NRMJ_9_5_398-416

Introduction

The optimisation of microbial biomass via tailored media formulations is a critical area of research in microbiology and agricultural biotechnology. Microorganisms, particularly effective microorganisms (EMs), play an important role in promoting sustainable agricultural practices by enhancing soil fertility, plant growth, and crop yields (Naik et al., 2019). EMs consist of beneficial microorganisms, including bacteria, yeasts, and photosynthetic microorganisms, which work synergistically to solubilize nutrients, suppress plant pathogens, and increase microbial activity in the rhizosphere (Qadir et al., 2024). Recent studies have explored the potential of EM in various applications. Chaudhary et al. (2025) found significant increases in soil microbial biomass carbon, phosphorus, and nitrogen when using facultative endophytic fungi, mainly Serendipita indica and Azotobacter chroococcum; a nitrogen-fixing bacterium. Co-inoculation of these microorganisms showed a synergistic effect, enhancing soil fertility more than individual treatment or control. Similarly, Ma et al. (2025) examined the effects of Bacillus velezensis SQR9 or Trichoderma harzianum NJAU4742- amended biofertilizers on pear seedling growth, revealing significant improvements in biomass and soil nutrient levels. These studies collectively demonstrate the versatility of EM in soil improvement and agricultural applications, while emphasizing the need for further research to fully understand and optimize their potential across various fields.

Despite the proven benefits of EMs, their large-scale application as biofertilizers faces challenges due to the complex and strain-specific nutritional requirements necessary for their cultivation (Krupodorova et al., 2024). Unlike the chemical fertilisers, which have uniform nutrient profiles, EMs require tailored growth media to optimize their growth and activity (Ali et al., 2023). For example, optimal sources of carbon, nitrogen, micronutrients, pH levels, and growth conditions may vary greatly across the microbial taxa (Ghazanfari et al., 2023). The traditional trial-and-error approaches to media preparation are often inefficient, highlighting the need for systematic and scalable methods.

To address this challenge, the current study employed the Automated Media Optimization System (AMOS); an advanced high-throughput platform, to identify the optimal nutrient formulation for five EMs, mainly Bacillus subtilis, Candida utilis, Pediococcus acidilactici, Rhodopseudomonas palustris, and Brevibacillus borstelensis. These strains were selected based on their well-documented efficacy as biofertilizers (Christmann et al., 2023; Sheng et al., 2023; Yánez-Mendizábal et al., 2023; Ross, 2024), including their abilities to enhance nitrogen fixation, photosynthesis, and biocontrol. For example, B. subtilis, has been extensively studied for its biocontrol properties (Dimkić et al., 2022), while R. palustris, contributes substantially to nitrogen fixation and photosynthetic activity, supporting the development of a diverse and resilient microbial consortium that enhance soil fertility and crop productivity, critical for sustainable agricultural systems (Chowdhury et al., 2022).

Automated Media Optimization System enables a precise and high-throughput evaluation of multiple variables simultaneously, allowing for a more efficient exploration of nutrient interactions compared to the traditional methods (Chowdhury et al., 2022; Mwampashi et al., 2024). A factorial design and RSMs can be utilised to optimize carbon and nitrogen sources, macro- and micronutrient ratios, and pH levels. Such systematic approaches provide insights into the synergistic nutrient effects while minimising the inhibitory interactions, ensuring scalability and reproducibility in agricultural applications.

The objective of this study was to optimize the biomass production of selected EM by systematically determining key nutrients, evaluating nutrient interactions, and validating reproducible formulations using an AMOS. By developing optimized media formulations tailored to EMs, this study advances microbial biotechnology and addresses critical barriers to sustainable agriculture. These findings not only enhance the scalability of EMs production but also align with global efforts regarding the environmental impact of agriculture. This work represents a step forward in bridging theoretical researches with practical ones and commercial applications, demonstrating how EMs can be integrated into ecofriendly agricultural practices to promote food security and sustainability.

Materials and Methods

The current study was carried out in three distinct phases using an AMOS to optimize the biomass of EMs. A detailed flowchart (Figure 1) outlines the used methodologies.

Selection of EM and preparation of stock cultures

Four bacterial strains, Bacillus subtilis, Pediococcus acidilactici, Rhodopseudomonas palustris, and Brevibacillus borstelensis, in addition to a single yeast strain (Candida utilis) were selected for their known biofertilizer properties in the formulation of EM. A previous study isolated these bacterial cultures from salted seafood and soil samples collected from an oil palm plantation in Tawau, Sabah, Malaysia (Radzun et al., 2015). P. acidilactici was grown in deMan-Rogosa-Sharpe (MRS) broth and agar, whereas B. subtilis, B. borstelensis, and R. palustris were grown on nutrient broth (NB) and nutrient agar (NA). C. utilis was cultured in potato dextrose broth (PDB) and potato dextrose agar (PDA). The inocula contained 5×103 cells/mL and were kept at optimal pH levels, mainly pH 6.4 for B. subtilis, B. borstelensis, and C. utilis; pH 6.2 for P. acidilactici, and pH 6.8 for R. palustris. To prevent cross-contamination, the cultures were kept separated and incubated at 30°C with agitation at 80 rpm. R. palustris was exposed to continuous white fluorescent light at a brightness of about 2220 lux.

Screen 1: Nutrient interaction analysis and optimization using factorial box-behnken design

An analysis of the compiled literatures was carried out to evaluate and determine the optimal nutrient and growth conditions (i.e., nitrogen and carbon sources and their concentrations, pH, agitation condition, and basal media composition) for an EM basal medium formulation. The average nutrient concentrations and conditions were calculated based on information of several previous studies for B. subtilis (Gao et al., 2012; Basamma et al,, 2017; Ionuț et al., 2017; Somda et al., 2018), P. acidilactici (Nguyen et al., 2018), R. palustris (Zhao et al., 2010), and B. borstelensis (Sharma et al., 2014; Arya et al., 2016). The first full factorial design used in screen 1 was to determine the optimal nitrogen and carbon sources and their concentrations for EM at five different concentration levels, as shown in Table 1. Other micronutrient and macronutrient concentrations were kept constant during this experimental design. Each concentration level was tested in triplicate. The EM Screen 1 optimization process included both positive and negative control

Table 1: Full factorial design of different nitrogen and carbon sources and their concentrations for EMs production.

