Effects of Yeast Product Addition and Fermentation Temperature on Lipid Composition, Taste and Mouthfeel Characteristics of Pinot Noir Wine

Lipids have important impacts on wine sensory. By targeting the lipid sources in wine, mainly from grape tissues and yeast cell walls, it was possible to alter the wine lipid profile thus potentially changing the final product quality. This research examined the changes of wine total lipids, lipid composition and sensory characteristics of Pinot noir wines in response to the winemaking factors, fermentation temperature and yeast product addition. Pinot noir grapes were fermented at 16 °C and 27 °C. After fermentation, Oenolees® yeast product was added to the wines at three levels (0 g/L, 0.5 g/L and 1.0 g/L). The six wine treatments were subjected to chemical analyses measuring total lipids and an untargeted lipidomic approach analyzing lipid composition. High temperature fermentation wines had significantly higher total lipid content. Random forest analysis distinguished the wine groups based on the 25 main lipids, including free fatty acids, acylcarnitines, diglycerides, triglycerides and phospholipids. Taste and mouthfeel characteristics of each treatment were assessed using descriptive analysis and check-all-that-apply (CATA) techniques. Multivariate analyses showed that changing fermentation temperature significantly impacted sweetness and drying perception in Pinot noir wines. Yeast product addition had nuanced effects on wine lipid profiles and sensory perception.

Dynamic Optimisation of Beer Fermentation under Parametric Uncertainty

Fermentation is one of the most important stages in the entire brewing process. In fermentation, the sugars are converted by the brewing yeast into alcohol, carbon dioxide, and a variety of by-products which affect the flavour of the beer. Fermentation temperature profile plays an essential role in the progression of fermentation and heavily influences the flavour. In this paper, the fermentation temperature profile is optimised. As every process model contains experimentally determined parameters, uncertainty on these parameters is unavoidable. This paper presents approaches to consider the effect of uncertain parameters in optimisation. Three methods for uncertainty propagation (linearisation, sigma points, and polynomial chaos expansion) are used to determine the influence of parametric uncertainty on the process model. Using these methods, an optimisation formulation considering parametric uncertainty is presented. It is shown that for the non-linear beer fermentation model, the linearisation approach performed worst amongst the three methods, while second-order polynomial chaos worked the best. Using the techniques described below, a fermentation process can be optimised for ensuring high alcohol content or low fermentation time while ensuring the quality constraints. As we explicitly consider uncertainty in the process, the solution, even though conservative, will be more robust to parametric uncertainties in the model.

Research of technological features of production of functional fermented milk products

The most important factor determining human health is nutrition. Functional products containing many valuable ingredients contribute to the strengthening and maintenance of human health. In this regard, over the years, the development of technology for the production of such products does not lose its relevance. The authors selected the composition and ratio of probiotic microflora in the composition of a complex starter culture for the production of a functional fermented milk product. Two variants of complex starter culture were selected containing Streptococcus salivaris subsp. thermophilus, Bifidobacterium subsp. lactis (BB-12) and Lactobacterium casei subsp. casei in a ratio of 4: 1: 1 and Streptococcus salivaris subsp. thermophilus, Bifidobacterium bifidum and Lactobacillus acidophilus in a 4: 1: 1 ratio. The paper considers the influence of individual technological factors on the quality formation of the finished product, such as the dose of the prebiotic galactooligosaccharides (GOS), the dose of the concentrate of whey protein (CWP) and the fermentation temperature of the milk mixture. The following technological factors of production have been established: doses of introduced components (GOS - (1 ± 0.1) %, CWP (2.0 ± 0.5) % and the fermentation temperature of the mixture (38 ± 2) °C. The results of the study will be used further in the development of technology for a functional fermented milk product.

Effect of Fermentation Parameters on Natto and Its Thrombolytic Property

Natto is a popular food because it contains a variety of active compounds, including nattokinase. Previously, we discovered that fermenting natto with the combination of Bacillus subtilis GUTU09 and Bifidobacterium animalis subsp. lactis BZ25 resulted in a dramatically better sensory and functional quality of natto. The current study further explored the effects of different fermentation parameters on the quality of natto fermented with Bacillus subtilis GUTU09 and Bifidobacterium BZ25, using Plackett–Burman design and response surface methodology. Fermentation temperature, time, and inoculation amount significantly affected the sensory and functional qualities of natto fermented with mixed bacteria. The optimal conditions were obtained as follows: soybean 50 g/250 mL, triangle container, 1% sucrose, Bacillus subtilis GUTU09 to Bifidobacterium BZ25 ratio of 1:1, inoculation 7%, fermentation temperature 35.5 °C, and fermentation time 24 h. Under these conditions, nattokinase activity, free amino nitrogen content, and sensory score were increased compared to those before optimization. They were 144.83 ± 2.66 FU/g, 7.02 ± 0.69 mg/Kg and 82.43 ± 5.40, respectively. The plate thrombolytic area and nattokinase activity both increased significantly as fermentation time was increased, indicating that the natto exhibited strong thrombolytic action. Hence, mixed-bacteria fermentation improves the taste, flavor, nattokinase activity, and thrombolysis of natto. This research set the groundwork for the ultimate manufacturing of natto with high nattokinase activity and free amino nitrogen content, as well as good sensory and thrombolytic properties.

Artificial Neural Networks and Gompertz Functions for Modelling and Prediction of Solvents Produced by the S. cerevisiae Safale S04 Yeast

The present work aims to develop a mathematical model, based on Gompertz equations and ANNs to predict the concentration of four solvent compounds (isobutanol, ethyl acetate, amyl alcohol and n-propanol) produced by the yeasts S. cerevisiae, Safale S04, using only the fermentation temperature as input data. A beer wort was made, daily samples were taken and analysed by GC-FID. The database was grouped into five datasets of fermentation at different setpoint temperatures (15.0, 16.5, 18.0, 19.0 and 21.0 °C). With these data, the Gompertz models were parameterized, and new virtual datasets were used to train the ANNs. The coefficient of determination (R2) and p-value were used to compare the results. The ANNs, trained with the virtual data generated with the Gompertz functions, were the models with the highest R2 values (0.939 to 0.996), showing that the proposed methodology constitutes a useful tool to improve the quality (flavour and aroma) of beers through temperature control.