Hybrid modeling for industrial fermentation processes with an \u201cIntra-Batch Experimental Design\u201d

A robust Saccharomyces cerevisiae strain has been widely applied in continuous and batch/fed-batch industrial fermentation. However, little is known about the molecular basis of fermentative behavior of this strain in the two realistic fermentation processes. In this paper, we presented comparative proteomic profiling of the industrial yeast in the industrial fermentation processes. The expression levels of most identified protein were closely interrelated with the different stages of fermentation processes. Our results indicate that, among the 47 identified protein spots, 17 of them belonging to 12 enzymes were involved in pentose phosphate, glycolysis, and gluconeogenesis pathways and glycerol biosynthetic process, indicating that a number of pathways will need to be inactivated to improve ethanol production. The differential expressions of eight oxidative response and heat-shock proteins were also identified, suggesting that it is necessary to keep the correct cellular redox or osmotic state in the two industrial fermentation processes. Moreover, there are significant differences in changes of protein levels between the two industrial fermentation processes, especially these proteins associated with the glycolysis and gluconeogenesis pathways. These findings provide a molecular understanding of physiological adaptation of industrial strain for optimizing the performance of industrial bioethanol fermentation.

Hybrid Modeling and Optimisation of Industrial Fed-Batch Fermentation Process

Study of the application of multiway multivariate techniques to model data from an industrial fermentation process

Although many Brazilian sugar mills initiate the fermentation process by inoculating selected commercial Saccharomyces cerevisiae strains, the unsterile conditions of the industrial sugar cane ethanol fermentation process permit the constant entry of native yeast strains. Certain of those native strains are better adapted and tend to predominate over the initial strain, which may cause problems during fermentation. In the industrial fermentation process, yeast cells are often exposed to stressful environmental conditions, including prolonged cell recycling, ethanol toxicity and osmotic, oxidative or temperature stress. Little is known about these S. cerevisiae strains, although recent studies have demonstrated that heterogeneous genome architecture is exhibited by some selected well-adapted Brazilian indigenous yeast strains that display high performance in bioethanol fermentation. In this study, 11 microsatellite markers were used to assess the genetic diversity and population structure of the native autochthonous S. cerevisiae strains in various Brazilian sugar mills. The resulting multilocus data were used to build a similarity-based phenetic tree and to perform a Bayesian population structure analysis. The tree revealed the presence of great genetic diversity among the strains, which were arranged according to the place of origin and the collection year. The population structure analysis revealed genotypic differences among populations; in certain populations, these genotypic differences are combined to yield notably genotypically diverse individuals. The high yeast diversity observed among native S. cerevisiae strains provides new insights on the use of autochthonous high-fitness strains with industrial characteristics as starter cultures at bioethanol plants.

Wines in the northwest region of China often suffer from poor color stability, low wine acidity, and inelegant aroma description. The Lachancea thermotolerans yeasts with a high yield of lactic acid and pleasant aroma compounds can ameliorate such wines. In this study, the performances of native L. thermotolerans CVE-LT1 co-inoculated with Saccharomyces cerevisiae in different ratios and strategies (simultaneous and sequential mode) were evaluated in pilot scale fermenter (60 L) and industrial scale fermenter (520 hL) of Cabernet Sauvignon grape must with high sugar/low acidity. Results evidenced that the mixed culture of L. thermotolerans and S. cerevisiae can significantly increase lactic acid level (up to 6.98 g/L) and decrease wine pH (up to 3.57). Meanwhile, the color parameters of a* and C* in mixed culture wines were improved. The higher concentrations of volatile compounds were mainly observed in sequential inoculation treatments, including the enhancement of ethyl lactate and 2-phenylethanol. As for phenolic compounds, L. thermotolerans promoted the formation of anthocyanins derivatives and phenolic acids in pilot and industrial fermentation. In conclusion, these results indicated that L. thermotolerans LT1 can be used in hot winery regions to improve the acidity, color indexes, phenolic compounds, and aroma quality of wines.