Effective microorganism (EM)	Nutrient sources	Test nutrients	Concentrations range (mM)
Pediococcus acidilactici	Nitrogen	Beef extract	0, 2.5, 5.0, 7.5, 10.0 (g/L)
Pediococcus acidilactici		Manganese	0, 0.125, 0.25, 0.375, 0.5 (g/L)
Bacillus subtilis	Nitrogen	Yeast extract	0, 2, 4, 6, 8 (g/L)
	Nitrogen	Peptone	0, 3.5, 7.0, 10.5, 15.0 (g/L)
	Carbon	Glucose	0, 10, 20, 30, 40 (g/L)
	Carbon	Sodium bicarbonate	0, 10, 20, 30, 40 (g/L)
Brevibacillus borstelensis	Nitrogen	(NH4)2SO4	0, 3, 6, 9, 12 (g/L)
	Nitrogen	Yeast extract	0, 2, 4, 6, 8 (g/L)
	Carbon	Glucose	0, 10, 20, 30, 40 (g/L)
	Carbon	Sodium bicarbonate	0, 10, 20, 30, 40 (g/L)
Rhodopseudomonas palustris (PSB)	Nitrogen	Yeast extract	0, 0.9, 1.8, 3.6, 7.2 (g/L)
	Carbon	Glucose	0, 10, 20, 30, 40 (g/L)
	Carbon	Sodium bicarbonate	0, 10, 20, 30, 40 (g/L)
Candida utilis (Yeast)	Nitrogen	(NH4)2SO4	0, 3, 6, 9, 12 (g/L)
	Nitrogen	Peptone	0, 3.5, 7.0, 10.5, 15.0 (g/L)
	Carbon	Glucose	0, 14, 28, 42, 56 (g/L)
	Carbon	Sodium bicarbonate	0, 14, 28, 42, 56 (g/L)

Where; PSB: Photosynthetic bacteria.

Table 2: Culture conditions and plate well nutrient compositions for each EM production.

Effective microbes (EM)	Optical density reading (nm)	pH	Agitation (rpm)	Medium composition in the plate well (µL)
Bacillus subtilis	600	6.4	80	Basal Media: 9; CaCl2.2H2O: 3; PBS: 5; EM culture: 15
Candida utilis	600	6.4	80	Basal Media: 9; CaCl2.2H2O: 3; PBS: 5; EM culture: 15
Pediococcus acidilactici	600	6.2	80	Basal Media: 9; CaCl2.2H2O: 3; PBS: 5; EM culture: 15
Rhodopseudomonas palustri	680	6.8	80	Basal Media: 9; CaCl2.2H2O: 3; PBS: 5; EM culture: 15
Brevibacillus borstelensis	600	6.4	80	Basal Media: 9; CaCl2.2H2O: 3; PBS: 5; EM culture: 15

Where; CaCl2.2H2O: Calcium chloride dihydrate; PBS: Phosphate buffered saline

media, specifically MRS media for P. acidilactici, NB for B. subtilis, R. palustris, and B. borstelensis, and PDB for Candida utilis, alongside a basal medium serving as EM’s base formulation.

Optical density (OD) was measured at 600 nm for non-photosynthetic strains (B. subtilis, P. acidilactici, B. borstelensis, and C. utilis) and at 680 nm for the photosynthetic strain (R. palustris) to account for pigment absorbance. For preparation of the screening experiment with the EM, 50 mL of each EM harvested during the logarithmic growth phase (indicated as OD600/680 ≈ 0.8–1.0, corresponding to mid-log phase) were collected by centrifugation at 2,800 × g for 10 min at 4°C to obtain a cell pellet (Arya et al., 2016; Othman et al., 2017). The samples were then rinsed several times with phosphate-buffered saline (PBS) to remove any remaining media. The EM cells were inoculated into sterile 96-well microwell plates, according to the experimental design, and resuspended in an appropriate volume of PBS at the specific pH required for each microorganism. The starting inoculum, indicated as OD600/680, was set to 0.1 through a serial dilution using a microwell plate reader. All EM strains were cultured in a 96-well plate, with each well containing 150 μL of the culture on an orbital shaker at a specified rpm under continuous dark conditions, except for the R. palustris that was exposed to white light of approximately 2220 lux.

Each well contained a variety of tested elements (i.e., nitrogen and carbon sources and their concentrations, Table 1), basal media, PBS, CaCl2, and EM cells, either with or without vitamins as described in Table 2. The volume of sterile dist. water was adjusted according to the amount of tested elements. The miniaturized robotic screening system (AMOS) enabled cultivation up to 72 h to minimize the evaporation effects under the tested culture conditions (Table 2). Individual optimization screenings for each effective microorganism were performed using separate 96-microwell plates; with maintaining a sterile environment for all. The robotic system was regularly sanitized with 70% aseptic ethanol, especially when the culture plates were briefly removed for photography. Growth rate data was automatically recorded at hourly intervals.

Screen 2: Nutrient interaction analysis and optimization using factorial box-behnken design

The optimization for screen 2 utilized the best concentration of the tested nutrients obtained from screen 1, specifically the high endpoint of OD600/680 (Saharuddin et al., 2018; Abdulhameed et al., 2022). This optimization was based on a statistically incomplete factorial Box-Behnken design, which involved a 3×10 setup with three concentration levels for 10 different nutrients, including calcium, zinc, magnesium, iron (ferrous), cobalt, copper, manganese, molybdate, sodium, and boron. In screen 2, a total of 180 formulations were tested for each effective microorganism (B. subtilis, C. utilis, P. acidilactici, R. palustris, and B. borstelensis) which included 20 centre points or controls. The different EMs required different combinations of these 10 nutrients, each were tested at three concentration levels.

The concentration range was determined as previously described by Radzun et al. (2015) to establish initial points and endpoints for optimal nutrient concentrations. The average concentration identified in the literatures served as the midpoint, while a two-fold increase from this midpoint defined the high concentration, and a two-fold decrease defined the low concentration. Additionally, the same amounts of chelating agents, mainly 0.5373 mM Na-EDTA (pH 8.0) and 100 mM PBS (with pH levels of 6.2 for P. acidilactici, 6.4 for B. subtilis, C. utilis, B. borstelensis, and 6.8 for R. palustris) were included as buffers. Other essential elements, such as yeast extract and sodium selenite were also added to the formulated media as additives. The effects of micro and macro-elements were measured on the growth performance of the EMs at three concentration levels, coded as -1 (low), 0 (medium), and +1 (high).

