Rapid determination of acetic acid, furfural and 5-hydroxymethylfurfural in biomass hydrolysates using near-infrared spectroscopy

Authors: LI, JUN - Kansas State University; ZHANG, MENG - Kansas State University; Dowell, Floyd; WANG, DONGHAI - Kansas State University

Submitted to: ACS Omega
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/8/2018
Publication Date: 5/18/2018
Citation: Li, J., Zhang, M., Dowell, F.E., Wang, D. 2018. Rapid determination of acetic acid, furfural and 5-hydroxymethylfurfural in biomass hydrolysates using near-infrared spectroscopy. ACS Omega. 3:5355-5361. doi: 10.1021/acsomega.8b00636.
DOI: https://doi.org/10.1021/acsomega.8b00636

Interpretive Summary: Developing low-cost and high-efficient alternative renewable energy to ease our energy resource challenges and air pollution is a common goal for researchers working in the field of energy. Bioethanol derived from biomass is a desirable alternative to conventional petroleum-based fossil fuels and has received widespread attention. However, a method is needed to quickly and cheaply measure the traits of the biomass. We used near infrared spectroscopy (NIR) as a rapid detection technique to measure the acetic acid, furfural, and 5-hydroxymethylfurfural (HMF) contents in biomass. The NIR prediction models were developed with accuracies suitable for screening, which shows that this can be a simple and rapid method for industrial application of ethanol production. The NIR method can predict acetic acid, furfural, and HMF in biomass hydrolysates in <1 min, and has no cost for reagents. This information will benefit researchers and commercial biofuels producers.

Technical Abstract: Near infrared spectroscopy (NIR) is a rapid detection technique that has been used to characterize biomass. The objective of this study was to develop suitable NIR 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 (PLS) regression was used to develop models capable of providing good prediction for acetic acid, furfural, and HMF contents. Optimal models were built using the wavelength range of 9,000-8,000 plus 7,000-5,000 cm-1 with high R2 for calibration and validation models, small root mean square error of calibration (RMSEC, <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 NIR method largely reduced the analytical time from ~55 to < 1 min and has no cost for reagents.