Abstract
BackgroundObtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus.ResultsWe present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models.ConclusionIt is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-015-0222-2) contains supplementary material, which is available to authorized users.
Highlights
Obtaining accurate chemical composition and reactivity information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels
A set of 279 samples was assembled from a large population of feedstock samples to develop broad-based multi-feedstock models for composition and reactivity
When samples were selected from welldeveloped calibrations, they were chosen from the larger calibration set using principle component analysis (PCA) scoring
Summary
Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. High-throughput methods for the determination of biomass composition and recalcitrance, as it relates to the production of biofuels and chemicals, are increasingly valuable for screening large numbers of plants for suitability as biofuel feedstocks, as well as determining plants that may require further genetic modification of traits that lead to higher fuel yields [1,2]. These methods are vital in reducing the cost of biofuel production by allowing for a more rapid assessment of cost-effective paths forward [1]. It is a secondary method and requires primary methods, such as bench top compositional analysis, to build the predictive models for rapid analysis
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