Study and Effect of Agitation on Kojic Acid Production by Aspergillus oryzae in Liquid Fermentation

Abstract

Kojic acid (KA) is an economically important molecule, due to its functions as an anti-inflammatory, antifungal, and facial skin-lightening agent. Considering the wide application of this metabolite, it is essential to study processes that increase or improve its production. The objective of this study was to evaluate the effect of agitation on fungal KA production. To evaluate the effect of agitation on fungal KA production, liquid medium fermentation was carried out using batch bioreactors with a capacity of one liter. The Aspergillus oryzae strain was used, with glucose as the sole carbon source. Three experimental factors were evaluated: illumination (light or darkness), agitation type (no agitation, bubbling, and tangential), and time (0, 24, 48, 72, 96, 120, 144, 168 h). The evaluated variables included pH, product-to-biomass yield, protein content, reducing sugar consumption, and KA concentration. The bubbling level with light for 144 h showed the highest efficiency by producing 7.86 ± 2.21 g KA/L. The production of KA in liquid medium with the fungus A. oryzae requires bubbling conditions with light to achieve the best yields and production. The findings in this study provide insights into the influence of agitation conditions on KA biosynthesis and its potential for scaling up industrial fermentation. However, future work could investigate the metabolic and genetic mechanisms of this enhanced production to generate more efficient biotechnological applications for KA production.

1. Introduction

During the life cycle of a crop, it goes through several phenological stages, such as germination, seedling, growth, flowering, fruiting, aging, and death. However, the seedling stage is one of the most significant, as it will completely influence the development conditions of the subsequent stages. As we know, this stage involves the transition from relying on internal storage sources, such as those provided by the cotyledons from the seeds, to external sources, such as the soil [1]. Many commercially important crops begin their early stages as seedlings grown in trays, which helps control growth conditions, climate, as well as pests and diseases, directly impacting their development program. This approach focuses on improving traits for better performance under stress conditions or to enhance the quality of the commercially important organ [2]. Living organisms develop and maintain their morphological conditions through signaling cascades [3], which can be influenced by abiotic stress conditions. Among many effects, one of the first and most important is the generation of reactive oxygen species, such as superoxide, hydrogen peroxide, and hydroxyl radicals [4]. This leads to oxidative stress, also increasing membrane permeability, damaging proteins, and affecting cellular structure, with a possible risk of cell death [5]. A possible solution is the use of biostimulants, which have been applied for a long time to generate a biological response in plants [6]. There are many definitions for the term “biostimulant”; however, it can be considered any chemical substance, physical stimulus, or microorganism that, when applied in small quantities, promotes quality improvements or helps crops under stress conditions [7].

Under the terms of the previous concept, the use of kojic acid in agriculture can be considered as a biostimulant. Kojic acid (KA) is an organic acid, produced as a secondary metabolite in microorganisms of the Aspergillus genus. The chemical name of kojic acid is 5-hydroxy-2-(hydroxymethyl)-4-pyrone, with the molecular formula C₆H₆O₄ [8]. In terms of its chemical characteristics, it has a ring-like structure, which gives it properties such as low acidity, metal-chelating action on metals like copper and iron, and antimicrobial qualities, which lead to a higher demand for the health industry [9]. In agriculture, it contributes by inhibiting polyphenol oxidase synthesis to prevent fruit browning. In both cases, its inhibitory action works through interference with the oxygen uptake required for enzymatic activity [10]. One of the main organisms used for KA production is the fungus Aspergillus oryzae, which is classified by the FDA under the GRAS (Generally Recognized As Safe) term, meaning it is safe for human consumption [11]. When using this fungus for KA production as a secondary metabolite, the conditions during its growth, such as the carbon source, nitrogen source, temperature, must be considered. However, one of the most important factors influencing KA production is the type of agitation [12]. Agitation is the process by which nutrients and oxygen in the liquid medium are homogenized and concentration gradients in the medium are avoided. This condition will depend greatly on how the agitation is performed, whether tangential or bubbling [13]. For the production of kojic acid with Aspergillus oryzae, systems such as solid-state fermentation have been used, in which the microorganism is cultivated on a moist, solid material that serves as support and nutrients for its growth. However, one of its disadvantages compared to liquid fermentation is the lack of control over factors such as temperature, oxygen, and humidity in the medium [14]. Similarly, liquid fermentation can also be performed, in which there is greater control over important variables in the production of kojic acid, such as oxygenation and medium homogeneity, among others, which facilitates its scaling in production and industry [15]. Regarding the liquid medium, various systems can be used, such as batch fermentation, which was employed in this study because it is a very simple system to operate with a low risk of contamination and is the most commonly used in industry. When compared to the fed-batch system, the latter has the advantage of even greater control over the liquid medium and an increase in productivity, although it requires more operational control [16].

