## 1 Introduction  
For optimum process control and quality assurance in chemical, as well as biotechnological processes, knowledge of the reaction mixture's current composition is of great benefit. In that regard, educt and product concentrations are important parameters [1]. In recent years, optical spectroscopy has been established for simultaneous real-time measurements of multiple analytes [2]. Taking the rapid pace of change in the fermentation industry, as well as financial pressure into consideration, vibrational spectroscopy has shown great promise regarding process monitoring [3]. Two vibrational spectroscopic approaches are Raman and mid-infrared (MIR) spectroscopy, where predominantly fundamental vibrations are observed in the spectral range from 4000-200 cm\({}^{-1}\)[4]. The limits of detection (LOD) of both spectroscopic techniques have similar values in the three-digit ppm range [5; 6; 7; 8], depending on the setup conditions. Moreover, the identification of molecules is facilitated by the characteristic band positions of the different functional groups and skeletal vibrational motions. In Raman spectroscopy, the polarizability of a chemical bonding orbital is crucial for inelastic light scattering, and thus excitation of the fundamental vibration in a molecule. The good polarizability of non-polar bonds (e.g., C-C) results in intensive Raman bands, and vice versa, chemical bonds with a high dipole moment have usually a poor polarizability, which decreases the Raman activity. In contrast, strong absorption bands occur in MIR spectroscopy if the dipole moment changes significantly during a fundamental vibration. Examples for functional groups with intensive MIR bands are the stretching vibration of carbonyls (C=O) and hydroxyl groups (O-H). Consequently, Raman and MIR spectroscopy are partially complementary to each other [4; 9]. There have been a number of studies carried out using Raman or MIR spectroscopy for reaction monitoring [3; 6; 10; 11; 12; 13]. In order to analyze multicomponent mixtures, optical spectroscopy in conjunction with chemometric techniques is often required for converting optical spectra into analyte concentrations [14; 15]. For example, [PERSON] et al. [16] used a 785-nm laser and a Raman spectrometer to predict the glucose concentration in mammalian cell cultivations. In that study, multiple linear regression (MLR), principle component regression (PCR), and partial least squares (PLS) regression models combined with different variable selection methods were tested. The best result was achieved for a pre-processed PLS regression model where the variable importance in projection (VIP) was applied. This model was the most advanced algorithm of the tested models. In contrast, only an internal standard in combination with linear regression was sufficient for [PERSON] et al. [17] during real-time monitoring of a lignocellulosic bioethanol production process by Raman spectroscopy (excitation wavelength: 785 nm). Further pre-processing of the Raman signals or chemometric evaluation like principal component analysis (PCA) or PLS regression was not required. [PERSON] et al. [18] presented a Fourier-transform near-infrared (FT-NIR) spectrometer with auto-calibration for the compensation of the influence of ambient temperature on the Raman spectra. The measurement setup consisted of a Bruker MultiRAM system with a 1064-nm laser and a liquid nitrogen-cooled Ge detector. The Raman immersion probe was located in a special filter tube in order to eliminate light scattering effects caused by particles (cells) during a wine fermentation process. The realization of a particle-free measurement environment was combined with PLS-PCA models for data evaluation. [PERSON] et al. [19] monitored the fermentation process of glucose by _Saccharomyces cerevisiae_ (_S. cerevisiae_) in a bypass using a Raman spectrometer equipped with a quartz cuvette for flow analysis. The spectra were evaluated using PLS.