Abstract
The potential of Fourier transform middle-infrared spectroscopy has been demonstrated for the quantitative analysis of substrates (glucose and fructose) and metabolites (glycerol and ethanol) involved in alcoholic fermentation. Temperature variations between samples and water background reference caused changes in absorbance, and therefore the prediction of concentrations with partial least-squares (PLS) regressions was affected. The same temperatures for the calibration, validation, and prediction sets gave the smallest standard error of prediction (SEP): SEP<sub>glucose</sub> = 3.9 g L<sup>-1</sup>; SEP<sub>fructose</sub> = 4.3 g L<sup>-1</sup>; SEP<sub>glycerol</sub> = 0.5 g L<sup>-1</sup>; SEP<sub>ethanol</sub> = 1.3 g L<sup>-1</sup>. In order to take different working temperatures (18, 25, and 35 °C) into account, an artificial neural network was used to create a nonlinear multivariate model. Compared to PLS regression, this method provided better results, especially for glycerol and ethanol, where SEP decreased by 0.3 g L<sup>-1</sup> and 0.4 g L<sup>-1</sup>, respectively.
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