Phase 2: Determination of key nutrients and nutrient interactions via AMOS and RSM analysis

The growth rate of an EM was systematically analyzed through optical density (OD) measurements, as described previously by Radzun et al. (2015). Absorbance readings were obtained using an Infinite M1000 microplate reader, (Tecan, Männedorf, Switzerland) with OD measurements taken at 600 nm for P. acidilactici, B. subtilis, B. borstelensis, and C. utilis, and 680 nm for R. palustris. These measurements served as reliable proxies for assessing the microbial growth. Throughout a 48-h nutrient optimization process, OD readings of the EM were recorded at regular intervals beginning at 0 h. Graphical representations were created to elucidate the bacterial growth patterns and rates. The growth rate was quantitatively determined using the following equation reported by Graspeuntner et al. (2023):

μ (/h) = 2.303 lg (ODt2 – ODt1)/t2 – t1

Where; ODt1 = Optical density at time 1 and ODt2 = Optical density at time 2. t1 = time 1, t2 = time 2. μ = Growth rate unit is per hour (/h)

Following nutrient optimization via the AMOS (Saharuddin et al., 2018), the highest recorded growth rates data of EM’s were subjected to analysis using RSM with Minitab 18 software (Minitab Inc., USA). The main effects and interaction effects were comprehensively evaluated to identify the specific influences of nutrients and their statistical interactions; thereby elucidating the significance of nutrients in enhancing microbial growth rates. The main effect analysis determined the individual contributions of the specific nutrients on the growth rate, while the interaction effect analysis revealed the combined significance of the various nutrients and their synergistic impacts.

Phase 3: Reproducibility testing in miniaturized and large-scale systems

The obtained three nutrient media formulations were developed, corresponding to the lowest, medium, and highest growth rates for each EM from phase 2 (Saharuddin et al., 2018). The initial biomass density of EM in each cultivation flask was set at approximately 0.1 (calculated as OD600/680 as described earlier). The cultivation conditions were carefully maintained to align with the optimal parameters for agitation, pH, temperature, and light availability. Cultures of R. palustris were exposed to white light, while the other EM cultures were incubated in the absence of light. The OD of the EM cultures was measured using an Infinite M1000 microplate reader, (Tecan, Männedorf, Switzerland), which allowed for calculation of the corresponding growth rates.

The experimental conditions included media formulations that resulted in the observed lowest, medium, and highest growth rates as determined in phase 2 (Saharuddin et al., 2018). The media for each EM were prepared using the formulas for higher growth media (+1), middle growth media (0), and lower growth media (-1). Each used medium, including NaEDTA and PBS was freshly prepared as needed, monitored, and adjusted to its optimal pH. The culture samples were systematically collected in triplicate after incubation for 2 h. Following cultivation, the microbial biomass was harvested through centrifugation, and drying was performed using a drying oven set at 65°C for 3 to 5 d. The weight of the pellet was measured daily in triplicate to monitor the reduction in moisture. The dry cell weight (DCW) obtained from this process represented the biomass collected for this study.

Statistical analysis

The RSM was employed to analyze the nutrient interactions. Main and interaction effects were visualised through 3D response surface plots. Lack-of-fit tests ensured model robustness, and any models with R² < 50% were flagged for further refinement. Statistical analysis was conducted using Minitab 18. GraphPad Prism 8 (GraphPad Prism Inc., USA) was employed to generate a sigmoidal curve fit that accurately captured the progression of bacterial growth from the lag phase to the stationary phase under the optimal growth conditions. In instances where the main and interaction effects demonstrated statistical significance (p ≤ 0.05), the implicated nutrient factors were systematically refined to optimize the growth performance. Based on the data obtained, three distinct formulations for each EM were developed, emphasising the key nutrients and their interactions that significantly facilitated the microbial growth. The formulation media corresponding to the lowest, medium, and highest growth rates for each microorganism were systematically utilized in the subsequent phase of this investigation.

Results and Discussion

Identification of the optimal nutrient sources

Screening using AMOS was conducted for 30 h to investigate the effects of nitrogen and carbon sources, each at five different concentration levels for Screen 1. After 48 h of incubation, the growth rates measurements demonstrated that the tested nutrients at the specific concentrations in Screen 1 positively influenced the growth performance of each experimental model, as illustrated in the 3D response surface plots (Figure 2). Based on their performance, this study selected three different types of nitrogen sources, mainly yeast extract, beef extract, and ammonium sulphate (NH4)2SO4.

The RSM analysis plot for B. subtilis in Figure 2a indicated that the growth rate increased with both the glucose and yeast extract concentrations, reaching a peak starting from moderate levels of yeast extract (4 g/L) and higher glucose concentrations (30–40 mM). The formulation media for B. subtilis exhibited the highest growth rate of 0.07248/h when supplemented with 6.0 g/L of yeast extract and 40.0 mM of glucose. Abd Aziz et al. (2020) demonstrated that a 1.0% glucose concentration supported optimal B. subtilis growth, yielding a specific growth rate of 0.7995/ h with a corresponding doubling time of 52.02 min. Consistently, Stamenković et al. (2020) confirmed glucose as an effective and readily available substrate, reporting enhanced cultivation within the 5–15 g/L range and maximum biomass production at 10 g/L, with a specific growth rate of 0.273/h. In terms of nitrogen supplementation, Sahoo et al. (2020) identified yeast extract as the most effective source, with supplementation at 10 g/L increasing caseinolytic activity to 833.43 U/mL. Extending this observation, Olszewska-Widdrat et al. (2024) demonstrated that 10% yeast extract supported the highest specific rates of glucose consumption and microbial growth in B. coagulans, achieving a glucose-to-lactic acid yield of 0.99 g/g. This suggested that higher concentrations of glucose and yeast extract were optimal for maximising B. subtilis growth due to their roles in supplying energy and essential nitrogen-based nutrients.

