Rapid, efficient, and low-cost technologies for monitoring the fermentation process during second generation (2G) or cellulosic ethanol production are essential for the successful implementation of this process at the commercial scale. Here, the use of near-infrared (NIR) spectroscopy associated with partial least squares (PLS) regression was investigated as a tool for monitoring the production of 2G ethanol from lignocellulosic sugarcane residues including bagasse, straw, and tops. The spectral data was based on a set of 103 alcoholic fermentation samples. Models based on different pre-processing techniques were evaluated. The best root mean square error of prediction (RMSEP) values obtained in the external validation were around 3.02 g/L for ethanol and 6.60 g/L for glucose. The findings showed that the PLS-NIR methodology was efficient in accurately predicting the glucose and ethanol concentrations during the production of 2G ethanol, demonstrating potential for use in monitoring and control of large-scale industrial processes.
Keywords: Bioprocess monitoring; Lignocellulosic biomass; Near-infrared (NIR) spectroscopy; Second generation ethanol; Sugarcane.
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