Rapid Biomass Quality Determination of Corn Stover Using Near-Infrared Reflectance Spectroscopy

被引:0
作者
Patrick T. Murphy
Kenneth J. Moore
D. Raj Raman
Robert P. Anex
Steven L. Fales
机构
[1] Purdue University,Department of Agricultural and Biological Engineering
[2] Iowa State University,Department of Agronomy
[3] Iowa State University,Department of Agricultural and Biosystems Engineering
[4] University of Wisconsin Madison,Department of Biological Systems Engineering
来源
BioEnergy Research | 2012年 / 5卷
关键词
Biomass; Lignocellulosic feedstock; Feedstock quality; Near-infrared reflectance spectroscopy;
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中图分类号
学科分类号
摘要
Near-infrared reflectance spectroscopy (NIRS) has been used extensively in the forage industry for rapid measurement of forage constituents and could be useful for determining quality of biomass feedstocks at the point of delivery. In previous work, we developed an assay that partitions feedstock carbohydrates based on their availability to be converted to fermentable sugars, including non-structural carbohydrates (CN), biochemically available carbohydrates (CB) with an associated first-order availability rate constant (kB), and unavailable carbohydrates (CU). Additional quality parameters measured included neutral detergent lignin (NDL), total available carbohydrates (CA), and total carbohydrates (CT). We evaluated the variability of biomass quality parameters in a set of corn stover samples and developed calibration equations for determining parameter values using NIRS. Fifty-two corn stover samples harvested in Iowa and Wisconsin in 2005 and 2006 were analyzed using a high-throughput assay for determining feedstock quality for biochemical conversion. Non-structural carbohydrates ranged from 84 to 155 g kg−1 dry matter (DM); CB ranged from 354 to 557 g kg−1 DM; kB ranged from 0.199 to 0.330 h−1; CA ranged from 463 to 699 g kg−1 DM, and NDL ranged from 32 to 74 g kg−1 DM. Significant differences (P < 0.0001) among samples were observed for all parameters, except kB. Near-infrared reflectance spectroscopy calibration equations were developed for CN, CB, CA, CU, CT, and NDL. It was not possible to generate a meaningful calibration equation for kB. There is significant variability within the corn stover population for several key quality-related carbohydrate and lignin constituents which can be predicted reliably using NIRS.
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页码:79 / 85
页数:6
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