Abstract
Gamma aminobutyric acid (GABA) is a biologically active compound that has attracted significant attention in recent years due to its beneficial effects on the nervous system and its role in brain synthesis processes. GABA is widely produced by fermentation method, and its microbial biosynthesis can be improved by adjusting operational parameters and supplementing nutrients during fermentation. However, the optimal fermentation conditions for GABA production vary depending on raw materials and microbial strain used. Rice bran is known as a low-cost and nutritious raw material, capable of producing greater GABA content than other substrates. This study aimed to enhance GABA production from rice bran slurry fermented by Lactobacillus brevis VTCC-B397 by adjusting fermentation conditions, including temperature (25–37 °C), pH (3.0–6.0), monosodium glutamate (MSG) concentration (0–1 M) and fermented time (0–72 hr). The effects of these factors on bacterial cell density and GABA content were assessed over the fermentation period. Furthermore, response surface methodology was applied to optimise the medium and fermentation parameters, as well as to investigate the interactions between factors that influence the fermentation process. The biosynthesis pathway of GABA was considerably impacted by temperature, pH, and MSG content. The optimal conditions for GABA production from defatted rice bran solution were found to be 0.56 M MSG, 34 °C, and pH 5.12. Under optimised conditions, the highest GABA yield achieved was 7.69 g/L.
Introduction
Gamma aminobutyric acid (GABA) is a non-protein amino acid widely distributed in nature. It can be synthesised through fermentation from various raw materials and has received attention for its positive health effects (Diez-Gutiérrez et al., 2022). GABA is a bioactive compound that functions as a neurotransmitter in the central nervous system, playing a key role in brain metabolism and contributing to various physiological functions. In addition, GABA is crucial for the nervous and cardiovascular systems, involving in various cellular activities within the body (Hou et al., 2024). It has been shown to reduce the risk of chronic diseases in mammals, maintain a high seizure threshold, and contribute to memory formation and cognitive function (Lee et al., 2023; Pannerchelvan et al., 2023). Due to its diverse physiological benefits, the commercial demand for GABA is on the rise. It is widely utilised in purified forms and as a supplement in functional foods. GABA is widely found in microorganisms, plants, and animals and it can be synthesised through various methods, including chemical synthesis, plant extraction, and microbial fermentation (Li et al., 2023). Among these, biosynthesis is considered as a simple, high-yield efficient and environmentally friendly method, making it a promising choice for large scale production of GABA, particularly in food industry. Up to now, numerous microbial strains have bene isolated and optimised for GABA production, with lactic acid bacteria (LAB) emerging as some of the most widely used and effective microorganisms. Their safety and efficiency in GABA synthesis make LAB an ideal choice for industrial production (Cui et al., 2020; Pannerchelvan et al., 2023).
Rice bran, an agricultural byproduct with high nutritional value, particularly rich in L-glutamate and essential nutrients for the growth of specific microorganisms, has been proven in numerous studies to be an excellent fermentation medium for producing GABA via LAB fermentation. According to the reviews by Hou et al. (2024) and Luo et al. (2021), rice bran has been demonstrated to be a highly efficient raw material for GABA production compared to many other culture media. For instance, GABA production from rice bran is superior to that from soybean residue, raw milk, a mixture of dairy sludge, molasses combined with soybean meal, and litchi juice, which yield GABA concentrations of 6 g/L (Lai et al., 2022), 4.31 g/L (Zhang et al., 2022), 0.39 g/L (Galli et al., 2022), 0.35 g/L (Falah et al., 2024), and 1.34 g/L (Wang et al., 2021), respectively. Additionally, global rice production is projected to increase in the near future, resulting in a corresponding rise in rice bran production (Rahman & Zhang, 2023). Until now, most of rice bran and defatted rice bran are utilised for purposes such as animal feed, biofuel production, and other industrial uses, making them relatively low-cost resources. This efficiency, abundance, and affordability position rice bran as an attractive and economically viable raw material for GABA production. Utilising rice bran extract as a fermentation substrate for GABA production not only diversifies the raw materials available for GABA synthesis but also offers considerable economic and sustainability advantages. This approach significantly reduces agricultural waste and adds value to the rice production chain, thereby promoting more sustainable agricultural practices.
