Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover

Wolfrum, Edward J.; Sluiter, Amie

doi:10.1007/s10570-009-9320-2

Cited by 61 publications

(53 citation statements)

References 6 publications

(4 reference statements)

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“…Normally, a low RMSEP and high R 2 val values (close to 1), are desired. The R 2 val values of the four models in this study were 0.98-0.99, which is higher than that in a corn model (0.42-0.85) [48], and a sorghum model (0.90-0.94) [46]. The RMSEP of the cellulose model was 1.62 (1.96 for corn, 1.45 for sorghum); the RMSEP of the hemicellulose model was 0.97 (1.33 for corn, 0.81 for sorghum), lignin model was 0.66 (1.49 for corn, 0.82 for sorghum), and extractives model was 2.71 (2.33 for corn model, 2.33 for sorghum model).…”

Section: -64%contrasting

confidence: 65%

High-Throughput Profiling of the Fiber and Sugar Composition of Sugarcane Biomass

et al. 2016

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Lignocellulosic biomass from sugarcane (Saccharum spp. hybrids) could potentially be a major feedstock for second-generation biofuel production. Consequently, selecting sugarcane varieties with favorable biomass characteristics, typically less enzymatic recalcitrance and better saccharification yield without sugaryield penalty, will be important in sugarcane breeding. Economical and high-throughput techniques for profiling the major biomass components of this complex system will facilitate selection of clones with ideal lignocellulosic composition from large numbers of genotypes in breeding programs. We used a combined high-throughput profiling approach to evaluate the biomass composition of samples from a sugarcane germplasm collection. This employed near-infrared (NIR) spectroscopy for fiber characterization and high-performance liquid chromatography (HPLC) for determining the sugar content in juice. The results for 331 samples, from a diverse sugarcane population of 186 genotypes, derived from 143 parents of different genetic backgrounds, showed that high-quality NIR spectroscopic predictions were feasible for cellulose, hemicellulose, lignin, and extractives values in fiber, and sugars in juice were suitably analyzed by HPLC. The analysis of total biomass indicated that this NIR-and HPLC-based highthroughput method allowed a robust phenotypic assessment of a large number of samples for the key biomass traits in the sugarcane system, including total dry biomass, fiber, sugar content, and theoretical ethanol yields, and could potentially become the method of choice for sugarcane germplasm screening in breeding programs targeting the support of biofuel production.

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Section: -64%contrasting

confidence: 65%

High-Throughput Profiling of the Fiber and Sugar Composition of Sugarcane Biomass

et al. 2016

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“…NREL's calibration models [24] were developed using multivariate statistical analyses to correlate NIR spectroscopic data to compositional data produced using standard wet chemical analysis techniques developed at NREL [25]. Predicting sample composition from NIR spectroscopic data is well established in the literature [26][27][28][29].…”

Section: � Ft-nir Compositional Analysis Modelmentioning

confidence: 99%

Drought effects on composition and yield for corn stover, mixed grasses, andMiscanthusas bioenergy feedstocks

et al. 2014

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“…It is common to mathematically transform spectral data prior to building calibration models [20,26]. These pretreatments can help to reduce spectral variation due to instrument or sample variability.…”

Section: Multivariate Calibration Of Exogenous Lipid Spikementioning

confidence: 99%

Feasibility of Spectroscopic Characterization of Algal Lipids: Chemometric Correlation of NIR and FTIR Spectra with Exogenous Lipids in Algal Biomass

2010

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A large number of algal biofuels projects rely on a lipid screening technique for selecting a particular algal strain with which to work. We have developed a multivariate calibration model for predicting the levels of spiked neutral and polar lipids in microalgae, based on infrared (both nearinfrared (NIR) and Fourier transform infrared (FTIR)) spectroscopy. The advantage of an infrared spectroscopic technique over traditional chemical methods is the direct, fast, and non-destructive nature of the screening method. This calibration model provides a fast and high-throughput method for determining lipid content, providing an alternative to laborious traditional wet chemical methods. We present data of a study based on nine levels of exogenous lipid spikes (between 1% and 3% (w/w)) of trilaurin as a triglyceride and phosphatidylcholine as a phospholipid model compound in lyophilized algal biomass. We used a chemometric approach to corrrelate the main spectral changes upon increasing phospholipid and triglyceride content in algal biomass collected from single species. A multivariate partial least squares (PLS) calibration model was built and improved upon with the addition of multiple species to the dataset. Our results show that NIR and FTIR spectra of biomass from four species can be used to accurately predict the levels of exogenously added lipids. It appears that the cross-species verification of the predictions is more accurate with the NIR models (R 2 =0.969 and 0.951 and RMECV=0.182 and 0.227% for trilaurin and phosphatidylcholine spike respectively), compared with FTIR (R 2 =0.907 and 0.464 and RMECV=0.302 and 0.767% for trilaurin and phosphatidylcholine spike, respectively). A fast high-throughput spectroscopic lipid fingerprinting method can be applied in a multitude of screening efforts that are ongoing in the microalgal research community.

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Improved multivariate calibration models for corn stover feedstock and dilute-acid pretreated corn stover

Cited by 61 publications

References 6 publications

High-Throughput Profiling of the Fiber and Sugar Composition of Sugarcane Biomass

High-Throughput Profiling of the Fiber and Sugar Composition of Sugarcane Biomass

Drought effects on composition and yield for corn stover, mixed grasses, andMiscanthusas bioenergy feedstocks

Feasibility of Spectroscopic Characterization of Algal Lipids: Chemometric Correlation of NIR and FTIR Spectra with Exogenous Lipids in Algal Biomass

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