Glutamic Acid Fermentation Research Articles

The Effect of Polyglutamic Acid Produced from Glutamic Acid Wastewater on Maize Growth

This experiment uses polyglutamic acid water-soluble fertilizer produced from glutamic acid fermentation waste liquid as the experimental fertilizer to study its impact on maize yield and quality in maize cultivation. The results showed that compared to farmers who are accustomed to fertilization, the application of the new water-soluble polyglutamic acid fertilizer reduced the bald head length of corn by 0.7 cm; The weight of 100 grains of corn increased by 0.7g, resulting in an increase of 26.7 kilograms of corn per mu, with a yield increase of 5%.

Coupling fermentation of glutamic acid and γ-polyglutamic acid and preparation of poly(amino acid) superabsorbent polymers

γ-polyglutamic acid (γ-PGA) is a biomarker that can be directly obtained by microbial fermentation. Poly(amino acid) superabsorbent polymers (SAPs) were prepared with purified γ-PGA as raw material and ethylene glycol diglycidyl ether (EGDGE) as a cross-linking agent. However, γ-PGA fermentation broth has a high viscosity, requires complex extraction and separation processes, and entails high energy consumption, resulting in the high cost of poly (amino acid) SAPs. Therefore, the coupling fermentation processes of glutamate polyglutamic acid, the process of using glutamate fermentation broth instead of pure glutamate powder for fermentation, and the process of treating the fermentation broth under conditions of centrifugation, UV irradiation, and high temperature, were studied. The results showed that the yield of γ-PGA after centrifugation decreased by 5%, but it did not affect the synthesis of hydrogels, and the addition of γ-PGA fermentation broth had a significant effect on the performance of γ-PGA-co-PASP SAPs. The proposed method not only helps avoid the separation of complex γ-PGA fermentation broth and reduces the cost, but it also helps improve the performance of the super-absorbent resin, which has great application potential.

Enhanced phycocyanin production from Spirulina subsalsa via freshwater and marine cultivation with optimized light source and temperature

To find out optimum and cost-efficient strategy for phycocyanin production, the effect of light source and temperature on Spirulina subsalsa growth were studied in chemically defined freshwater medium and seawater supplied with wastewater from glutamic acid fermentation tank. Maximum growth rate and the highest phycocyanin content were obtained by 35 °C and green light, respectively. A two-stage cultivation strategy was proposed and applied, which combines biomass accumulation at 35 °C and phycocyanin synthesis simulated under green light. As a result, phycocyanin production reached 70 mg/L/d and 11 mg/L/d from freshwater and seawater medium, respectively. With all tested conditions, a strong correlation between biomass and phycocyanin/chlorophyll ratio, rather than phycocyanin, revealed the dependence of Spirulina subsalsa growth on coordinating regulation of photosynthetic pigments. The relationship between growth and phycocyanin production under various light and temperature can be a good basis for improving phycocyanin production from Spirulina subsalsa with or without freshwater consumption.

Modeling uncertain processes with interval random vector functional-link networks

This paper presents a new approach to building an interval model for an industrial process with uncertainty that employs an interval neural network (INN), which can solve problems such as model structure demands and complexity limitations in the conventional unknown but bounded (UBB) errors method. A new architecture for an interval random vector functional-link network (IRVFLN) and its learning algorithm with penalty factors are proposed, to solve the problems such as the local minima, slow convergence, and very poor sensitivity to learning rate settings in the interval feed-forward neural networks with error back-propagation (IBPNNs). As an application case study, the IRVFLN is used to model the glutamic acid fermentation process under the condition of bounded-error data, and the test results indicate that the accuracy of the IRVFLN model meets the manufacturing requirements. The comparison is performed with IBPNN, and the results demonstrate that the proposed network outperforms IBPNN both on effectiveness and efficiency. Also, a comparison is given with a crisp (point-valued) approach using RVFLN, and the results show that the crisp approach is less reliable when existing uncertainties in measuring or process.

Microbial extraction of biotin from lignocellulose biomass and its application on glutamic acid production

Biotin (vitamin B7) is an important nutrient for various fermentations. It is abundant in agricultural lignocellulose biomass and maintains stable in biorefinery processing chain including acid pretreatment, biodetoxification and saccharification. Here we show a microbial extraction of biotin from biotin-rich corn leaves hydrolysate. Corynebacterium glutamicum was found to have the highest biotin uptake capacity among different biotin auxotrophic microorganisms, and it was further significantly increased by overexpressing the bioYMN gene cluster encoding biotin transporter. Finally 250 folds greater biotin was extracted by recombinant C. glutamicum (303.8 mg/kg dry cell) from virgin corn leaves (1.2 mg/kg), which was far higher than that in commonly used fermentation additives including yeast extract (∼2 mg/kg), molasses (∼1 mg/kg) and corn steep liquor (∼0.75 mg/kg). The biotin extracted from corn leaves was successfully applied to glutamic acid fermentation. This is the first report on microbial extraction of biotin from lignocellulose biomass and fermentation promotion application.