For the C. utilis, the highest growth rate was reported at 0.0795±0.003/h in media supplemented with 12 mM ammonium sulphate and 42 mM glucose (Figure 2b). The surface chart highlighted a synergistic effect between ammonium sulphate as a nitrogen source and glucose as a carbon source, indicating that their concentrations effectively supported yeast metabolism and growth. Statistical analysis (ANOVA) confirmed that the growth rate under these conditions was significantly higher (p < 0.05) compared to lower nutrient concentrations, emphasising the critical role of balanced carbon and nitrogen supplementation.

For P. acidilactici, the highest growth rate was reported at 0.18433±0.007/h in media containing 10 g/L beef extract and 0.125 mM manganese (Figure 2c). The plot revealed that a low concentration of manganese combined with a high concentration of beef extract considerably enhanced the microorganism’s growth.

This may be attributed to the manganese’s role as a cofactor in enzymatic reactions, particularly those involved in oxidative stress management, while beef extract provided peptides and amino acids necessary for protein synthesis. Beef extract was also regarded as the most effective complex nitrogen source compared to the other alternatives such as peptone and yeast extract. This is attributed to its relatively higher total nitrogen content compared to the others. Moreover, an adequate supply of nitrogen from meat extract not only promoted the growth of P. acidilactici but also supported pediocin production (Saharuddin et al., 2018).

Rhodopseudomonas palustris showed the highest growth rate of 0.14557±0.006/h with the media supplementation of 7.2 mM yeast extract and 40 mM sodium bicarbonate (Figure 2d). The RSM demonstrated that elevated sodium bicarbonate levels promoted autotrophic carbon assimilation, while yeast extract supplied the essential nitrogen needed for biosynthesis. Papapanagiotou et al. (2024) revealed that sodium bicarbonate concentration substantially affected biomass production in Chlorella sorokiniana, with optimal conditions including 750 mg/ L NaHCO3. Similarly, Kusi et al. (2024) reported that bicarbonate concentration influenced carbon utilization rates, with several microalgal species achieving >85% carbon removal efficiency at 6 g/ L NaHCO3, though optimal concentrations varied by species. The plateauing effect observed at bicarbonate concentrations beyond 40 mM suggested saturation of the carbon assimilation pathways, indicating an optimal threshold.

As for B. borstelensis, the highest growth rate was reported at 0.102/h in media containing 4.0 g/L yeast extract and 10 mM sodium bicarbonate (Figure 2e). The flat growth response across the varying tested yeast extract concentrations suggested that B. borstelensis was relatively tolerant to low nutrient conditions but performed optimally at balanced levels of carbon and nitrogen. Comparatively, B. borstelensis exhibited a lower growth rate than P. acidilactici, indicating a slower metabolic response to the same range of nutrient concentrations.

Based on the analytical results from screen 1, three distinct nitrogen sources, including yeast extract, beef extract, and ammonium sulphate demonstrated preferential utilization by B. subtilis, C. utilis, P. acidilactici, R. palustris, and B. borstelensis. This preference may be attributed to the highly nutritive characteristics of the medium derived from yeast extract and beef extract, as they provided a richer array of amino acids, water-soluble vitamins, and carbohydrates compared to the conventional nitrogen chemical sources (Sun et al., 2024). In addition, ammonium sulphate, although being a simpler nitrogen source, proved effective for supporting the yeast growth, indicating its potential cost-effectiveness for industrial fermentation processes. Similarly, (Joshi, 2014) demonstrated that ammonium sulphate was the best nitrogen supplement among the tested sources (i.e., ammonium phosphate, sodium nitrate, urea, and glycine), enhancing ethanol production and cell viability in Saccharomyces cerevisiae, with maximum ethanol percentage of 8.3% achieved using 2% ammonium sulphate concentration. However, Prins and Billerbeck (2021) employed ammonium sulfate as a nitrogen source in synthetic complete drop-out medium, although reported difficulties in maintaining a stable pH when relying solely on this component. The stability and activity of the secreted proteins during yeast fermentation are often dependent on the culture pH. Since yeast metabolism will acidify the medium, thus buffering is required to maintain the desired extracellular pH throughout biomass production. The authors proposed a buffered medium supplemented with urea, which provided a stable pH control across a broad range without impairing yeast growth.

In terms of the different carbon sources, both glucose and sodium bicarbonate were relatively favoured by the EM’s being essential for their growth. The obtained findings indicated that growth performance of these microorganisms was significantly enhanced (p < 0.05) when both nutrients were administered at elevated concentrations, as demonstrated by the maximum specific growth rates achieved under the optimal conditions illustrated in Figure 2. This finding supported the conclusion that high carbon availability correlated with increased microbial biomass. For instance, a previous study conducted on microalgae such as Scenedesmus sp. in a medium supplemented with sodium bicarbonate (up to 12 mM) considerably boosted the biomass and photosynthetic pigment production (Singh et al., 2022). Similarly, glucose is a well-documented carbon source that enhances microbial growth by serving as a primary energy source, facilitating robust metabolic activity. These findings confirmed the synergistic role of such carbon sources in optimizing the microbial growth. A recent study revealed that in P. aeruginosa, glucose provided the highest microbial growth rates (0.2-0.5 logOD/ h) among the tested carbohydrates such as glycerol, soy hull hydrolysate (SHH), and mimicking soy hull hydrolysate (MSH), supporting robust rhamnolipid production at 2.78 g/L (Sharma et al., 2025). The observed synergistic effects of combining optimal carbon and nitrogen sources highlight the importance of balanced nutrients formulation for maximizing microbial productivity, particularly in biotechnological applications.

Nutrient interaction analysis and optimization of EM using factorial box-behnken design

Table 3 displays the results of optimizing the specific growth rates and nutrient variations for each EM using a factorial Box-Behnken design in screen 2. The analysis showed that optimal nutrient levels varied greatly among the microbial species, emphasising the importance of tailored nutrient profiles to meet the specific microbial growth requirements. Detailed analysis revealed that certain nutrient conditions exhibited significantly higher mean values (0.553/h, 0.470/h, 0.083/h, 0.351/h, and 0.251/h for B. subtilis, C. utilis, P. acidilactici, R. palustris, and B. borstelensis, respectively) compared to the centre point (0.473/h, 0.425/h, 0.079/h, 0.334/h, and 0.243/h for B. subtilis, C. utilis, P. acidilactici, R. palustris, and B. borstelensis, respectively.