Microorganisms synthesise GABA through the action of glutamate decarboxylase (GAD), an enzyme that catalyses the conversion of L-glutamic acid into GABA. Thus, the biosynthesis of GABA by LAB specifically, and microorganisms in general, is influenced by factors such as pH, temperature, and the availability of L-glutamic acid. Numerous studies have shown that the performance of GABA biosynthesis through LAB fermentation can be influenced by various factors, including culture medium, presence of additives, and the nutrient composition of the bacterial growth environment (Pannerchelvan et al., 2023; Sarasa et al., 2020). In previous studies using various materials, the authors compared GABA content with and without the addition of external glutamic acid. The results indicated that the GABA content was significantly higher when glutamic acid was added to the fermented medium (Eamarjharn et al., 2016; Ke et al., 2016; Park et al., 2019; Zhong et al., 2019). This increase is due to the enhanced substrate availability for the α-decarboxylation reaction, leading to greater GABA production. However, the use of glutamic acid-enriched media for GABA production has not been widely applied in industrial settings due to their high cost. As production costs remain a critical challenge that restricts both the scalability and accessibility of GABA in industrial applications, optimising GABA synthesis has become an area of growing interest. One effective strategy to addressing this challenge is the utilisation of more affordable resources and improving bacterial metabolite efficiency by optimising medium and operating conditions. This is particularly crucial for industrial-scale production, as it not only reduces overall production costs but also makes GABA more widely available for diverse applications in food, pharmaceuticals, and other industries. Additionally, optimization helps minimise waste, conserve resources, align with sustainable practices, and ensure consistent product quality. Numerous studies have shown that optimising fermentation conditions significantly improves GABA synthesis performance across various bacterial strains and fermentation media (Lin et al., 2024; Yee et al., 2024; Zou et al., 2024). However, each substrate and strain exhibit distinct fermentation characteristics, leading to variations in optimal GABA production conditions. Thus, accurately determining the suitable fermentation conditions for specific raw materials and microbial strains is crucial and forms the foundation for evaluating the comprehensive efficiency of these conditions in practical production. Currently, there has been no research to determine the optimal conditions for GABA fermentation from defatted rice bran extract.
In our previous research, we evaluated the effectiveness of various additives when incorporated into the rice bran fermentation medium for GABA production using Lactobacillus brevis VTCC-B397 (Lai et al., 2022). To further enhance the GABA synthesis performance from rice bran extract by L. brevis VTCC-B397, this study assessed the effects of key factors, including monosodium glutamate (MSG) concentration, pH control, temperature, and time in fermentation. The study then optimised medium and fermentation conditions for LAB growth to achieve the highest GABA synthesis yield, utilising response surface methodology (RSM). These improvements contribute to establishing an optimal fermentation environment for maximising GABA production from cost-effective raw materials.
Materials and methods
Materials
This study used rice bran from Oryza sativa OM 4900 cultivar, obtained from rice grain and milling process, with a milling degree of 10% and a moisture content of 8%. The conditions for processing the rice bran and preparing the fermented solution were conducted following the procedures described by Lai et al. (2022). The fermentation was performed by L. brevis VTCC-B397, isolated from naturally picked vegetable, at a bacterial density of 108 CFU/ml. The propagation conditions were carried under similar to those reported by Lai et al. (2021).