Bayesian Takagi–Sugeno–Kang Fuzzy Model and Its Joint Learning of Structure Identification and Parameter Estimation

In this paper, a novel B ayesian T akagi– S ugeno– K ang (BTSK) fuzzy model and its j oint l earning method BTSK-JL of structure identification and parameter estimation are proposed for regression tasks from the perspective of Bayesian inference framework with a prior assumption about the number of fuzzy rules. Unlike most of existing TSK fuzzy systems where both their structure identification and parameter estimation of each fuzzy rule involved in them are learnt in a separate manner, BTSK-JL can determine the number of fuzzy rules and antecedent/consequent parameters of rules simultaneously in the proposed model. In order to guarantee their optimal solutions, BTSK-JL adopts a particle filter method to find the maximum-a-posterior value of the parameters. Due to taking into account the subtle interaction between input and output spaces, BTSK-JL can obtain good predictive performance and a set of compact fuzzy rules. Fuzziness and probability can work complementarily rather than competitively for such a TSK fuzzy system modeling. Experimental results on four time-series datasets and a glutamic acid fermentation process dataset have shown the validity and effectiveness of the proposed model.

Rich biotin content in lignocellulose biomass plays the key role in determining cellulosic glutamic acid accumulation by Corynebacterium glutamicum

BackgroundLignocellulose is one of the most promising alternative feedstocks for glutamic acid production as commodity building block chemical, but the efforts by the dominant industrial fermentation strain Corynebacterium glutamicum failed for accumulating glutamic acid using lignocellulose feedstock.ResultsWe identified the existence of surprisingly high biotin concentration in corn stover hydrolysate as the determining factor for the failure of glutamic acid accumulation by Corynebacterium glutamicum. Under excessive biotin content, induction by penicillin resulted in 41.7 ± 0.1 g/L of glutamic acid with the yield of 0.50 g glutamic acid/g glucose. Our further investigation revealed that corn stover contained 353 ± 16 μg of biotin per kg dry solids, approximately one order of magnitude greater than the biotin in corn grain. Most of the biotin remained stable during the biorefining chain and the rich biotin content in corn stover hydrolysate almost completely blocked the glutamic acid accumulation. This rich biotin existence was found to be a common phenomenon in the wide range of lignocellulose biomass and this may be the key reason why the previous studies failed in cellulosic glutamic acid fermentation from lignocellulose biomass. The extended recording of the complete members of all eight vitamin B compounds in lignocellulose biomass further reveals that the major vitamin B members were also under the high concentration levels even after harsh pretreatment.ConclusionsThe high content of biotin in wide range of lignocellulose biomass feedstocks and the corresponding hydrolysates was discovered and it was found to be the key factor in determining the cellulosic glutamic acid accumulation. The highly reserved biotin and the high content of their other vitamin B compounds in biorefining process might act as the potential nutrients to biorefining fermentations. This study creates a new insight that lignocellulose biorefining not only generates inhibitors, but also keeps nutrients for cellulosic fermentations.

Screen of Poly-γ-gamma Glutamic Acid Producing Bacteria and Their Glutamic Acid Fermentation Broth’s Function on the Drought Resistance of Maize

Screen of Poly-γ-gamma Glutamic Acid Producing Bacteria and Their Glutamic Acid Fermentation Broth’s Function on the Drought Resistance of Maize

Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

Modeling and optimization of the glutamic acid fermentation process using computational intelligence techniques

This paper presents a framework for modeling and optimizing the glutamic acid fermentation process using computational intelligence techniques. Considering the special characteristics of such an industrial process, we propose a two-phase optimization strategy to maximize the conversion rate and product concentration of the glutamic acid. Neural network ensembles and an improved Differential Evolutionary Algorithm (DEA) with a non-inferior sorting scheme and niche technology are employed for problem solving. This work provides an approach for design of a model-free optimal control system for the fed-batch fermentation process. Experimental results are promising and demonstrate the applicability of the proposed modeling and optimization techniques for real world applications.

