Bioprocess monitoring and control via adaptive sensor calibration

The contribution of glutathione to the destabilizing effect of yeast on wheat dough

Any factor which impairs the development of the gluten network affects the gas retention capacity and the overall baking performance. This study aimed to examine why rising yeast concentrations (Saccharomyces cerevisiae) decrease the dough elasticity in an asymptotic manner. Since in 27 commercial fresh and dry yeasts up to 81 mg glutathione (GSH) per 1g dry sample were found. Through the addition of reduced GSH in dough without yeast, the extent of dough weakening was analysed. Indeed rheological measurements confirmed that yeast-equivalent levels of GSH had a softening effect and during 3h fermentation the weakening coefficient increased from 0.3% to 20.4% in a Rheofermentometer. The present results indicate that free -SH compounds, as represented by GSH, considerably contribute to the softening of dough through dead yeast cells.

Ultrasonic sensor for predicting sugar concentration using multivariate calibration

This paper presents a multivariate regression method for the prediction of maltose concentration in aqueous solutions. For this purpose, time and frequency domain of ultrasonic signals are analyzed. It is shown, that the prediction of concentration at different temperatures is possible by using several multivariate regression models for individual temperature points. Combining these models by a linear approximation of each coefficient over temperature results in a unified solution, which takes temperature effects into account. The benefit of the proposed method is the low processing time required for analyzing online signals as well as the non-invasive sensor setup which can be used in pipelines. Also the ultrasonic signal sections used in the presented investigation were extracted out of buffer reflections which remain primarily unaffected by bubble and particle interferences. Model calibration was performed in order to investigate the feasibility of online monitoring in fermentation processes. The temperature range investigated was from 10 °C to 21 °C. This range fits to fermentation processes used in the brewing industry. This paper describes the processing of ultrasonic signals for regression, the model evaluation as well as the input variable selection. The statistical approach used for creating the final prediction solution was partial least squares (PLS) regression validated by cross validation. The overall minimum root mean squared error achieved was 0.64 g/100 g.