El-Kady et al. [17] studied KA production in a liquid medium based on different agricultural residues with the Aspergillus flavus strain and found higher KA levels when agitation was set at 150 rpm, compared to static conditions. Literature data suggest that higher revolutions per minute, greater than 150 rpm, are the most productive for KA production. However, other studies have found conditions of 200 rpm in a liquid medium with rice noodle as the carbon source and the A. oryzae ATCC 10124 strain, yielding 1.65 g/L of KA [12]. There are even studies where genetically modified Aspergillus niger strains were tested for KA production under conditions of 250 rpm and 1 vvm (volume of air per volume of medium per minute) aeration, demonstrating the importance of agitation in KA production [18]. It is clear that one or more variables, including agitation, play a significant role in KA production. El-Aziz et al. [19] reported that with A. flavus under conditions of 30 °C and 180 rpm, the best productivity results of 0.234 g/[L·h] were achieved. The importance of agitation is frequently highlighted in research for increasing KA production, even when using genetically modified strains. Wu et al. [18] used a genetically modified A. niger S834 strain derived from the ATCC 1015 strain, which was altered to inhibit genes related to KA production, under liquid medium cultivation conditions at 200 rpm for 7 days at 28 °C, resulting in a production of 25.71 g/L [18]. Considering the importance of agitation in KA production by A. oryzae, and despite various studies aimed at increasing and optimizing the metabolite’s production through agitation, there were no records of bubbling agitation being used to enhance KA production by A. oryzae or the effect of prolonged periods of darkness on the fungus. Regarding the potential implications of this study, one is the industrial optimization for kojic acid production by identifying optimal agitation and bubbling conditions, which would reduce costs and production time. The homogeneity of the liquid medium could facilitate the scaling-up process for kojic acid production, which directly impacts industries such as the pharmaceutical industry. Additionally, the results could have applications in the agriculture industry, particularly in the postharvest handling of fruits and vegetables by offering a more efficient production of this metabolite. This study proposed that by applying a homogeneous agitation source in a liquid medium with the A. oryzae strain, the necessary conditions were created to induce the synthesis of a higher amount of KA. The objective of this work was to apply both tangential and bubbling agitation in a liquid medium and combine them with periods of light and darkness to achieve more efficient kojic acid production.

2. Materials and Methods

2.1. Location and Biological Material

The work was carried out at the Universidad Autónoma Agraria Antonio Narro (UAAAN), within the Department of Food Science and Technology (FSTD), in the Fermentations and Biomolecules Laboratory (FBL). The fungus Aspergillus oryzae (ATCC 10124) from the FBL collection was used for metabolite production.

2.2. Strain Culturing

The strain was preserved at −20 °C in skim milk (10%, w/v) and glycerol (5%, w/v). To reactivate the microorganism, inoculation was carried out on Petri dishes using potato dextrose agar (PDA; DIBICO, México) culture medium for 7 days at 30 °C. Subsequently, the inoculum was harvested using 1% Tween 80 solution (HYCEL, Zapopan, Jalisco, Mexico), and the spore count was determined using a Neubauer chamber (MARIENFELD, Monterrey, Nuevo León, Mexico) for the fermentation process, inoculating 1 × 10⁶ spores per ml of A. oryzae [20].