B. subtilis exhibited the most significant growth enhancement, recording a 37.38% increase compared to screen 1. This increase was correlated with elevated concentrations of manganese, sodium chloride, calcium, potassium, and cobalt. The ANOVA results derived from the quadratic model indicated that B. subtilis demonstrated improved growth under nutrient-rich conditions characterized by increased levels of Mn, NaCl, Ca, K, and Co (Table 3). Among the nutrients, Mn functions as a critical cofactor for the various enzymes involved in cellular processes, including modulation of the oxidative stress and metabolic pathways. Evidence from several studies demonstrated that Mn enhanced the activity of antioxidant enzymes in Bacillus spp.; thereby facilitating growth under nutrient-rich conditions (Azeem et al., 2022; Moravcová et al., 2024; Sharma and Lamsal, 2025). Other nutrients, such as cobalt, plays a crucial role in the synthesis of cobalamin (vitamin B12), facilitating the metabolic processes of certain microorganisms (Moravcová et al., 2024). The model’s p-value was 0.610, suggesting that it was not statistically robust in elucidating the variation in growth rates. Nonetheless, the lack of fit p-value was 0.445, implying that the model reasonably fitted with the observed data, despite recording a low R² value of only 34.79%, indicating that additional factors may influence the growth variability of B. subtilis. De Leersnyder et al. (2018) reported that media composition strongly influenced bacterial responses, displaying that elevated concentrations of tryptone, yeast extract, or sulfide mitigated silver ion toxicity, thereby underscoring the critical role of medium components in shaping the experimental outcomes.

Table 3: The summary of specific growth rate and optimum medium composition of the EMs in screen 2.

Effective microorganism	Highest µmax (/h)	Centre point µmax (/h)	Increase over screen	Medium with highest µmax	Nutrient variation in best media	ANOVA
B. subtilis	0.553 ± 0.459	0.473 ± 0.449	37.38	115	Mn (+1), NaCl (+1), Ca (+1), K (+1), Co (+1)	Model’s p-value: 0.610; Lack-of-fit p-value: 0.445; R2 value: 34.79 %
C. utilis	0.470 ± 0.307	0.425 ± 0.169	28.15	33	Ca (+1), K (-1), YE (+1), Co (+1)	Model’s p-value: 0.013; Lack-of-fit p-value: 0.004; R2 value: 47.93%
P. acidilactici	0.083 ± 0.061	0.079 ± 0.050	0.87	21	Glucose (+1), Co (+1)	Model’s p-value: 0.664; Lack-of-fit p-value: 0.111; R2 value: 34.07 %
R. palustris	0.351 ± 0.208	0.334 ± 0.200	6.40	88	NaCl (+1), Co (-1), Ca (-1), B (+1), Mn (-1)	Model’s p-value: 0.011; Lack-of-fit p-value: 0.705; R2 value: 48.26 %
B. borstelensis	0.251 ± 0.160	0.243 ± 0.186	8.09	115	Fe (+1), Co (-1), Cu (-1)	Model’s p-value: 0.811; Lack-of-fit p-value: 0.777; R2 value: 31.81 %

Where; The effects of micro- and macro-elements on the growth performance of EMs were assessed at three different concentration levels, denoted as -1 (low), 0 (medium), and +1 (high). Mn (manganese), NaCl (sodium chloride), Ca (calcium), K (potassium), Co (cobalt), YE (yeast extract), B (boron), Fe (iron), Cu (copper). The model’s p-value ≤ 0.05 indicates that the model is statistically significant. A lack-of-fit p-value ≤ 0.05 indicates a significant lack of fit in the model.

Candida utilis demonstrated a growth increase of 28.15% compared to screen 1, primarily attributed to higher levels of calcium and yeast extract, alongside a beneficial reduction in potassium (Table 3). ANOVA analysis revealed a model p-value of 0.013, indicating statistical significance in explaining the variation in µmax. However, the model’s fit was inadequate, as indicated by a lack-of-fit p-value of 0.004. Nevertheless, the increase in µmax highlighted the importance of calcium and yeast extract for enhancing C. utilis growth, supported by a recorded R² value of 47.93% (Table 3). Calcium plays a crucial role in maintaining the integrity of cell walls and is essential for stabilising the cellular processes, particularly in response to stress conditions (Li and Shaw, 2023; Choudhary et al., 2024). Additionally, yeast extract is an important source of amino acids, peptides, vitamins, and essential growth factors, vital for the yeast metabolic processes (Tao et al., 2023; Timira et al., 2024).

In contrast, P. acidilactici exhibited only a marginal growth enhancement of 0.87% as observed in screen 1, which can be attributed to the increased concentrations of glucose and cobalt. Despite a recorded model fit of 0.111, the statistical model showed a lack of significance with a p-value of 0.664. These values indicated limited predictability and low explanatory power, as evidenced by a recorded R² value of 34.07% (Table 3). The modest increase in maximum growth rate (µmax) suggests that P. acidilactici may require complex nutrient sources for significant growth enhancement, including specific vitamins and nitrogen compounds beyond glucose or cobalt supplementation (Screpanti et al., 2024; Sun et al., 2024).

Rhodopseudomonas palustris demonstrated a growth enhancement of 6.40% in response to elevated concentrations of sodium chloride and boron, concurrently accompanied by a reduction in levels of manganese, cobalt, and calcium. The statistical model employed exhibited a significant efficacy, as evidenced by a recorded p-value of 0.011, a lack of fit p-value of 0.705, and a R² value of 48.26%, indicating a moderate explanatory power (Table 3). Sodium chloride functions dually as an osmotic regulator and a key influencer of nutrient transport processes (Solórzano-Acosta et al., 2023; Nguyen et al., 2024). Additionally, boron; a crucial micronutrient, plays a significant role in preserving the membrane integrity and facilitating enzyme activity in the photosynthetic microorganisms (Haleema et al., 2024; Zou et al., 2024).

On the other hand, the growth rate of B. borstelensis improved by 8.09% with an elevated iron and a low concentration of cobalt and copper. Although the model had no remarkable lack of fitness (Lack of fit p-value = 0.777), the recorded model’s p-value was greater than 0.05, indicating a lack of predictive strength (Table 3). Iron is essential for electron transport and enzymatic activities in the extremophiles and the thermophilic microorganisms, supporting metabolic and energy-generating pathways (Hussain et al., 2024; Ortega-Villar et al., 2024).

