Journal
ACS OMEGA
Volume 3, Issue 5, Pages 5355-5361Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.8b00636
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Funding
- U.S. National Science Foundation [1562671]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1562671] Funding Source: National Science Foundation
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Near-infrared spectroscopy (NIRS) is a rapid detection technique that has been used to characterize biomass. The objective of this study was to develop suitable NIRS models to predict the acetic acid, furfural, and 5-hydroxymethylfurfural (HMF) contents in biomass hydrolysates. Using a uniform distribution of pretreatment conditions, 60 representative biomass hydrolysates were prepared. Partial least-squares regression was used to develop models capable of predicting acetic acid, furfural, and HMF contents. Optimal models were built using the wavenumber range of 9000-8000 and 7000-5000 cm(-1) with high R-2 for calibration and validation models, small root-mean-square error of calibration (<0.21) and root-mean-square error of prediction (RMSEP, <0.42), and a ratio of the standard deviation of the reference values to the RMSEP of >2.7. The NIRS method largely reduced the analytical time from similar to 55 to <1 min and has no cost for reagents.
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