Optimization of Solid-state Fermentation Conditions of Sophora japonica cv.jinhuai by Response Surface Methodology

[Objectives]To optimize the solid-state fermentation process of Flos Sophorae Immaturus by Penicillium with Sophora japonica cv.jinhuai as raw material.[Methods]The fermentation conditions were optimized by single factor experiment and response surface methodology with quercetin content as the dependent variable.[Results]According to the established model,the optimal fermentation process of Flos Sophorae Immaturus was obtained as follows:temperature 29.97℃,time 6.88 d,rotation speed 180.86 rpm,inoculation amount 3.93 mL,and the expected content of quercetin was 34.8053 mg/g.Based on this,the fermentation parameters were adjusted,and the actual content was 33.67 mg/g,which was close to the predicted value.[Conclusions]The optimization of fermentation process of Flos Sophorae Immaturus by response surface methodology provides a reference for the development and utilization of this medicinal material.

Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.

Optimization of Fermentation Process for Human-like Collagen Production of Recombinant Escherichia coli Using Response Surface Methodology

In order to improve the production of human-like collagen III(HLC III)by fed-batch culture of recombinant Escherichia coli BL21,the Plackett-Burman and Box-Behnken design were applied to optimize the fermentation process parameters.Three variables(induction time,inoculum age and pH),which have significant effects on HLC III production,were selected from eight variables by Plackett-Burman design.With the regression coefficient analysis in the Box-Behnken design,a relationship between HLC III production and three significant factors was obtained,and the optimum levels of the three variables were as follows:induction time 3.2h,inoculum age 12.6 h and pH 6.7.The 3D response surface plots and 2D contour plots created by the Box-Behnken design showed that the interaction between induction time and pH and that between innoculum age and pH were significant.An average 9.68 g·L1HLC III production was attained in the validation experiment under optimized condition,which was 80%higher than the yield of 5.36 g·L1before optimization.

Characterization of flavor fingerprinting of red sufu during fermentation and the comparison of volatiles of typical products

Traditional Chinese red sufu is a popular condiment,and typical flavor is an important indicator of sensory qualities in commercial products.In this study,the volatile aroma analysis of Wangzhihe red sufu(WRS)from different fermentation stages and four typical red sufu products from different locations was carried out by gas chromatography-mass spectrometry(GC–MS),gas chromatography-mass spectrometry/olfactory(GC–MS/O),electronic nose(E-nose)and sensory evaluation.Results showed that 106 volatile compounds were identified in Wangzhihe red sufu during fermentation process,in which phenolics and alcohols were dominant at molded and salted phetze stages,while esters and alcohols became predominate at post-fermentation stage.The volatile aroma substances varied at each fermentation stage.Furthermore,86 volatiles,including 16 aroma-active compounds,were detected in four typical red sufu products.The multivariate analysis results showed the difference in samples from different fermentation stages and typical red sufu products according to GC–MS and E-nose analysis.Combined with flavor omics,discriminant model was established for effective discrimination of samples from different fermentation stages and locations,as well as references to sufu maturity extent.The study presented a new strategy for quality evaluation of red sufu,which could be the supplement to quality evaluation standards.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

Method of Soft-Sensor Modeling for Fermentation Process Based on Geometric Support Vector Regression

The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.

Optimization of Fermentation Process for Hyaluronic Acid Production by Streptococcus zooepidemicus

Hyaluronic acid （HA） is a high molecular weight glycosaminoglycan consisting of alternating D-glucuronic acid and N-acetylglueasamine and plays ex- tremely important roles in many biological processes. In this study, we optimized fermentation process for the production of HA by Streptococcus zooepidemicus ATCC35246, including fermentation broth composition and various fermentation parameters. The experimental results showed that the optimal fermentation broth composition was： glucose 45 g/L, yeast extract 10 g/L, tryptone 12 g/L, KH2PO4 2 g/L, K2HPO4 . 3H20 2 g/L, MgSO4 · 7H2O 2 g/L, and （NH4 ）2SO4 0.4 g/L. The optimal parameters involved in fermentation was： liquid volume 20%, pH 6. 0, rotation speed 180 r/min, fermentation temperature 35 ℃, fermentation duration 18 h, CTAB concentration 25 mg/L. Under the optimized conditions, the yield of HA was 0. 305 g/L, which was dramatically improved by 43.87% compared to that of 0. 212 g/L before optimization.

On-line estimation of concentration parameters in fermentation processes

It has long been thought that bioprocess, with their inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers. A novel software sensor is proposed to make more effective use of those meas- urements that are already available, which enable improvement in fermentation process control. The proposed method is based on mixtures of Gaussian processes (GP) with expectation maximization (EM) algorithm employed for parameter estimation of mixture of models. The mixture model can alleviate computational complexity of GP and also accord with changes of operating condition in fermentation processes, i.e., it would certainly be able to examine what types of process-knowledge would be most relevant for local models’ specific operating points of the process and then combine them into a global one. Demonstrated by on-line estimate of yeast concentration in fermentation industry as an example, it is shown that soft sensor based state estimation is a powerful technique for both enhancing automatic control performance of biological systems and implementing on-line moni- toring and optimization.