Optimised fermentation conditions of GABA synthesis from defatted rice bran broth
First, the study evaluated the influence of individual factors on GABA biosynthesis from defatted rice bran broth. Based on the growth condition ranges recommended by the manufacturer and preliminary experiments conducted across a wider range, the optimal conditions for microbial growth and GABA biosynthesis were identified. The experimental range was narrowed down to focus on the effective conditions. The effect of additive concentration was investigated to optimise the substrate sources by adjusting the concentration of MSG in medium to 0, 0.25, 0.5, 0.75, and 1 M. The fermentation was carried out at 30 °C and initial pH 5.0. After that, the fermentation process with different values of medium pH including 3.0, 4.0, 5.0, and 6.0 was tested. During fermentation, pH conditions were adjusted with H2SO4 to keep the pH during fermentation stable and unchanged. MSG concentration was selected as the optimal value obtained for GABA production from the previous section. Finally, the fermentation temperature was investigated at values of 25, 30, 34, and 37 °C, using the previously selected MSG concentration and medium pH value. The fermentation process was carried out in a shaking incubator. The fermented broth was sterilised at 121 °C for 10 min to inactivate bacteria. The effects of these conditions were evaluated at different fermentation time based on the bacterial density and GABA content produced, respectively.
Based on the single-factor results, the optimal ranges and three different coded levels (−1, 0, and +1) of each factor were selected for the RSM experiment. The investigated factor was used as independent variables in build the matrix to determine the interactions of these variables and optimise GABA production based on the central composite circumscribed design, while other fermented conditions were fixed. A total of 20 treatments were designed, including six centre point runs, with each treatment being repeated three times. The ANOVA, regression coefficients, and model fitting to the quadratic model of the response factors were analysed by Design-Expert software (version 13, Stat-Ease. Inc., Minneapolis, MN, USA). The significance of all terms was analysed at p < .05. Moreover, the validity of the model was verified by comparing the model predicted values with the experimental values.
A quadratic polynomial model was employed to fit the independent data for the response Y, using the general mode with three factors. The equation of objective function model Y is
Y: the amount of GABA produced (g/L).
Xi: independent variables representing MSG concentration, pH and temperature.
b0 is free coefficient, bi, bij is the coefficients that accompany single variables, interaction variables and squared variables.
Analytical methods
GABA content was analysed following the method described by Zhang et al. (2022), which involved using Berthelot reagent (a solution of borate, phenol and hypochlorite in base) and measuring absorbance at 645 nm of wavelength.
Cell density was determined by the spectrophotometer to measure the optical density at 600 nm (Beal et al., 2020).
Statistical treatment
The experimental results were expressed as means of triplicate ± SD. Statistical analysis was performed using one-way ANOVA, followed by the least significant difference test. The values were considered at 5% significance level. All statistical data were conducted by R software (version 3.5.3).
Results and discussion
Effect of MSG concentration on GABA production from defatted rice bran extract by L. brevis VTCC-B397
The effect of MSG concentration added to the culture medium on the growth of L. brevis VTCC-B397 and GABA synthesis are shown in Figures 1 and 2. The results in Figure 1 show that cell density increased rapidly, reached a steady state after the first 24 hr, and then began to decline. The bacterial density was inversely related to the MSG concentration. As the amount of MSG added to the culture increased, microbial growth progressively decreased due to metabolic disorders and raised osmotic pressure caused by higher MSG levels. As a result of mitosis, water was lost from the cells, potentially leading to cell shrinkage, death, and inhibition of GAD activity (Zhu et al., 2024). The disruption in the cellular environment might hinder the growth and metabolic processes essential for efficient GABA production.
Effect of MSG concentration in medium on growth of L. brevis VTCC-B397 (■: 0 M, □: 0.25 M, ●: 0.5 M, |$\circ $|: 0.75 M, ▲: 1 M). MSG = monosodium glutamate.
Effect of MSG concentration in medium on GABA production L. brevis VTCC-B397 (■: 0 M, □: 0.25M, ●: 0.5 M, |$\circ $|: 0.75 M, ▲: 1 M). MSG = monosodium glutamate.
