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Fourier transform mid-infrared (FT-MIR) spectroscopy is a fast, accurate, and practical technique for the quantitative analysis of compounds during ethanol fermentation. However, it is very temperature sensitive. This study aimed to develop a methodology for the real-time monitoring of fed-batch ethanol fermentations, taking into account the thermal fluctuations in the industrial process (in the range 30−35 °C). The use of FT-MIR combined with partial least squares (PLS) regression was evaluated as a tool to quantify sucrose, glucose, fructose, ethanol, and glycerol. The PLS models, based on fermentations carried out at constant temperatures in the range from 26 to 36 °C, presented high correlation coefficients (R 2 ) and low root-mean-square errors of cross-validation and prediction that were below 7% of the calibration ranges for all compounds. The good performance of the models demonstrated their suitability for accurate process monitoring, covering a wide range of thermal variations observed in industrial operations.

The Fourier transform mid-infrared (FT-MIR) spectroscopy technique, with multivariate calibration by the partial least-squares (PLS) regression method, was used for real-time monitoring of the ethanol molar fraction in the gas phase during ethanol fermentation. The PLS model was obtained using a calibration set constructed of gas streams with different molar fraction proportions and the corresponding FT-MIR spectra. The molar fraction compositions were calculated by combining thermodynamic assumptions and mass balances. The proposed model presented excellent performance when the spectra were submitted to pretreatment using middle center (MC), smoothed moving average (SMA), and second derivative (secondD). The determination coefficient for the model revealed an excellent fit to the experimental data (R 2 = 0.999). The root-mean-square error of cross-validation (RMSECV) and the root-mean-square error of prediction (RMSEP) were less than 1.6 and 1.9% of the range of values, respectively, indicative of excellent predictive capacity. The high values of the range error ratio (RER = 52.52) and the ratio of the standard error of performance to the standard deviation (RPD = 17.54) provided further evidence of the excellent resolution of the model, which was used for carrying out quantitative analyses of ethanol in the gas phase. A set of fermentations performed in batch and fed-batch modes was used to evaluate the predictive capacity of the model. The data for the molar fraction of ethanol in the gas phase were used to estimate the ethanol and substrate concentrations in the liquid phase. The findings demonstrated that analysis of the gas phase using the FT-MIR/PLS technique is a reliable strategy for real-time monitoring of ethanol fermentation under different operational modes, without any sample manipulation.

No abstract

Fast, simple, accurate, and inexpensive methods for obtaining analyte concentration data are desirable in the industrial sector. In the present study, the use of Fourier transform mid-infrared (FT-MIR) spectroscopy, combined with partial least squares (PLS) regression, was investigated as a tool for real-time monitoring of processes of ethanol absorption in glycols. Calibration was performed using simple synthetic samples containing ethanol, water, and monoethylene glycol (MEG) or diethylene glycol (DEG). The PLS models presented excellent performance, with correlation coefficients (R 2) close to unity and root-mean-square errors of cross-validation (RMSECV) and prediction (RMSEP) lower than 2% of the calibration data ranges for both analytes (ethanol and water) in both absorbents (MEG and DEG). The monitoring technique developed has potential to be applied in absorption processes and could also be used in other large-scale unit operations, providing information in real time and enhancing process control.