Saccharomyces cerevisiae is widely applied in large-scale industrial bioethanol fermentation; however, little is known about the molecular responses of industrial yeast during large-scale fermentation processes. We investigated the global transcriptional responses of an industrial strain of S. cerevisiae during industrial continuous and fed-batch fermentation by oligonucleotide-based microarrays. About 28 and 62% of all genes detected showed differential gene expression during continuous and fed-batch fermentation, respectively. The overrepresented functional categories of differentially expressed genes in continuous fermentation overlapped with those in fed-batch fermentation. Downregulation of glycosylation as well as upregulation of the unfolded protein stress response was observed in both fermentation processes, suggesting dramatic changes of environment in endoplasmic reticulum during industrial fermentation. Genes related to ergosterol synthesis and genes involved in glycogen and trehalose metabolism were downregulated in both fermentation processes. Additionally, changes in the transcription of genes involved in carbohydrate metabolism coincided with the responses to glucose limitation during the early main fermentation stage in both processes. We also found that during the late main fermentation stage, yeast cells exhibited similar but stronger transcriptional changes during the fed-batch process than during the continuous process. Furthermore, repression of glycosylation has been suggested to be a secondary stress in the model proposed to explain the transcriptional responses of yeast during industrial fermentation. Together, these findings provide insights into yeast performance during industrial fermentation processes for bioethanol production.

Koumiss, a traditional fermented beverage made from mare’s milk, is typically consumed by nomads. Industrialized production of koumiss has been increasingly applied recently due to the increased demand for the beverage and awareness of its potential health benefits. However, it is unknown whether industrial koumiss is comparable to the traditional koumiss in terms of quality. In this study, we compared the microbiological and physicochemical properties in the industrial and traditional koumiss fermentation processes synchronously using culture-dependent and culture-independent approaches. Although Lactobacillus and Kazachstania species were similarly dominant in the bacterial and fungal communities, respectively, in both processes, the microbial counts and diversity in the traditional koumiss were significantly higher than those in the industrial koumiss. Furthermore, the traditional koumiss fermentation consumed more lactose, produced more flavor substances including acetic acid, lactic acid, ethanol, and free amino acids, and reached a lower pH value at the final stage. The physicochemical characters of traditional koumiss were mainly associated with Lactobacillus and Kazachstania species, which, in turn, were positively correlated with each other but negatively correlated with other non-dominant microbes. The starter was the major source of the microbial community of industrial koumiss, whereas both the starter and environment were the major sources of traditional koumiss. Random forest analysis recognized 11 significantly important genera as microbial indicators to distinguish industrial from traditional koumiss. Overall, this study shows that the microbial and physicochemical dynamics during the traditional and industrial fermentation of koumiss differ significantly, and the results obtained are valuable for improving the quality of industrial koumiss.

The fermentation performance of industrial yeast strains is influenced, among other things, by their genetic composition and the nature of the fermentable sugar, availability of nitrogen, and temperature. Therefore, to manipulate the fermentation process, it is important to understand, at a molecular level, the changes occurring in the yeast cell throughout industrial fermentation processes. With this aim in mind, using two-dimensional gel electrophoresis and matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF MS), we have examined the proteome of distillers yeast in an industrial context. Using yeast sampled from a local grain whisky distillery, we have prepared a detailed reference map of the proteome of distillers yeast and have examined in some detail the alterations in protein levels that occur throughout fermentation. In particular, as fermentation progresses, there is a significant increase in the levels of a variety of proteins involved in protecting against stress and nitrogen limitation. These results therefore give an insight into the stresses that yeast are exposed to in industrial fermentations and reveal some of the proteins and enzymes that are either necessary or important for efficient fermentation.

In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.

A Perspective on PSE in Fermentation Process Development and Operation

Seed Quality Assessment in an Industrial Fermentation Using MPCA

Electrical conductivity as a tool for analysing fermentation processes for production of cheese starters

State Identification and Control Strategy in Penicillin Fermentation

Brettanomyces yeasts — From spoilage organisms to valuable contributors to industrial fermentations

Piezoelectric bimorph actuator has the advantages of small size, fast response speed and high displacement accuracy, but its inherent hysteresis nonlinearity seriously affect the control accuracy and stability of the system. The dead-zone operator was incorporated into classical Prandtl–Ishlinskii model to enable the description of asymmetric hysteresis of piezoelectric bimorph actuator. A hybrid model approach was developed with neural network and improved Prandtl–Ishlinskii model, and it has the advantages of a neural network with ready-made training algorithms and improve the Prandtl–Ishlinskii (PI) model to describe the asymmetric hysteresis. The adaptive control method was derived from training algorithm of neural network, which can update the weight parameters of Play operator and Dead-zone operator in real time. Comparing the results without control, the RMSE of displacement error decreases by 61.35% with classic model, and decreases by 82.93% with hybrid model and proposed adaptive tracking control. Experimental results show that the proposed hybrid model and adaptive control approach can more effectively compensate the hysteresis of piezoelectric bimorph.