However, it is evident from the data in Figure 2 that the increase in concentration of MSG positively impacted the amount of GABA produced. When compared to control samples without MSG, samples supplemented with MSG produced 8–23 times more GABA. The rise in GABA content demonstrated that the added MSG served as a substrate for GAD activity. When the MSG concentration in the medium raised from 0 to 0.5 M, the GABA yield increased significantly, reaching its peak value of 7.62 g/L at 0.5 M after 24 hr of fermentation, although the rate of cell growth appeared to be slightly inhibited. When the concentration of MSG was further raised from 0.5 to 1 M, the amount of GABA decreased compared to the yield at 0.5 M. This finding was consistent with previous studies, which discovered the growth of some bacterial strains was adversely affected by high glutamate concentration (Park et al., 2021; Rayavarapu et al., 2021). This is explained by the fact that the higher glutamate concentration becomes more toxic to bacteria and inhibits the expression of the gadB gene, which encodes the enzyme needed to synthesise GABA in LAB bacteria (Yunes et al., 2016).
Over time, the GABA content gradually decreased after 24 hr of fermentation. This might be the consequence of GABA transaminase activity-induced GABA catabolism. The activity of GABA transaminase could decrease the amount of GABA produced by converting GABA to succinic semialdehyde (Yogeswara et al., 2020). In addition, the fact that MSG had no effect on bacterial growth indicated that GABA could be consumed by the cells to maintain their viability during the cultivation phase.
The fermentation time required for GABA synthesis varies based on the medium composition and the strain of Lactobacillus. Achieving optimal GABA production in rice bran extract medium required only 24 hr, significantly shorter than the typical 36–48 hr for other media. For example, LAB from kimchi took 40 hr in De Man - Rogosa - Sharpe (MRS) medium with MSG (Hwang & Park, 2020), a medium of dairy sludge, molasses, and soybean meal required 48 hr with L. fermentum 4–17 (Falah et al., 2024), and MSG with L-glutamic acid mixtures needed 32 hr for L. brevis CD0817 (Wang et al., 2024a). However, the GABA content gained in the defatted rice bran extract medium was still much higher than that of other raw materials as mentioned above. The superior GABA content produced in defatted rice bran extract is attributed to its rich composition, including dietary fibre, proteins, carbohydrates, and lipids, which are favourable for GABA biosynthesis by Lactobacillus. Additionally, rice bran contains essential minerals such as magnesium, manganese, and calcium, which contribute to enhanced enzyme activity and intracellular synthesis (Wu et al., 2018). Furthermore, its abundance of vitamin B6 derivatives, like pyridoxal phosphate, a crucial coenzyme in converting glutamate to GABA, further supports efficient production (Cui et al., 2020; Sapwarobol et al., 2021; Zarei et al., 2017).
The results proved that the addition of MSG to defatted rice bran broth was necessary for effective GABA synthesis. Maximum GABA production was observed at 24 hr, with a slight decrease or no significant change afterward. Among the tested conditions, GABA content reached its peak and showed no statistically significant difference between MSG concentrations of 0.5 M and 0.75 M after 24 hr of fermentation. In order to promote the GABA synthesis of L. brevis VTCC-B397 while lowering the MSG level, the favourable condition of 24 hr and 0.5 M of MSG concentration was chosen, corresponding to a GABA content of 7.62 g/L.
