2D-fluorescence and multivariate data analysis for monitoring of sourdough fermentation process

- A process monitoring for rye sourdough fermentations is presented.
- The process variables pH-value and the degree of acidity are predicted.
- Different regression models and artificial neural networks were applied.
- The temperature has a higher impact on the prediction quality than the dough yield.

Sourdough is used for the manufacture of bakery products, especially rye bread and vital for the development of its typical flavor. Although the process of sourdough fermentation is known thousands of years, still it is not understood in detail. Despite, in modern bread fabrication quality requirements are very high and demand a consequent control not only for the final baking process, but also for the production of intermediates. Characteristic process variables like pH-value and the degree of acidity are typically measured off-line to receive information about the state of the fermentation process. A new approach for monitoring the actual process state is the employment of 2D-fluorescence spectroscopy. As a non-invasive, optical method it is widely used for monitoring various types of bioprocesses, e.g. yeast or bacterial cultivations. In this contribution the application of partial least squares (PLS) regression and principal component regression (PCR) models for prediction of process variables of rye sourdough fermentations are compared to an evaluation where principal component analysis (PCA) is combined with artificial neural networks (ANN) for prediction of pH-value and acidity. For the pH-value PLS regression proved as good as PCR models and the combination of PCA and ANN. The average percentage root mean square error of prediction (pRMSEP) was between 2.5 and 5.1%. For the prediction of the acidity level, the best results were obtained using PLS regression models (pRMSEP: 6.0-8.1%). Smoothing the noisy 2D-fluorescence spectra slightly decreased the errors about 0.6%. Predictions with data sets of varying dough yield and constant temperature led to about 2% better results than data sets with different temperatures and constant dough yield. These results indicate a higher sensitivity of the prediction quality with respect to varying temperature compared to varying dough yield.