseng.2004.05.009
[27]	Inoue, A., Kojima, K., Taniguchi, Y. and Suzuki, K. (1984) Near-Infrared Spectra of Water and Aqueous Electrolyte Solutions at High Pressures. Journal of Solution Chemistry, 13, 811-823. http://dx.doi.org/10.1007/BF00647696
[28]	Gillon, D., Henando, C., Valette, J.C. and Joffre, I. (1997) Fast Estimation of the Calorific Values of Forest Fuels by Near-Infrared Reflectance Spectroscopy. Canadian Journal of Forest Research, 27, 760-765. http://dx.doi.org/10.1139/x97-021