Simultaneous Determination of Yeast Inhibitors 5‑HMF and Furfural in Hydrolyzed Lignocellulosic Biomass using...

The lignocellulosic biomass acid hydrolysis process, for either pretreatment or saccharification purposes, involves temperature and acidity, which can lead to carbohydrate dehydration into furfuraldehydes, such as 5-hydroxymethylfurfural (5-HMF) and furfural. Unfortunately, these compounds can reduce the biomass quality for biofuel production, potentially inhibiting yeast fermentation, which converts sugars into ethanol, leading to low yields. Given the need to control these substances, a methodology for the simultaneous determination of 5-HMF and furfural via high-performance liquid chromatography (HPLC) was developed and validated to monitor the formation of these unwanted byproducts directly after the hydrolysis process of the Hevea brasiliensis lignocellulosic matrix. The method showed adequate selectivity for both analytes. Linearity was confirmed by analysis of variance (p < 0.05) for 5-HMF and furfural, with excellent correlation coefficients: R ² = 0.99984 in the 0.1-50 μg·mL^-1 range for 5-HMF, and R ² = 0.99956 in the 0.1-25 μg·mL^-1 range for furfural, with low limits of detection and quantification: 0.1981 and 0.6002 μg·mL^-1 for 5-HMF, and 0.1585 and 0.4802 μg·mL^-1 for furfural, respectively. The method also demonstrated accuracy, with recovery rates in fortified samples between 100.7 and 104.9% for 5-HMF and 97.54 and 100.4% for furfural. Precision, divided into repeatability and intermediate precision, showed both values for RSD < 15%. Additionally, the method demonstrated robustness, maintaining expected performance when subjected to small variations. The developed method proved to be a quick, effective, and reliable approach for quantifying 5-HMF and furfural in the hydrolyzed lignocellulosic biomass, successfully applied to 43 real samples without the need for complex pretreatment and with a shorter run time and high sensitivity. This makes it suitable for routine monitoring and supports more practical, scalable, and both time and cost-effective strategies for optimizing biomass conversion and bioethanol production.