## Materials and Methods
### Chemometrics  
For quantification of glucose, ethanol, and biomass, PLS regression and MLR models were applied. In the case of MLR, which was done in Excel(r), two-dimensional (2D) and four-dimensional (4D) models were built for glucose, ethanol, and biomass. For the MLR models, the intensities in analyte-specific spectral regions were chosen for each analyte. The PLS models were realized using the software OPUS(r). Thereby, models without data pre-processing (PLS without PP) and vector normalization as a pre-processing method (PLS with PP) were built. For evaluation, a spectral range of characteristic ethanol and glucose peaks was chosen. In addition to vector normalization, other pre-processing methods such as first derivative, second derivative, and straight-line subtraction were tested. Applying vector normalization to the Raman and MIR spectra, constant low calibration and prediction errors were achieved for all three analytes, while the other data pre-processing methods led to strong varying quality parameters. In order to avoid overfitting of data, the maximum number of PLS factors was limited to seven. Depending on the analyte and the spectroscopic method, the optimal number of factors was between two and five. The spectral regions for PLS and MLR evaluation for both spectroscopic methods are listed in Table 1.  
For the quantification of glucose and ethanol by 2D MLR models, the signal intensity (Raman or MIR-ATR) in the measurement region and the intensity in a neighboring reference region (for fluorescence and/or offset correction) were used. The signal intensities were linked to the concentration values of the HPLC analysis (see Equations (1) and (2)). No characteristic peaks for biomass were found for Raman and MIR spectroscopy. For the determination of biomass by MIR spectra, the glucose (S1) and ethanol (S3) measurement channels were used, while in case of Raman spectroscopy, the glucose (S2) and ethanol (S4) reference channels were taken (see Section 3.1). The reference values for biomass in Equations (3) and (4) were determined by gravimetry. In contrast to 2D MLR, all four channels in Table 1 were used for 4D MLR models of glucose, ethanol, and biomass (see Equation 5). According to Equation (5), six 4D MLR models were built: one model for each of the three analytes in combination with the Raman or MIR-ATR signal intensities. \[\text{2D MLR (glucose)}:\ c=\alpha+\beta_{S1}\cdot x_{S1}+\beta_{S2}\cdot x_{S2}, \tag{1}\]  
\[\text{2D MLR (ethanol)}:\ c=\alpha+\beta_{S3}\cdot x_{S3}+\beta_{S4}\cdot x_{S4}, \tag{2}\]  
\[\text{2D MLR (biomass by MIR)}:\ c=\alpha+\beta_{S1}\cdot x_{S1}+\beta_{S3} \cdot x_{S3}, \tag{3}\]  
\[\text{2D MLR (biomass by Raman)}:\ c=\alpha+\beta_{S2}\cdot x_{S2}+\beta_{S4} \cdot x_{S4}, \tag{4}\]  
\[\text{4D MLR : }c=\alpha+\beta_{S1}\cdot x_{S1}+\beta_{S2}\cdot x_{S2}+\beta_{S3 }\cdot x_{S3}+\beta_{S4}\cdot x_{S4}, \tag{5}\]  
where \(c\) is the concentration of the analyte (ethanol, glucose, or biomass), \(\alpha\) the intercept, \(\beta_{i}\) the regression coefficient (slope), and \(x_{i}\) the mean value of the intensities in the corresponding wavenumber regions (\(i\) = S1, S2, S3, S4). The regression coefficients and the intercept were determined during calibration by minimization of the predicted residual error sum of squares (PRESS). In contrast to the MLR models, which used separate optical channels, the PLS models were based on broad spectral ranges between 800 and 1400 cm\({}^{-1}\) for Raman and 850 and 1250 cm\({}^{-1}\) for MIR spectroscopy. These PLS ranges covered all MLR channels for glucose and ethanol without loss of information. Three fermentation reactions were carried out for the calibration of glucose, ethanol, and biomass concentrations. During each fermentation reaction, 21 samples were taken for reference analysis. The 63 concentrations obtained for each analyte were used for the PLS and MLR calibration.