The findings from screen 2 highlight the critical importance of customized media formulations in facilitating optimal growth conditions for each EM. The examination of both positive and negative nutrient variations revealed considerable interactions among the individual nutrients and the metabolic pathways of the microorganisms. Although several models revealed low predictive power (R² values < 50%), reasonable fits were observed in the lack-of-fit analyses, with recorded p-values exceeding 0.05. It is important to note that several statistical models displayed a low predictive power (R² values <50%). Therefore, further refinement of the optimization process is necessary, focusing on additional nutrient interactions and environmental factors to enhance the model accuracy and predictability.

The optimized media for EM culture

The optimal media compositions for B. subtilis, C. utilis, P. acidilactici, R. palustris, and B. borstelensis were established through a systematic nutrient screening process utilizing the Box-Behnken Design that are presented in Table 4. The resultant optimized media formulations were derived from the key nutrients that considerably influenced the growth dynamics of these microorganisms. This systematic optimization underscores the critical roles of the tailored media compositions in enhancing the metabolic performance of each EM, reflecting their distinct physiological requirements and environmental adaptations.

All EM demonstrated varying degrees of nitrogen source utilization using yeast extract, with C. utilis being uniquely dependent on ammonium sulfate. Furthermore, P. acidilactici was the sole microorganism that utilized beef extract and peptone

Table 4: The Optimized media compositions for EMs production.

Nutrients	Optimal media compositions concentration (mM)
Nutrients	Bacillus subtilis	Candida utilis	Pediococcus acidilactici	Rhodopseudomonas palustris	Brevibacillus borstelensis
Yeast extract	6.0 g/L	0.7 g/L	10.0 g/L	2.3 g/L	4.0 g/L
Beef extract	-	-	10.0 g/L	-	-
Peptone	-	-	2.5 g/L	-	-
(NH4)2SO4	-	12.000	-	-	-
Glucose	40.0	42.000	72.100	40.000	-
Sodium bicarbonate	-	-	-	-	10.000
MnCl2.4H2O	9.900	0.505	0.125	1.617	9.900
K2HPO4	8.611	2.800	8.209	4.305	8.610
MgSO4.7H2O	3.200	0.122	1.217	1.217	0.974
CaCl2.2H2O	5.680	1.700	0.517	1.034	5.680
NaCl	8.562	85.616	-	32.248	33.000
Sodium acetate	-	-	30.477	-	-
CuSO4.7H2O	0.721	0.180	1.440	0.180	0.721
ZnSO4.7H2O	1.700	0.696	0.348	0.550	1.700
FeSO4.7H2O	16.000	2.698	3.500	2.698	7.000
CoCl2.6H2O	2.360	0.441	1.180	1.765	23.620
H3BO3	0.970	-	-	13.909	-
Na2SeO3	-	-	-	0.046	-
Na2MoO4	-	-	-	-	0.024
Na2EDTA	0.5373, pH 8.0	0.5373, pH 8.0	0.5373, pH 8.0	0.5373, pH 8.0	0.5373, pH 8.0
Phosphate Buffer Saline	100, pH 6.4	100, pH 6.4	100, pH 6.2	100, pH 6.8	100, pH 6.4

as nitrogen sources. This differentiation emphasised the importance of nitrogen source variability in microbial growth and suggested that C. utilis may preferentially metabolize inorganic nitrogen, while P. acidilactici relied on more complex organic nitrogen sources. This observation highlights the diverse metabolic adaptations among the microorganisms to the various nitrogen sources (Screpanti et al., 2024; Sun et al., 2024).

Glucose was identified as the primary carbon source for all EMs, with the notable exception of B. borstelensis. Instead, B. borstelensis predominantly utilized sodium bicarbonate, which likely supported its adaptation to carbon-limited and more alkaline environments. This finding underscored the metabolic versatility of B. borstelensis, suggesting its reliance on inorganic carbon fixation pathways rather than glycolysis, which was more prevalent in the other EMs. Probst et al. (2017) reported the central role of the Wood–Ljungdahl (WL) pathway in carbon fixation and metabolic versatility across diverse microbial lineages, noting that in high-CO2 subsurface environments, the WL pathway and Calvin–Benson–Bassham cycle were the most prevalent carbon fixation strategies. Many autotrophs in these systems employed mixotrophic growth, further enhancing their metabolic adaptability. This divergence highlighted the metabolic versatility of B. borstelensis in utilizing inorganic carbon via several fixation pathways, contrasting with the glycolytic reliance observed in the other EMs.

Most EMs required elevated Mn concentrations, essential for their enzymatic activity, oxidative stress management, and cell wall integrity (Bosma et al., 2021). Manganese acts as a crucial cofactor for various enzymes, facilitating essential biochemical pathways, particularly in B. subtilis and B. borstelensis, which displayed the highest Mn requirements. Exceptions included P. acidilactici and C. utilis, which exhibited lower Mn dependency. Specifically, C. utilis required a lower concentration of Mg (0.122 mM), whereas P. acidilactici necessitated Ca at a concentration of 0.517 mM. The differential requirements for Mg and Ca reflected the species-specific needs for ionic stabilization, enzymatic function, and membrane integrity. Wang et al. (2019) emphasized that divalent cations were essential nutrients for bacterial growth and cell maintenance, exerting multifaceted effects on cell attachment and biofilm formation through physicochemical interactions, gene regulation, and biomacromolecular structural modifications.