In vitro degradation of lysine by ruminal fluid-based fermentations and by Fusobacterium necrophorum

The objective of these studies was to characterize some factors affecting lysine degradation by mixed ruminal bacteria and by ruminal Fusobacterium necrophorum. Mixed ruminal bacteria degraded lysine, and addition of pure cultures of F. necrophorum did not increase lysine degradation. Addition of acetic or propionic acid strikingly reduced NH3 production from lysine by mixed ruminal bacteria at pH 6, but not at pH 7. Although typical ruminal environments with acidic pH and normal concentrations of volatile fatty acids might inhibit lysine degradation by F. necrophorum, ruminal fluid contained enough bacteria with a lysine-degrading capacity to ferment 50mM lysine in vitro. Of 7 strains of ruminal F. necrophorum tested, all grew on both lactate and lysine as the primary energy source. Both subspecies of ruminal F. necrophorum (necrophorum and funduliforme) used lysine as a primary C and energy source. Lysine and glutamic acid were effectively fermented by F. necrophorum, but alanine and tryptophan were not, and histidine and methionine were fermented only to a minor extent. The end products of lactate fermentation by F. necrophorum were propionate and acetate, and those of lysine degradation were butyrate and acetate. Fermentation of glutamic acid by F. necrophorum yielded acetate and butyrate in a ratio near to 2:1. The minimum inhibitory concentration of tylosin for F. necrophorum was not dependent on whether bacteria were grown with lactate or lysine, but F. necrophorum was more susceptible to monensin when grown on lysine than on lactate. Although F. necrophorum is generally resistant to monensin, the ionophore may reduce lysine degradation by F. necrophorum in the rumen. The essential oil components limonene, at 20 or 100μg/mL, and thymol, at 100μg/mL, inhibited F. necrophorum growth, whereas eugenol, guaiacol, and vanillin had no effect. Our findings may lead to ways to minimize ruminal lysine degradation and thus increase its availability to the animal.

Response of cholecystokinin and digestive enzyme mRNA levels to various feed ingredients in yellowtail Seriola quinqueradiata

Cholecystokinin (CCK) is the key regulator hormone that stimulates the secretion of digestive pancreatic enzymes in vertebrates. In fish, little is known about the mechanism of induction of CCK in the digestive tract by feed ingredients. To investigate the response of CCK and digestive enzymes to fish feed ingredients in yellowtail Seriola quinqueradiata, we performed a number of experiments in which we measured the mRNA levels of CCK, trypsin, and lipase after oral administration of a single bolus of the ingredients. We administered fish meal and fish oil in experiment 1; high and low concentrations of fish meal in experiment 2; and five different dietary protein sources (fish meal, soybean meal, soy protein concentrate, corn gluten meal, and glutamic acid fermentation by-products) in experiment 3. In experiments 1 and 3, only fish meal significantly increased the mRNA levels of CCK and digestive enzyme. In experiment 2, a high concentration of fish meal [20 % (w/v)] significantly increased CCK and trypsin mRNA levels, but a low concentration of fish meal [1 % (w/v)] did not. These results suggest that high concentrations of fish meal (the protein source in fish feed) has the most potent effect on stimulation of CCK synthesis and secretion of digestive enzymes in yellowtail.

Production of succinic acid and lactic acid by Corynebacterium crenatum under anaerobic conditions

A new succinic acid and lactic acid production bioprocess by Corynebacterium crenatum was investigated in mineral medium under anaerobic conditions. Corynebacterium crenatum cells with sustained acid production ability and high acid volumetric productivity harvested from the glutamic acid fermentation broth were used to produce succinic acid and lactic acid. Compared with the first cycle, succinic acid production in the third cycle increased 120% and reached 43.4 g/L in 10 h during cell-recycling repeated fermentations. The volumetric productivities of succinic acid and lactic acid could maintain above 4.2 g/(L·h) and 3.1 g/(L·h), respectively, for at least 100 h. Moreover, wheat bran hydrolysates could be used for succinic acid and lactic acid production by the recycled C. crenatum cells. The final succinic acid concentration reached 43.6 g/L with a volumetric productivity of 4.36 g/(L·h); at the same time, 32 g/L lactic acid was produced.

Parameter optimization algorithm for support vector machine based on predatory search genetic algorithm

Based on the fact that generalization and fitting accuracy of the Support Vector Machine(SVM) model depend on its parameters setting,a predatory search genetic algorithm was proposed to determine the parameters of the SVM.The target of this algorithm was to minimize the fitting error of output and three parameters of SVM were used as the decision variables.An application example on the glutamic acid fermentation process shows that the method can improve the training accuracy and forecast accuracy of glutamate concentration and is an effective way to optimize the parameters of SVM.

