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a This paper presents a multivariate regression method for simultaneous detection of sugar (sucrose as a sugar equivalent) and ethanol concentrations in aqueous solutions via temperature-dependent ultrasonic velocity. Thus, several samples of different combined concentration values were exposed to a temperature spectrum ranging from 2 to 30-C to investigate the temperature dependence of ultrasonic velocity. Model calibration was performed in order to predict the concentrations of interest. With results of proceeded experiments, the equations for calculation of unknown concentrations were carried out using polynomial regression revealing two equations with functional dependence of concentrations on each other. Further, side effects or systematic errors are still included in this model. To avoid such problems as well as to increase the accuracy with respect to the absolute errors in determining unknown probes, multivariate regression methods such as partial least squares (PLS) were tested and compared to the results obtained by polynomial regression. The accuracy achieved with chemometric models on average was three times higher. In direct comparison, the values of the error for the prediction of sucrose concentration were on average around 0.4 g/100 g in the regression model with polynomial background (RMPA) and around 0.12 g/100 g in the PLS model, and for ethanol concentration 0.13 and 0.04 g/100 g, respectively. Furthermore, calculations of the concentrations are possible without knowing the concentrations of the other solute.

The food industry is the fourth largest industrial sector in Germany. The eagerness for innovation is classified as low. The food industry faces significantly larger challenges compared to the chemical industry since the demands of raw materials on processing are higher and more complex. In this contribution, the characteristics of food manufacturing are presented. The potential of optical process analyzers based on NIR, fluorescence, and Raman spectroscopy as well as on digital image analysis is demonstrated. These process analyzers can provide important information on raw materials, intermediate and end products, and improve the automation grade of production processes.

Zusammenfassung Bei alkoholischen Hefefermentationsprozessen in der zuckerverarbeitenden Industrie ist es wichtig, die Konzentrationen von Zucker und Ethanol im Fermentationsfluid als Indikatoren für den Reaktionsfortschritt genau zu kennen. Die vorliegende Arbeit beschreibt ein neues Verfahren zur Konzentrationsbestimmung, das sich der physikalischen Parameter adiabatische Kompressibilität und Dichte bedient, die aus den vom einem Ultraschallsensor bei einem Messvorgang simultan gemessenen Größen Schallgeschwindigkeit und akustische Impedanz berechnet werden können. Die Genauigkeit des Verfahrens liegt bei einer Massekonzentration von ca. 0,05 g/100 g.

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