Determination of ethanol content during simultaneous saccharification and fermentation (SSF) of cassava based on a colorimetric sensor technique

Abstract

Ethanol content is an important indicator reflecting the yield of simultaneous saccharification and fermentation (SSF) of cassava. This study proposes an innovative method based on a colorimetric sensor technique to determine the ethanol content during the SSF of cassava. First, 14 kinds of porphyrin material and one kind of pH indicator were used to form a colorimetric sensor array for collecting odor data during the SSF of cassava. Then, the ant colony algorithm (ACO) and the simulated annealing algorithm (SA) were used to optimize and reconstruct the input color feature components of the support vector regression (SVR) model. The differential evolution algorithm (DE) was used to optimize the penalty factor (c) and the kernel function (g) of the SVR model. The results obtained showed that the combined prediction model of SA-DE-SVR had the highest accuracy, and the coefficient of determination (R_P²) in the prediction set was 0.9549, and the root mean square error of prediction (RMSEP) was 0.1562. The overall results reveal that the use of a colorimetric sensor technique combined with different intelligent optimization algorithms to establish a model can quantitatively determine the ethanol content in the SSF of cassava, and has broad development prospects.