Forward heuristic breadth-first reasoning based on rule match for biomass hybrid soft-sensor modeling in fermentation process

Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.

First Development of a Biotechnological Ferment Based on a Consorsium of the Genus <i>Bacillus</i>for the Optimization of the Fermentation Process of Cassava Tubers

Due to its nutritional values, cassava has become an unavailable food and is one of the essential foods in the Republic of Congo. Fermentation of tubers is still traditional. Fiftyrod-shaped spore-forming bacteria were screened and carried out in batch mode for the fermentation abilities of cassava tubers in order to develop biotechnological starter. The Penetrometry Index (PI) has been used to screen bacteria and 16SrRNA as well as fibEone step multiplex PCR which were used to molecularly identify isolates. Emulsification Index, Proteolytic as well as amylolytic, and cellulolytic activities of some strains were quantitatively evaluated for prooving orgaleptic characterics. As results Bacillus subtilis (MT994787), Bacillus subtillis (MT994789), Bacillus tequilensis (MT994788), Bacillus safensis, and Bacillus subtilis have been identified. Single isolates were able to ferment tubers in 48 h and 72 hours meanwhile Bacillus consortia were able to shift fermentation of tubers from 48 hours to 24 hours. The consortium could be used as the major bacterial starters. Strains were associated with the ability to secrete biomolecules including biosurfactants, protease, amylase and cellulase.

Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system.However,ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass,substrate,feed-rate,etc.An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction(LSTDC) algorithm(LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process.As a new algorithm of adaptive critic designs,LSTDC-DHP is used to realize online learning control of chemical dynamical plants,where LSTDC is commonly employed to approximate the value functions.Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces.Simulation results demonstrate the effectiveness of LSTDC-DHP,and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.

Principles and Case Studies of Fed Batch Fermentation and Continuous Fermentation

Modern fermentation processes include a variety of fermentation methods,such as fed batch fermentation and continuous fermentation.This paper will focus on the principles and case studies of these two methods.Fed batch fermentation originates from fractionation fermentation with closed culture and adjustment of the pH value of the carbon source,to which the process of feeding the carbon source to the cell culture in a controlled manner has been added.This type of fermentation is more commonly used compared to the other.Continuous fermentation is also a closed fermentation system,which can operate without restrictions by continuous or intermittent addition of fresh nutrient media to the fermenter;however,it is susceptible to contamination by stray bacteria and metabolic inconvenience.

Effective and simple recovery of 1,3-propanediol from a fermented medium by liquid–liquid extraction system with ethanol and K_3PO_4

1,3-Propanediol,traditionally obtained from fossils,has numerous industrial applications,including use in the production of high performance polymers.The microbial production of 1,3-propanediol presents several opportunities,and the final purity grade determines its price and commercial viability.The development of novel separation technology could improve the economic viability of the bioproduction of 1,3-propanediol.Thus,we investigated salting-out extraction as a novel process for 1,3-propanediol recovery from fermentation broth.Initially,a screening for the best salt/solvent combination was conducted and then optimized using the response surface methodology.The solvents studied were methanol,ethanol,isopropanol and acetone,and the salts examined were K_2HPO_4,Na_2CO_3,K_2CO_3,(NH_4)_2SO_4,NaHPO_4,K_3PO_4 and C_6H_5NaO_7.The optimal extraction system consisted of 34 wt%K_3PO_4,28 wt% ethanol,and 38 wt% fermentation broth containing 23.0 g·L^(-1)1,3-propanediol,which gave the highest partition coefficient of 33 and recovery yield of 97%.The results demonstrated that salting-out extraction was a promising method for 1,3-propanediol recovery from fermentation broth.

Development of a new cleaner production process for cassava ethanol

A new cleaner production process for cassava ethanol has been developed, in which the thin stillage by-product was treated initially by anaerobic digestion, and the digestate further processed by hydrogen-form cation exchange resin before being recycled as process water to make mash for the next ethanol fermentation batch.Thus wastewater was eliminated and freshwater and energy consumption was significantly reduced. To evaluate the new process, ten consecutive batches of ethanol fermentation and anaerobic digestion at lab scale were carried out. Average ethanol production in the recycling batches was 11.43%(v/v) which was similar to the first batch, where deionized(DI) water was used as process water. The chemical oxygen demand(COD) removal rate reached 98% and the methane yield was 322 ml per gram of COD removed, suggesting an efficient and stable operation of the anaerobic digestion. In conclusion, the application of the new process can contribute to sustainable development of the cassava ethanol industry.