2.3. Agitation Effect on Fermentation

A solution containing 100 g/L glucose (FERMONT, Monterrey, Nuevo León, México), 2.5 g/L yeast extract (DIBICO, México), 1 g/L KH₂PO₄ (J.T.BAKER, Monterrey, Nuevo León, México) and 0.5 g/L MgSO₄·7H₂O (J.T.BAKER, Monterrey, Nuevo León, México), was used as the growth medium. The initial pH of the solution was 5.5. Fermentation was carried out in glass reactors with 1 L capacity, operating at 75% of their capacity, under four different conditions (darkness, light, tangential agitation, and bubbling agitation). Figure 1 presents the schematic diagram of the operation of the batch-type reactor under the most effective condition for kojic acid production, which was bubbling agitation for 144 h under light exposure. For the light conditions, two Goodwill LED lamps (Goodwill model 20461, Saltillo, Coahuila, México) with a luminous output of 2880 lumens were used, positioned 3 m apart and applied only from the top. Tangential agitation was set at 125 rpm using a magnetic stirrer (Scientific brand model CVP-3250A, Monterrey, Nuevo León, México). An agitation speed of 125 rpm was selected based on previous studies that reported it as optimal for kojic acid production, as it provides sufficient oxygen and homogenization of the liquid medium [20].

For the bubbling agitation conditions, an Elite 799 air pump (Hagan, Saltillo, Coahuila, México) of 1.5 watts was used, with an airflow of 1.2–1.5 L/min. Sampling was performed every 24 h, starting from time zero and continuing until 168 h, with a total of 8 samples per fermentation condition, for the following 6 treatments (

Table 1. Description of the treatments performed and their abbreviations for later reference.

Treatment	Description	Abbreviation
1	Bubbling in light	BL
2	Bubbling in darkness	BD
3	Tangential in light	TL
4	Tangential in darkness	TD
5	Static in light	SL
6	Static in darkness	SD

).

2.4. Kojic Acid Determination

After fermentation, the entire extract was referred to as raw fermentation extract (RE). For the analytical determinations, the filtered extract (FE) was used, obtained by centrifuging the RE for 10 min at 279.5 RCF (Relative Centrifugal Force). The supernatant was then collected and stored at −20 °C for future analytical determinations. For the determination of KA, the colorimetric technique by Bentley [21] was used, which is based on the chelation of KA with iron. Next, 1 mL of previously filtered RE and 2 mL of 1% FeCl3 prepared with 0.1 N HCl were added, and the mixture was diluted with 5 mL of distilled water [20]. The technique uses 2 mL of ferric chloride, which undergoes a color change to reddish at a wavelength of 505 nm in a UV-vis spectrophotometer (Thermo Fisher Scientific, model G10S, Waltham, MA, USA). The glucose content was quantified using the Miller method [22], placing 1 mL of RE and 1 mL of DNS reagent into glass tubes. The tubes were boiled in a water bath for 5 min; then, the reaction was stopped with an ice bath for 5 min. A total of 5 mL of distilled water was added, shaken, and left to stand for 5 min at room temperature [20]. To calculate the biomass, the RE was directly measured in a spectrophotometer at a wavelength of 450 nm, and the variable was expressed in absorbance units [20]. Each of the aforementioned techniques was performed in triplicate.

2.5. Kinetic Modeling

The Verhulst logistic growth, Pirt, and Luedeking–Piret models were used to estimate the theoretical values of the biomass, substrate, and product coefficients, respectively, as described by Robledo et al. [23] and Quiterio et al. [20]. All calculations were performed using the Solver add-on in Microsoft Excel 2021.