Sodium chloride served as a universal salt requirement for most EMs, with the exception of P. acidilactici, which predominantly utilized sodium acetate. This indicated that P. acidilactici may have a distinct osmotic regulatory mechanism, relying on sodium acetate as both a carbon source and an osmotic stabilizer. In contrast to the strain-dependent osmoadaptation observed in P. freudenreichii (Gaucher et al., 2019), this dual role of sodium acetate suggests that different lactic acid bacteria have evolved species-specific strategies to cope with osmotic stress. Such diversity in osmoadaptive mechanisms highlights the metabolic versatility of the bacteria under fluctuating environmental conditions. Notably, there were marked variations in the required concentrations of Fe and Co, wherein B. subtilis required elevated levels of Fe and B. borstelensis exhibited a higher requirement for Co. This variation suggests differences in ionic regulation, electron transport, and cofactor biosynthesis, particularly for the enzymes dependent on Fe and Co (Solórzano-Acosta et al., 2023; Nguyen et al., 2024; Zou et al., 2024). To maintain pH stability, all media were standardized with 0.5373 mM Na2EDTA and supplemented with PBS. EDTA served as a chelating agent to prevent metal ion precipitation, ensuring the bioavailability of essential micronutrients such as Mn, Fe, and Co. The pH of the media was meticulously adjusted using HCl, H2SO4, or NaOH to align with the species-specific optimal growth conditions, which were determined in screen 1 based on several previous studies (Sánchez-Clemente et al., 2018; Prakash et al., 2025). Maintaining the optimal pH is essential for enzyme function, nutrient solubility, and membrane stability, thereby enhancing microbial growth and metabolic efficiency. Higher pH soils have been shown to support more complex and stable microbial networks with enhanced nutrient cycling functions, particularly for carbon, nitrogen, phosphorus, and sulfur metabolism (Guo et al., 2022). Moreover, pH interacts with other environmental factors in complex and non-additive ways to optimize microbial processes (Sánchez et al., 2024).

Micronutrient supplementation plays a species-specific role in supporting the growth of EMs. Boric acid exhibits a vital role in maintaining membrane integrity and facilitating enzymatic activity, particularly in photosynthetic microorganisms such as R. palustris (Harwood, 2022). In this study, boric acid was found to be essential for both B. subtilis and R. palustris, indicating a shared dependency, whereas sodium selenite was uniquely utilized by R. palustris. In contrast, B. borstelensis relied on sodium molybdate, underscoring distinct micronutrient preferences among these microorganisms. Collectively, these findings emphasized that differential micronutrient requirements shaped the metabolic adaptability and ecological niches of EM species. All microbial media were standardized to contain precisely 0.5373 mM of Na2EDTA at a pH of 8.0. The inclusion of sodium selenite and sodium molybdate reflected the specific metabolic pathways of R. palustris and B. borstelensis, respectively, including electron transport and nitrogen fixation. Previously, Fixen et al. (2018) used R. palustris as a model microorganism to investigate the regulatory and physiological factors governing nitrogenase activity. The obtained results demonstrated that ferredoxin Fer1 was the primary; though not exclusively, an electron carrier protein encoded by R. palustris, which donated electrons to nitrogenase. In a similar pattern, the same concentration of PBS was utilized across all used media as a buffering agent, albeit with varying pH values. Following the addition of the buffer, the pH levels were meticulously measured and monitored using a pH meter. Adjustments to the acidity or alkalinity of each medium were executed through the addition of HCl or H2SO4 to increase the acidity and NaOH to elevate the alkalinity, aligning with optimal pH levels determined in several previous literatures (Sánchez-Clemente et al., 2018; Prakash et al., 2025). This rigorous pH control ensures that the intracellular and extracellular environments are conducive to optimize microbial growth, supporting the physiological requirements of each EM.

Reproducibility of the optimized formulation

The dry cell weight results were collected after 2 h of cultivation in a 1 L flask inoculation system, as detailed in Table 5. The yield patterns revealed distinct metabolic adaptations among the tested EMs, underscoring their varying strategies for nutrients utilization. The yield patterns derived from the tabulated data exhibited a consistent trend across the various EM, with the exception of B. subtilis and C. utilis. Notably, microorganisms such as B. subtilis and B. borstelensis displayed superior growth in nutrient-limited media, indicating their metabolic flexibility and resource efficiency (Saxena et al., 2020). The biomass produced in low-nutrient media demonstrated slightly superior productivity compared to high-nutrient media, with the middle-nutrient levels yielding intermediate results. For instance, B. subtilis achieved the highest biomass production of 1.384 g/L in low nutrient conditions, followed by 1.083 g/L in middle medium, and 0.662 g/L in high nutrient conditions. This inverse correlation between nutrient concentration and biomass for B. subtilis suggested the potential growth inhibition at elevated nutrient levels, likely due to osmotic stress or nutrient imbalances. These findings highlight the ability of B. subtilis to optimize the metabolic pathways under stress conditions, supporting its suitability for cost-effective industrial applications.

Table 5: Dry cell weight of EMs production after 2 hour of flask scale cultivation.

Cultures	Cell dry weight (g/L)
Cultures	High (1)	Middle (0)	Low (-1)
Bacillus subtilis	0.662 ± 0.02	1.083 ± 0.006	1.384±0.08
Candida utilis	0.188 ± 0.01	0.075 ±0.015	0.143±0.008
Pediococcus acidilactici	0.028 ± 0.02	0.025 ± 0.015	0.030±0.01
PSB	0.333 ± 0.05	0.323 ± 0.01	0.388±0.01
Brevibacillus borstelensis	1.085 ± 0.25	0.933 ±0.38	1.563±0.35

Where; The cell dry weight is represented in mean of triplicate ± standard error of the mean (sem); PSB: Photosynthetic bacteria.

In contrast, C. utilis displayed its highest biomass yield of 0.188±0.01 g/L in high nutrient media, followed by a yield of 0.143±0.008 g/L in low nutrient, and 0.075±0.015 g/L in medium nutrient conditions. This trend indicated that C. utilis benefited from nutrient-enriched environments, likely due to its enhanced capacity for nitrogen and carbon assimilation when nutrients availability was high. The comparatively lower yield observed in the middle medium may reflect that suboptimal nutrient concentrations did not fully support its metabolic demands. For P. acidilactici, nutrient composition had a pronounced effect on growth, with maximum specific growth rates increasing from ~0.05/ h under low manganese and beef extract to ~0.27/ h at optimal conditions (i.e., 0.25 mM Mn and ~5 g/L beef extract). Beyond this range, higher nutrient concentrations reduced growth efficiency, though a secondary increase was observed under high beef extract (~10 g/L) combined with elevated manganese (~0.5 mM). These negligible differences suggested that P. acidilactici demonstrated a robust growth profile across varied nutrient environments, due to its adaptability to nutrient uptake mechanisms. However, the overall low biomass may indicate slower growth rates or limited metabolic activity within the brief cultivation period.