Monitoring and Fault Diagnosis for Batch Process Based on Feature Extract in Fisher Subspace

Multivariate statistical process control methods have been widely used in biochemical industries. Batch process is usually monitored by the method of multi-way principal component analysis (MPCA). In this article, a new batch process monitoring and fault diagnosis method based on feature extract in Fisher subspace is proposed. The feature vector and the feature direction are extracted by projecting the high-dimension process data onto the low-dimension Fisher space. The similarity of feature vector between the current and the reference batch is calcu- lated for on-line process monitoring and the contribution plot of weights in feature direction is calculated for fault diagnosis. The approach overcomes the need for estimating or filling in the unknown portion of the process vari- ables trajectories from the current time to the end of the batch. Simulation results on the benchmark model of peni- cillin fermentation process can demonstrate that in comparison to the MPCA method, the proposed method is more accurate and efficient for process monitoring and fault diagnosis.

Modified Recursive Least Squares Algorithm with Variable Parameters and Resetting for Time-Varying System

Based on the idea of the set-membership identification, a modified recursive least squares algorithm with variable gain, variable forgetting factor and resetting is presented. The concept of the error tolerance level is proposed. The selection criteria of the error tolerance level are also given according to the min-max principle. The algorithm is particularly suitable for tracing time-varying systems and is similar in computational complexity to the standard recursive least squares algorithm. The superior performance of the algorithm is verified ma simulation studies on a dynamic fermentation process.

Contents Variation Analysis ofγ-Amino Butyric Acid in Semen sojae praeparatum Fermentation Using Online Pre-Column Derivatization-HPLC

This paper reported the contents variation analysis ofγ-amino butyric acid(GABA)in Semen sojae praeparatum(SSP)which is a famous traditional Chinese medicine.High performance liquid chromatography(HPLC)was used and GABA was derivatized by online pre-column derivatization with o-phthalaldehyde(OPA).To validate this method,the precision,stability,repeatability and recovery were discussed.In the concentration range from 0.0125 to 0.400 mg/m L,the calibration curve for GABA was linear and the regression equation was obtained with correlation coefficient(R2)of 0.9999.Relatively high levels of GABA exist in SSP and the content changes of GABA at different time points during the fermenting process were detected.At the"yellow cladding"stage,GABA level was very low or even undetectable;the"secondary fermentation"stage witnessed a rapid increase of GABA content to 1.39-5.52 mg/g,which remained stable after 18 days of"secondary fermentation".This study demonstrated that GABA was generated at the"secondary fermentation"stage,revealing the significance and rationality of the"secondary fermentation"stage in the fermenting process of SSP.On the other hand,it suggested the downside of taking soy isoflavones as the only measurement in existing quality assessment and optimization approach for the fermenting process of SSP.

Optimization of fermentation process of papaya sauerkraut using response surface method

Papaya,a tropical fruit was used as the raw material to produce sauerkraut in the study.Three lactic acid bacteria strains isolated from papaya were added to the sauerkraut to facilitate the fermentation of papaya sauerkraut.In the fermentation process,the dynamic changes of total acid in sauerkrauts at different levels of sugar concentration,salt concentration,inoculation and temperature were studied.The response surface method was used to study the effects of changes in multiple factors at the same time.On the basis of“one-variable-at-a-time”approach,the response surface method optimized papaya sauerkraut fermentation process.According to the change of total acid in single factor,29 experiments were designed by 4×3 factorial central composite design.The optimum fermentation conditions were obtained as follows:sugar at 3.8%,salt at 2.8%,inoculation at 5%,and temperature at 31℃.

Impact of microbial self-induced anaerobiosis fermentation(SIAF)on coffee quality

The fermentation of natural(NC)and pulped coffee(PC)was performed with a conventional method(platform)and under self-induced anaerobic fermentation(SIAF).Of the 12 samples analyzed during the fermentation process,the highest temperature was obtained by the SIAF method(30.5℃ for NC and 29.67℃ for PC)with 87 h of fermentation.Nonvolatile compounds(36 samples)were evaluated by high-performance liquid chromatography.Fermentation in the SIAF method contributed to the maximum amount of citric acid(2.534 mg/g)in pulped coffee and acetic acid(6.04 mg/g)and lactic acid(2.533 mg/g)in NC.Furan was the primary chemical class detected,followed by ketones and pyrazines.All coffees(12 samples)were evaluated five times and classified as specialty coffees(>80 points)following Specialty Coffee Association(SCA)protocols.The pulped coffee processed by the SIAF method showed a 2.83-point increase in the sensory score compared to the conventional method.Therefore,the SIAF method is accessible to producers,contributes to coffees with differentiated sensory profiles,and increases beverage quality.

Model penicillin fermentation by least squares support vector machine with tuning based on amended harmony search

Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search （AHS） algorithm is developed to adjust the hyper-parameters of least squares support vector machine （LS-SVM） in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.