Effect of pH control on GABA production from defatted rice bran extract by L. brevis VTCC-B397
The effect of pH on the L. brevis VTCC-B397 growth and GABA synthesis is shown in Figures 3 and 4. It can be seen that pH has a significant effect on the growth of bacterial cells and amount of GABA produced. During fermentation, there was a notable increase in microbial density when the medium pH control value was increased from 3 to 6. At pH 4, the proliferation process was prolonged, and the cell viability was maintained, resulting in the microbial density potentially reaching its maximum after 60 hr. Meanwhile, the bacterial density was higher at pH 5 and 6 compared to pH 4 and reached their maximum just after 24 hr. The amount of GABA produced at pH 5 was much higher than at other pH levels, with a peak at 24 hr. During the first 24 hr, although the GABA production under at a controlled pH of 5 was noticeably higher than under uncontrolled pH conditions, 7.65 and 6.41 g/L, respectively, the trend in GABA content change was similar under both conditions. Under uncontrolled pH setting, the pH value and GABA content in medium decreased sharply after 24 hr (Figure 5). In contrast, GABA content remained stable in the pH-controlled environments. This suggests that pH control was essential for GABA biosynthesis. At pH 3, GABA was not significantly produced during the fermentation. The change in pH value could cause the denaturation of bacterial cell structures, specifically affecting the integrity of cell membrane. The pH value was responsible for regulating the different functions of proteins in the cell membrane, controlling the transport of materials into and out of the cell. Both highly alkaline or acidic environments could damage the cell membrane. This is clearly shown in the microbial growth curve, where the lowest cell density was observed at pH 3.0 (acidic environment) and the highest at pH 6.0 (mildly acidic environment). However, GAD enzyme catalysed biosynthesis of GABA in LAB, being effective at pH 4 and 5 (Lee et al., 2022). This is closely associated with the functionality of the GAD system, a vital acid resistance mechanism in numerous bacterial species. High acid concentration generates a stressful environment, which can adversely affect bacterial growth, leading to growth inhibition and increased cell death despite the availability of nutrients. To counteract this, bacteria enhance amino acid synthesis, including aspartic acid, arginine, and glutamic acid, to stabilise intracellular pH (Diez-Gutiérrez et al., 2022). The gadA and gadB genes of Lactobacillus bacteria encode GADs, consuming protons to maintain pH homeostasis through promoting GABA synthesis to protect against the external acidic environment (Lin et al., 2017; Lyu et al., 2018). Although cells exhibited optimal growth at pH 6, the conditions that favoured GABA synthesis were limited under these circumstances, which could slightly reduce the GABA production. Besides that, rapid growth and a prolonged stationary phase at pH 6.0 might quickly deplete the substrate in the culture, causing GABA to be used as a nutrient source for bacterial survival during the subsequent period. This resulted in GABA production at pH 6.0 was lower than at pH 5.0.
Effect of pH control on growth of L. brevis VTCC-B397 (■: pH = 3.0, □: pH = 4.0, ●: pH = 5.0, |$\circ $|: pH = 6.0).
Effect of pH control on GABA production of L. brevis VTCC-B397 (■: pH = 3.0, □: pH = 4.0, ●: pH = 5.0, |$\circ $|: pH = 6.0). GABA = gamma aminobutyric acid.
Variation in pH and GABA concentration in fermentation broth without pH control (column: pH, line: GABA concentration). GABA = gamma aminobutyric acid.
The above results indicate that the maintaining a stable pH of 5.0 during GABA fermentation process created favourable conditions for the GABA synthesis of LAB, while also hindering the degradation of GABA.
Effect of temperature on GABA production from defatted rice bran extract by L. brevis VTCC-B397
The effect of temperature on GABA synthesis and the growth of L. brevis VTCC-397 is presented in Figures 6 and 7. In the range of 30–37 °C, bacterial growth was most favourable, with the highest density observed at 30 °C after 24 hr. At 25 and 43 °C, the growth and development of L. brevis VTCC-397 were the lowest among the tested temperature conditions, showing no significant differences between these two extremes. This indicates that both suboptimal low and high temperatures negatively impacted bacterial growth and subsequently GABA production during the fermentation process. These findings show that temperature had an important effect on promoting the growth and development of microorganisms during the fermentation process. The GABA content increased as temperature increased from 25 to 34 °C, after that gradually decreased at higher temperatures. At 43 °C, GABA content remained almost unchanged throughout the fermentation process. Meanwhile, at other temperatures after 24 hr, GABA content decreased slightly. At temperature of 34 °C, the GABA content produced was significantly higher than at other conditions throughout the process, reaching a maximum of 7.73 g/L at 24 hr of fermentation.