The photosynthetic bacterium R. palustris displayed a modest increase in biomass in low nutrient media (0.388 g/L), followed by a yield of 0.333 g/L in high, and 0.323 g/L in medium nutrient conditions. The superior yield in low-nutrient environments suggested a preference for minimal nutrient concentrations, possibly due to its inherent ability to utilize light as an energy source and its capacity for inorganic carbon fixation. B. borstelensis recorded a peak biomass yield of 1.563 g/L in low nutrient medium, with reductions to 0.933 g/L in medium and 1.085 g/L in high media. Similar to B. subtilis, B. borstelensis demonstrated improved growth in nutrient-limited conditions, aligning with their previously identified thermophilic and stress-resistant characteristics, which may confer advantages in environments characterised by moderate nutrient stress (Aqel et al., 2024). This suggests that B. borstelensis can be a viable candidate for industrial processes that prioritize resource efficiency and environmental sustainability.

The dry cell weight data presented herein highlight the diverse metabolic strategies employed by the various EMs under different nutrient conditions. While microorganisms such as C. utilis exhibited enhanced growth in enriched media, others as B. subtilis and B. borstelensis thrived in nutrient-limited environments. This metabolic diversity highlights the importance of tailoring cultivation conditions to the specific nutrient preferences and stress tolerances of each microorganism, ensuring optimal biomass production and metabolic efficiency. This study holds significant potential for advancing microbial biotechnology and promoting sustainable and regenerative agriculture. By optimizing the media formulations, it supports the large-scale production of high-quality EMs for consistent biofertilizers performance. This study further enhances the understanding of EM nutritional ecology, emphasising the strategic use of nutrient-limited conditions to maximize the growth of microorganisms with stress-adaptive metabolic pathways. Aligned with the global efforts to reduce the agriculture’s environmental impact, the present study advocates for EM as a sustainable alternative to the chemical inputs, contributing to food security and environmental sustainability. Through utilizing advanced tools such as AMOS, the current study bridges the theoretical insights with practical applications, establishing EM as a cornerstone for eco-friendly agricultural systems and enhancing global efforts toward green biotechnology.

Conclusions and Recommendations

This study effectively identified and optimized essential nitrogen and carbon sources to enhance growth rates of five microorganisms: B. subtilis, C. utilis, P. acidilactici, R. palustris, and B. borstelensis. By integrating systematic screening, RSM, and Box-Behnken Design, the authors developed species-specific nutrient formulations that considerably boosted growth performance across various conditions, emphasizing the versatility of microbial metabolic pathways. The findings of this study resulted in the development of tailored nutrient mixtures that maximized the specific growth rates (µmax), validated under laboratory-scale (1 L flask) cultivation with recommendation for pilot-scale confirmation. Among the tested strains, B. subtilis exhibited the most promising enhancement, achieving 37.38% growth increase under manganese, cobalt, and sodium chloride supplementation, with a reproducible biomass yield of 1.384 g/L. This highlights the B. subtilis’s ability to leverage trace elements for its metabolic efficiency and stress resistance, positioning it as a strong candidate for agricultural and biotechnological applications. C. utilis showed 28.15% increase when cultivated with ammonium sulphate, calcium, and yeast extract, underscoring its reliance on ammonium-based nitrogen assimilation pathways. In contrast, P. acidilactici demonstrated only marginal improvement (0.87%) with beef extract and cobalt, while R. palustris displayed moderate gains (6.4%) with sodium chloride and boron, with sensitivity to excessive manganese. B. borstelensis recorded 8.09% increase with iron and sodium bicarbonate supplementation, yielding 1.563 g/L biomass in flask cultures, confirming its thermophilic iron dependency as an advantage for high-temperature bioprocesses. These optimized media formulations not only improved growth rates but also highlighted the significance of micronutrients in microbial metabolism. Our findings demonstrate that fine-tuning micronutrient concentrations can modulate metabolic pathways, optimize resource utilisation, and improve overall biomass yield, thereby offering a framework for cost-effective media formulation in industrial settings. This study also underscores the utility of RSM in nutrient optimization, with potential applications in microbial biomass production and sustainable biofertilizer development for improving agricultural productivity. Future research studies should focus on validating the scalability of these formulations in bioreactor systems to assess their commercial viability. Additionally, elucidating the mechanistic pathways governing nutrient utilization such as the activation of nutrient-specific transporters, metabolic flux redistribution, and regulatory network modulation will provide deeper insights into microbial adaptation and optimization strategies. Further, linking nutrient optimization with systems biology approaches (e.g., transcriptomics, fluxomics, and proteomics) is recommended to establish predictive models for precision fermentation tailored to specific industrial applications, including biofertilizers, bioremediation, and scalable synthesis of high-value biochemicals.

Acknowledgment

We would like to express our gratitude to Hydro Plant Sdn. Bhd., who supplied the effective microorganisms) and the Faculty of Pharmacy, Universiti Teknologi MARA Puncak Alam Campus that provided the laboratory facilities.

Novelty Statement

Using AMOS and Box-Behnken Design, this study optimized nutrient formulations for five EMs, achieving remarkable strain-specific biomass improvements. The obtained results were reproducible in scaled-up cultures, demonstrating a robust and high-throughput strategy for enhancing microbial growth. This approach supports the scalable biofertilizers development for sustainable and efficient agricultural biotechnological applications.

Author’s Contribution

KAR, MFI and ES: Conceptualization.

NS, KI, AI and MSA: Methodology.

MFI and NS: Analysis of results.

MFI, ES and KAR: Review writing and editing.

Ethical approval

There is no ethical approval required for this study.

Funding source

This work was jointly supported by the Faculty of Applied Science, Universiti Teknologi MARA (UiTM) and the Ministry of Higher Education Malaysia (MOHE) via the Fundamental Research Grant Scheme (FRGS), My GRANTS Reference Code: FRGS/1/2018/WAB01/UITM/02/9 and Project ID: 14282.

Generative AI and AI-assisted technology statement

The authors confirm that no generative artificial intelligence (AI) tools were used in the conception, data collection, analysis, interpretation, or writing of this manuscript. All content was created solely by the authors.

Conflict of interests

The authors have declared no conflicts of interest.

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