A Fault Diagnosis Method for Fermentation Process

Process fault diagnosis requires the on-line information on process state variables that are often inaccessible in real-time for the processes like a fermentation process. A composite model is proposed, combining a kinetic model of the first principles and a neural network model that models the kinetic model parameters changes, to estimate on line the states. This composite model can retain and enhance the process knowledge, at the same time, avoid the complexity of modeling the entire process by kinetics. The estimated process states from the composite model are then fed to a wavelet network for fault detection and diagnosis. The proposed system is successfully applied to a glutamic acid fermentation process, demonstrating the feasibility and effectiveness of the proposed system.

Bioreactor design via spreadsheet––a study on the monosodium glutamate (MSG) process

Preliminary design calculation of a unit operation is always necessary to determine an order of magnitude of the proposed chemical plant. This paper describes an application of a spreadsheet in preliminary design of a bioreactor. There are a few steps in bioreactor design which are mass/mole balances and energy balances calculations before carrying out the bioreactor sizing. A spreadsheet was used as a tool to make a quick and accurate calculation. Glutamic acid fermentation is used to describe the overall method in this bioreactor design via spreadsheet. The method presented here is easy to learn and easy for the designer to scale up and to simulate different operating conditions to meet an optimum design from time to time by changing only a few variable such as flow rate and substrate concentration of the feed in the previously constructed spreadsheet.

Product and by-product distributions in glutamic acid fermentation by Brevibacterium flavum: effects of the oxygen transfer

Production of l-glutamic acid (GA) was accomplished by using Brevibacterium flavum (DSM 20411) in 3.5 dm 3 laboratory scale bioreactors at 30°C, initial pH 7.2, penicillin ratio 10 000 U g −1 DW, agitation rate 150–500 min −1, and air flow rate 0.1–0.2 vvm. Cell and GA concentrations and concentrations of other amino acids and organic acids were measured throughout batch fermentations. By-products varied in number and concentration depending on the amount of oxygen supplied to the broth. Very low oxygen transfer rate favoured pyruvic acid and lactic acid production; while at higher oxygen transfer levels α-ketoglutaric acid and succinic acid were favoured. The 0.1 vvm and 250 min −1 conditions were optimum for maximum GA production and selectivity.

A method for the determination of pyruvate carboxylase activity during the glutamic acid fermentation with Corynebacterium glutamicum

A discontinuous lactate dehydrogenase coupled assay is described for the evaluation of the pyruvate carboxylase activity (Pc, EC 6.4.1.1) in a glutamate overproducing strain of Corynebacterium glutamicum. After an initial permeabilisation period of the cells, the method consisted of the fluorometric determination of the remaining pyruvate level after transformation into oxaloacetate by the endogenous Pc. The assay was demonstrated to be powerful and enabled the determination of the C. glutamicum Pc activity grown on different carbon sources. Besides, this method was used to assay Pc activity in C. glutamicum 2262 during a temperature triggered glutamate producing process with biotin excess or limitation.

Importance of Phosphoenolpyruvate Carboxylase of Corynebacterium glutamicum during the Temperature Triggered Glutamic Acid Fermentation

To give clues about the respective importance of phosphoenolpyruvate carboxylase (PEPc) and pyruvate carboxylase (Pc) in Corynebacterium glutamicum metabolism during a temperature triggered glutamic acid fermentation, PEPc activity was genetically amplified and Pc activity was suppressed by biotin limitation in the culture medium. In absence of Pc activity, glutamate production was dramatically reduced whereas lactate excretion was strongly increased. Whereas PEPc amplification in excess of biotin (4mg/L) only slightly modified the cell kinetics, under biotin limiting conditions this amplification strongly improved the glutamate production (4μg/L). When Pc was absent, PEPc activity was sufficient to allow up to 70% of the maximal glutamate production rate and seemed to have an important anaplerotic role, especially at the beginning of the production phase. In contrast, Pc was predominant during the remainder of the glutamate fermentation.

An extended Kalman filter and neural network cascade fault diagnosis strategy for the glutamic acid fermentation process

The purpose of this paper is to present results that were obtained in fault diagnosis of glutamic acid fermentation process. The diagnosis algorithm is based on the extended Kalman filter (EKF) and neural network classifier. Inputs of the network are the process I/O data, such as pressure and temperature, parameters estimated by EKF, and state values calculated by dynamic equations, while outputs of the network are process fault situations. A batch glutamic acid fermentation process is studied as a test case, which is with 13 measurements, five estimated parameters, three calculated states, and 11 fault situations. The running test results show that the strategy appears to be better suited to diagnose faults of such an industrial process.