Effect of temperature on GABA production of L. brevis VTCC-B397 (■: 25 °C, □: 30 °C, ●: 34 °C, |$\circ $|: 37 °C, ▲: 43 °C). GABA = gamma aminobutyric acid.
Effect of temperature on growth of L. brevis VTCC-B397 (■: 25 °C, □: 30 °C, ●: 34 °C, |$\circ $|: 37 °C, ▲: 43 °C).
The results obtained are similar to some other studies such as Zhang et al. (2022) and Wang et al. (2024b). L. brevis bacteria growth increased with increasing temperature and peaked in the range of 30–35 °C, then decreased as the temperature increased further. The extremes in temperature, whether too high (43 °C) or too low (25 °C), adversely affected the growth of L. brevis by inducing enzyme inactivation and accelerated cellular ageing. Moreover, at 43 °C, the stability of the GAD enzyme might decrease, leading to reduced activity and, consequently, a lower efficiency of GABA biosynthesis (Lin et al., 2017). High cell density was obviously necessary for efficient GABA production. However, the results obtained show that bacterial growth was strongest at the temperature of 30 °C, whereas the highest GABA content, 7.73 g/L, was obtained at 34 °C. These results indicate that the conversion of glutamate to GABA to achieve high efficiency required not only high cell density but also suitable temperature. GAD gene expression has been found to be influenced by factors such as pH and temperature. Hence, adjusting these conditions impacted GABA production by not only enhancing microbial cell proliferation but also improving the efficiency of glutamate to GABA conversion (Lin et al., 2017).
In general, although microbial cell growth reached its peak at 30 °C, temperature of 34 °C was the most favourable condition for GABA synthesis in the defatted rice bran extract medium. Under these conditions, the GABA content at its highest point was twice as high as that observed at 30 °C, primarily due to enhanced GAD enzyme activity at the optimal temperature.
Optimization of fermentation condition for GABA production from rice bran extract by L. brevis VTCC-B397
Based on the aforementioned results, the factors, i.e., MSG concentration, pH, and temperature, and their respective levels for response surface optimization were selected and presented in Table 1. The centre point in the experimental design was set at a medium with MSG concentration of 0.5 M, pH of 5 and temperature of 34 °C.
Independent variables, their coded, and actual values used in RSM optimization.
| Independent variables | Symbol | Code levels | ||
|---|---|---|---|---|
| −1 | 0 | 1 | ||
| MSG concentration (M) | X1 | 0.25 | 0.5 | 0.75 |
| pH | X2 | 4 | 5 | 6 |
| Temperature (°C) | X3 | 30 | 34 | 38 |
| Independent variables | Symbol | Code levels | ||
|---|---|---|---|---|
| −1 | 0 | 1 | ||
| MSG concentration (M) | X1 | 0.25 | 0.5 | 0.75 |
| pH | X2 | 4 | 5 | 6 |
| Temperature (°C) | X3 | 30 | 34 | 38 |
Note. RSM = response surface methodology.
Independent variables, their coded, and actual values used in RSM optimization.
| Independent variables | Symbol | Code levels | ||
|---|---|---|---|---|
| −1 | 0 | 1 | ||
| MSG concentration (M) | X1 | 0.25 | 0.5 | 0.75 |
| pH | X2 | 4 | 5 | 6 |
| Temperature (°C) | X3 | 30 | 34 | 38 |
| Independent variables | Symbol | Code levels | ||
|---|---|---|---|---|
| −1 | 0 | 1 | ||
| MSG concentration (M) | X1 | 0.25 | 0.5 | 0.75 |
| pH | X2 | 4 | 5 | 6 |
| Temperature (°C) | X3 | 30 | 34 | 38 |
Note. RSM = response surface methodology.
