Abstract
We have studied rapid calibration models to predict the composition of a variety of biomass feedstocks by correlating near-infrared (NIR) spectroscopic data to compositional data produced using traditional wet chemical analysis techniques. The rapid calibration models are developed using multivariate statistical analysis of the spectroscopic and wet chemical data. This work discusses the latest versions of the NIR calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Measures of the calibration precision and uncertainty are presented. No statistically significant differences (p = 0.05) are seen between NIR calibration models built using different mathematical pretreatments. Finally, two common algorithms for building NIR calibration models are compared; no statistically significant differences (p = 0.05) are seen for the major constituents glucan, xylan, and lignin, but the algorithms did produce different predictions for total extractives. A single calibration model combining the corn stover feedstock and dilute-acid pretreated corn stover samples gave less satisfactory predictions than the separate models.
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References
Barton FE, Kays SE (2001) Analytical application to fibrous foods and commodities. In: Williams P, Norris K (eds) Near-infrared technology in the agricultural and food industries. American association of cereal chemists, St. Paul
Burns DA, Ciurczak EW (2001) Handbook of near-infrared analysis, 2nd edn. Marcel Dekker, New York
Goulden CH (1952) Methods of statistical analysis, 2nd edn. John Wiley, New York
Hames B, Thomas S, Sluiter A, Roth C, Templeton D (2003) Rapid biomass analysis. Appl Biochem Biotechnol 105(1):5–16. doi:10.1385/ABAB:105:1-3:5
Hoskinson RL, Karlen DL, Birrell SJ, Radtke CW, Wilhelm WW (2007) Engineering, nutrient removal, and feedstock conversion evaluations of four corn stover harvest scenarios. Biomass Bioenergy 31(2):126–136. doi:10.1016/j.biombioe.2006.07.006
Martens H, Martens M (2001) Multivariate analysis of quality: an introduction. John Wiley, New York
Martens H, Naes T (1989) Multivariate calibration. John Wiley, Chichester
Naes T, Isaksson T, Fearn T, Davies T (2002) Selection of samples for calibration. In A user-friendly guide to multivariate calibration and classification, NIR Publications: Chichester, UK
Pordesimo LO, Hames BR, Sokhansanj S, Edens WC (2005) Variation in corn stover composition and energy content with crop maturity. Biomass Bioenergy 28(4):366–374. doi:10.1016/j.biombioe.2004.09.003
Sanderson MA, Agblevor F, Collins M, Johnson DK (1996) Compositional analysis of biomass feedstock by near infrared reflectance spectroscopy. Biomass Bioenergy 11(5):365–370. doi:10.1016/S0961-9534(96)00039-6
Shenk JS, Jerome J, Workman J, Westerhaus MO (2001) Application of NIR spectroscopy to agricultural products. In: Burns DA, Ciurczak EW (eds) Handbook of near-infrared analysis, 2nd edn. Marcel dekker, New York
Theander O, Aman P, Westerlund E, Andersson R, Pettersson D (1995) Total dietary fiber determined as neutral sugar residues, uronic acid residues, and Klason lignin (the Uppsala method). J Assoc Off Anal Chem Int 78(4):1030–1044
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This work was supported by the US Department of Energy under Contract No. DE-AC36-99GO10337 with the National Renewable Energy Laboratory.
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Wolfrum, E.J., Sluiter, A.D. Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover. Cellulose 16, 567–576 (2009). https://doi.org/10.1007/s10570-009-9320-2
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DOI: https://doi.org/10.1007/s10570-009-9320-2