The growth rate of the microorganism was measured by estimating the number of cells (X) over time (t), considering the carrying capacity of the system (X_max) and the maximum rate of cell growth (µ_max):

$${\displaystyle \frac{dX}{dt}}={\mu }_{max}·X\left({\displaystyle \frac{X_{max}-X}{X_{max}}}\right)$$

(1)

with the solved form

$$X_{\left(t\right)}={\displaystyle \frac{X_{max}{X}_0}{\left(X_{max}-X_0\right)e^{-{\mu }_{max}t}+X_0}}$$

(2)

where X₀ are the cells present in the medium when t = 0. For values of µ_max > 0, the resulting growth curve has a sigmoidal shape and is asymptotic to the carrying capacity (X_max). The higher µ_max is, the faster the curve reaches the load capacity (X_max).

For the production of biomass, it is required to consume substrate (S) for the synthesis of new cells (SG) and to maintain existing cells (SM). Substrate consumption will always be related to time and the biomass in the culture medium, and it can be represented by the following expression:

$$-{\displaystyle \frac{dS}{dt}}={\displaystyle \frac{1}{Y_{X/S}}}·{\displaystyle \frac{dX}{dt}}+m·X$$

(3)

where m is the cell maintenance coefficient and Y_X/S is the maximum growth yield coefficient when ΔSM = 0.

$$Y_{X/S}={\displaystyle \frac{X_2-X_1}{(S_1-S_2{)}_G+(S_1-S_2{)}_M}}$$

(4)

$$Y_{X/S}={\displaystyle \frac{X_2-X_1}{(S_1-S_2{)}_G}}$$

(5)

where X₁ is the biomass initial condition and X₂ is the biomass maximum value; S₁ is the substrate initial condition and S₂ is the substrate concentration maximum value. The solution of Equation (4) can be obtained as a function of X as follows:

$$S_{\left(t\right)}=S_0-{\displaystyle \frac{X-X_0}{Y_{X/S}}}-{\displaystyle \frac{m·X_{max}}{{\mu }_{max}}}·\mathit{ln}\left[{\displaystyle \frac{X_{max}-X_0}{X_{max}-X}}\right]$$

(6)

where S₀ is the initial condition for substrate level S.

The kinetics of product (P) formation were modeled using the equation as follows:

$${\displaystyle \frac{dP}{dt}}=Y_{P/X}·{\displaystyle \frac{dX}{dt}}+k·X$$

(7)

where Y_P/X is the product yield in terms of biomass (units of product per unit of biomass) and k is the secondary coefficient of product formation or destruction.

$$Y_{P/X}={\displaystyle \frac{P_2-P_1}{X_1-X_2}}$$

(8)

where P₁ is the product initial concentration and P₂ is the maximum product concentration; X₁ is the biomass initial condition and X₂ is the biomass concentration at the product maximum value. It is possible to solve Equation (8) as a function of biomass.

$$P_{\left(t\right)}=P_0+Y_{P/X}·\left(X-X_0\right)+{\displaystyle \frac{k·X_{max}}{{\mu }_{max}}}·\mathit{ln}\left[{\displaystyle \frac{X_{max}-X_0}{X_{max}-X}}\right]$$

(9)

Substrate consumption (q_S) and product formation (q_P) rates are linked to transformation yields and growth rates, respectively.

$$q_P={\mu }_{max}·Y_{P/X}$$

(10)

$$q_S={\displaystyle \frac{{\mu }_{max}}{Y_{X/S}}}+m$$

(11)

2.6. Statistical Analysis

A randomized multifactorial experimental design was used, with 3 factors (agitation, light, and time) at various levels (agitation = 3, light = 2, and time = 8), with KA production as the response variable. The reported data are the arithmetic mean ± standard deviation, with n = 3 for each sampling time. The multifactorial experimental analyses were carried out using Minitab 17 software (17.1.0). All other data were analyzed using Infostat 2016 statistical software (2016 version), applying a Shapiro–Wilk normality test, one-way ANOVA, and Fisher’s Least Significant Difference (LSD) post hoc test (p < 0.05).