Using the experimental data, a polynomial model for GABA yield (Y) was developed by regressing only the significant terms. This model explained the relationship and interactions between the three independent variables affecting GABA yield produced by L. brevis VTCC-397, as represented below:
The results of ANOVA are presented in Table 2. The p value of model indicated a good fit between the experimental data and the model (p < .05). The value of R2 and adjusted R2 were 0.9797 and 0.9614, respectively, demonstrating the accuracy of model in representing the actual relationship between the response and the significant variables. The analysis of variance (ANOVA) for the independent variables against GABA level indicated that the MSG concentration and pH were significant factors, whereas temperature was less influential. The results also revealed a relatively weak interaction between the factors, with no significant effect on the GABA yield. These findings confirmed the accuracy and predictive capability of the polynomial model for determining the process parameters of pH control, MSG concentration and temperature on GABA content in the fermented rice bran culture. Figure 8 presents 3D surface counterplots for two variables while maintaining the other factor at its central level (0). The interaction effect of the factors displayed a typical bell-shaped curve, depicting that initially, as the individual factors increased, the GABA yield also increased, followed by a decline. These suggest that the moderate level of the factors resulted in optimal GABA production. Figure 8A depicts the interaction between pH and MSG concentration on GABA yield at 34 °C, showing the GABA production increased to higher values at MSG concentrations above 0.35 M and pH levels above 4.5. Lower pH reduced the effect of MSG on GABA synthesis. Figure 8B explores temperature and MSG effects at pH 5.0, showing higher GABA yields at temperatures between 32 and 36 °C and MSG concentrations above 0.35 M. Figure 8C displaying the effect on temperature and pH at MSG concentration of 0.5 M on the GABA content revealed that the extreme pH and temperature conditions significantly lowered GABA production. At extreme temperatures, GABA content exhibited negligible change, regardless of changes in pH or MSG concentration, as supported by ANOVA analysis, which revealed that temperature was not a statistically significant factor.
Analysis of variance for the regression.
| Coefficient | Regression coefficient | p |
|---|---|---|
| Model | <.0001 | |
| b0 | 7.63 | <.05 |
| b1 | 0.5868 | <.05 |
| b2 | 0.5033 | <.05 |
| b3 | 0.1408 | .421 |
| b11 | −1.21 | <.05 |
| b22 | −2.23 | <.05 |
| b33 | −2.53 | <.05 |
| b12 | 0.1163 | .607 |
| b13 | −0.1063 | .638 |
| b23 | −0.0638 | .776 |
| Lack of fit | <.05 | |
| R2 | 0.9797 | |
| R2adj | 0.9614 | |
| Q2 | 0.829 |
| Coefficient | Regression coefficient | p |
|---|---|---|
| Model | <.0001 | |
| b0 | 7.63 | <.05 |
| b1 | 0.5868 | <.05 |
| b2 | 0.5033 | <.05 |
| b3 | 0.1408 | .421 |
| b11 | −1.21 | <.05 |
| b22 | −2.23 | <.05 |
| b33 | −2.53 | <.05 |
| b12 | 0.1163 | .607 |
| b13 | −0.1063 | .638 |
| b23 | −0.0638 | .776 |
| Lack of fit | <.05 | |
| R2 | 0.9797 | |
| R2adj | 0.9614 | |
| Q2 | 0.829 |
Analysis of variance for the regression.
