Schalk, RobertHeintz, AnnabellBraun, FrankIacono, GiuseppeRädle, MatthiasGretz, NorbertMethner, Frank-JürgenBeuermann, Thomas2019-08-152019-08-152019-06-17https://depositonce.tu-berlin.de/handle/11303/9815http://dx.doi.org/10.14279/depositonce-8836Raman and mid-infrared (MIR) spectroscopy are useful tools for the specific detection of molecules, since both methods are based on the excitation of fundamental vibration modes. In this study, Raman and MIR spectroscopy were applied simultaneously during aerobic yeast fermentations of Saccharomyces cerevisiae. Based on the recorded Raman intensities and MIR absorption spectra, respectively, temporal concentration courses of glucose, ethanol, and biomass were determined. The chemometric methods used to evaluate the analyte concentrations were partial least squares (PLS) regression and multiple linear regression (MLR). In view of potential photometric sensors, MLR models based on two (2D) and four (4D) analyte-specific optical channels were developed. All chemometric models were tested to predict glucose concentrations between 0 and 30 g L−1, ethanol concentrations between 0 and 10 g L−1, and biomass concentrations up to 15 g L−1 in real time during diauxic growth. Root-mean-squared errors of prediction (RMSEP) of 0.68 g L−1, 0.48 g L−1, and 0.37 g L−1 for glucose, ethanol, and biomass were achieved using the MIR setup combined with a PLS model. In the case of Raman spectroscopy, the corresponding RMSEP values were 0.92 g L−1, 0.39 g L−1, and 0.29 g L−1. Nevertheless, the simple 4D MLR models could reach the performance of the more complex PLS evaluation. Consequently, the replacement of spectrometer setups by four-channel sensors were discussed. Moreover, the advantages and disadvantages of Raman and MIR setups are demonstrated with regard to process implementation.en660 Chemische VerfahrenstechnikRaman spectroscopymid-infrared spectroscopyfermentation of Saccharomyces cerevisiaereal-time monitoringmulti-channel photometric sensorsmultiple linear regressionpartial least squares regressionmonitoring of glucoseethanolbiomassArticle2019-08-102076-3417