## 1 Introduction
Since the retrofit of Raman equipment in already existing pipelines and vessels is quite pricy, [PERSON] et al. [6] developed a non-contact Raman probe for in-line process monitoring through an optical inspection glass. This Raman probe combined with a Raman spectrometer was successfully applied by [PERSON] et al. [20] for in-line monitoring of glucose and ethanol during yeast fermentations. For quantification of the analyte concentrations during growth on glucose, MLR models were used. Regarding sensor systems based on separate optical channels, models with only two (2D) or four (4D) spectral regions were utilized. While similar ethanol prediction errors were reached for the 2D and 4D MLR models, the 4D MLR model achieved better results for the prediction of glucose concentrations than the 2D MLR model. A similar evaluation method based on separate optical channels for real-time monitoring of the identical yeast fermentations was carried out in 2017 [21]. Instead of Raman spectroscopy, an MIR sensor with four channels was used. This sensor was developed by [PERSON] et al. [10] for process monitoring. Since the sensor is based on four optical bandpass filters, the sensor can be easily adapted to a wide range of processes by exchanging and adjusting the filters to the spectral regions of interest. The fermentations ran by [PERSON] et al. [21] were recorded using the original MIR sensor design, as well as a commercial ALPHA FT-MIR spectrometer with an attenuated total reflection (ATR) module (Bruker Optik GmbH, Ettlingen, Germany) for comparison. In order to link the measured absorbance in the transmission ranges of the four optical sensor channels to the glucose and ethanol concentrations, the sensor and spectrometer were calibrated by MLR. [PERSON] [22] also monitored fermentations with the aid of an ATR element by MIR spectroscopy to predict glucose and ethanol concentrations. Besides the samples obtained during the fermentation processes, synthetic standards were used to form calibration sets. While one type of calibration set comprised either spectra from samples drawn during fermentation or synthetic standards, a mix of spectra from fermentation samples and synthetic standards formed the other type of calibration set. For data analysis, a PLS method was used. The optimal PLS regression method was obtained using the calibration type consisting of the mixed samples. Comparing FT-MIR and FT-Raman spectroscopy for bioprocess monitoring was done by [PERSON] et al. [23]. In order to monitor the ethanol production during yeast fermentations, FT-MIR spectra were recorded using a spectrometer equipped with a deuterated triglycine sulfate (DTGS) detector and an overhead ATR accessory. This ATR crystal was cleaned with water and dried using nitrogen gas between successive analysis of drawn samples from the anaerobic yeast fermentations. The same spectrometer extended by a Raman module with an indium-gallium-arsenide (InGaAs) detector and a Nd:YAG laser (wavelength: 1064 nm; output power: 1.2 W) was used for Raman spectroscopy. The spectral data were analyzed using PLS regression and PCR. Thereby, the PLS models with the first derivative as data pre-processing generated the smallest standard errors of prediction using FT-MIR spectroscopy. In the case of Raman spectroscopy, the smallest standard errors for calibration were also reached with the PLS models pre-processed with the first derivative. To the best of the authors' knowledge, the simultaneous application of Raman and MIR spectroscopy for real-time monitoring of yeast fermentations has not been reported so far. Either Raman or MIR spectroscopy was used for process monitoring in real time. In the case of a simultaneous application of both spectroscopic techniques, only drawn samples were analyzed [23]. The use of on-line and in situ sensors is preferred in any case with respect to the inherent danger of contamination in biotechnological processes [24].