| Coefficient | Regression coefficient | p |
|---|---|---|
| Model | <.0001 | |
| b0 | 7.63 | <.05 |
| b1 | 0.5868 | <.05 |
| b2 | 0.5033 | <.05 |
| b3 | 0.1408 | .421 |
| b11 | −1.21 | <.05 |
| b22 | −2.23 | <.05 |
| b33 | −2.53 | <.05 |
| b12 | 0.1163 | .607 |
| b13 | −0.1063 | .638 |
| b23 | −0.0638 | .776 |
| Lack of fit | <.05 | |
| R2 | 0.9797 | |
| R2adj | 0.9614 | |
| Q2 | 0.829 |
| Coefficient | Regression coefficient | p |
|---|---|---|
| Model | <.0001 | |
| b0 | 7.63 | <.05 |
| b1 | 0.5868 | <.05 |
| b2 | 0.5033 | <.05 |
| b3 | 0.1408 | .421 |
| b11 | −1.21 | <.05 |
| b22 | −2.23 | <.05 |
| b33 | −2.53 | <.05 |
| b12 | 0.1163 | .607 |
| b13 | −0.1063 | .638 |
| b23 | −0.0638 | .776 |
| Lack of fit | <.05 | |
| R2 | 0.9797 | |
| R2adj | 0.9614 | |
| Q2 | 0.829 |
Response surface of GABA as a function of independent variables: (A) pH and MSG concentration, (B) temperature and MSG concentration, and (C) temperature and pH. GABA = gamma aminobutyric acid; MSG = monosodium glutamate.
Based on the regression equation, the optimal condition for GABA production from rice bran fermentation by L. brevis VTCC-397 were determined to be a culture temperature of 34 °C, MSG concentration of 0.56 M, and a pH control of 5.12. Under these conditions, the predicted GABA yield was 7.73 g/L after 24 hr. The experiments were conducted three times under optimal conditions, producing an average yield of 7.69 g/L of GABA, representing a 23-fold increase compared to yields under unoptimised conditions. This close agreement between the experimental and predicted values confirmed the reliability of the model. The GABA content in defatted rice bran extract inoculated with L. brevis VTCC-397 and fermented under the optimal conditions was 51.3 times higher than that of unfermented defatted rice bran extract, which contained 0.15 g/L of GABA.
Conclusion
Fermenting defatted rice bran extract with LAB is one of the most effective methods for producing GABA. Optimising conditions to achieve high GABA yield using the L. brevis VTCC-397 strain is particularly important. Key factors such as pH control, temperature, and MSG concentration had a significant impact on the GABA synthesis pathway of L. brevis VTCC-B397, resulting in a substantial increase in GABA production in defatted rice bran extract compared to unoptimised conditions. The highest GABA yield of 7.69 g/L was obtained with L. brevis VTCC-B397 in defatted rice bran culture under RSM optimised conditions, with an MSG concentration of 0.56 M, a temperature of 34 °C, and a pH of 5.12. The results highlight defatted rice bran extract as an excellent medium for industrial applications and provide a solid foundation for scaling up GABA production. However, ensuring precise control of production conditions while maintaining economic viability at an industrial scale remains a significant challenge. Comprehensive techno-economic analyses are therefore essential to evaluate the feasibility of applying these findings to large-scale production. Future research should also explore the integration of these optimised factors into diverse production systems, such as co-fermentation strategies or the use of alternative substrates, to further enhance production efficiency and broaden applications in the food and pharmaceutical industries.
Data availability
Data are available on request from the authors.
Author contributions
Conceptualisation: Quoc Dat Lai; Methodology: Quoc Dat Lai and Ngoc Thuc Trinh Doan; Data curation: Ngoc Thuc Trinh Doan; Visualisation: Nguyen Thi Hien; Draft manuscript preparation: Ngoc Thuc Trinh Doan; Writing—review & editing: Quoc Dat Lai and Nguyen Thi Hien. All authors reviewed the manuscript.
Quoc Dat Lai (Conceptualisation [equal], Methodology [equal], Writing—review & editing [equal]), Ngoc Thuc Trinh Doan (Data curation [equal], Methodology [equal], Writing—original draft [equal]), and Hien Nguyen Thi (Visualisation [equal], Writing—review & editing [equal])
Funding
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number DN2022-20-01. We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.
Conflicts of interest
None declared.