3. Results

3.1. General Overview of KA Production Under Different Conditions

The kinetics for KA production obtained in this study reflect the interaction of the three evaluated factors: agitation, light, and time. Although some two-way interactions did not show significant differences, significant differences were observed in the three-way interactions. Overall, a generalized effect was seen across all variables, with the light–bubbling–144 h condition yielding the best results.

3.2. Statistical Analysis of Factor Effects

Table 2. Statistical comparison of the experimental factorial design, with its three levels for the kojic acid variable.

Source	DF	SS Adjust	MS Adjust	F Value	p-Value
Model	47	1135.56	24.1608	6.73	0.000
Lineal	10	564.69	56.4687	15.72	0.000
Agitation	2	155.80	77.8992	21.69	0.000
Light	1	0.18	0.1812	0.05	0.823
Time	7	412.91	58.9875	16.42	0.000
2-term interactions	23	371.71	16.1612	4.50	0.000
Agitation × Light	2	110.95	55.4740	15.45	0.000
Agitation × Time	14	224.83	16.0595	4.47	0.000
Light × Time	7	27.17	3.8810	1.08	0.383
3-term interactions	14	120.44	8.6032	2.40	0.007
Agitation × Light × Time	14	120.44	8.6032	2.40	0.007
Error	86	308.86	3.5915
Total	133	1444.42

shows the ANOVA of the general factorial regression of experimental data, according to the factorial design with various levels. It can be observed that, individually, the light variable did not have a significant effect on the production of kojic acid. Regarding the two-term interaction, the light time interaction did not show a significant effect on KA production, while the agitation × light and agitation × time interactions showed an effect on KA production.

3.3. Main and Interaction Effects on Kojic Acid Production

In Figure 2, the main effects and interactions affecting the production of KA by the fungus Aspergillus oryzae can be observed. Bubble agitation and a 168 h fermentation time are the levels that contribute the most to the highest amount of KA produced (Figure 2A). Regarding the tangential agitation treatment, it shows lower values than the static condition, with no statistically significant difference. In the interaction of the three factors (Figure 2B), it was shown that the conditions Tangential + light + 168, Bubble + darkness + 168, and Bubble + light + 144 were the ones that showed a significant effect with p-values of 0.028, 0.000, and 0.000, respectively.

3.4. Production Kinetics and Comparison Among Treatments

Only four (TL, BD, BL, SD) of the six treatments were significant (p < 0.05) over time. The condition with the interaction of the three levels that showed the highest KA production was BL for 144 h, with a value of 7.86 ± 2.21 g KA/L (

Table 3. Kinetics of kojic acid production in liquid medium fermentation with the A. oryzae strain, under different agitation and light conditions. The points represent the mean (n = 3) and the bars represent the standard deviation of the samples.

Treatment	Time (h)	Kojic Acid (g/L)	Treatment	Time (h)	Kojic Acid (g/L)	Treatment	Time (h)	Kojic Acid (g/L)
BL	24	0.70 ± 0.57	TL	24	1.20 ± 0.08	SL	24	0.30 + 0.19
BL	48	1.16 ± 0.44	TL	48	1.63 ± 0.15	SL	48	0.33 + 0.03
BL	72	2.79 ± 0.37	TL	72	1.46 ± 0.14	SL	72	0.18 + 0.15
BL	96	5.02 ± 0.98	TL	96	1.47 ± 0.09	SL	96	0.32 + 0.29
BL	120	6.56 ± 1.82	TL	120	1.72 ± 0.35	SL	120	0.27 + 0.11
BL	144	7.86 ± 2.21	TL	144	1.77 ± 0.15	SL	144	0.61 + 0.24
BL	168	7.61 ± 2.36	TL	168	2.07 ± 0.47	SL	168	1.02 + 0.39
BD	24	0.42 ± 0.00	TD	24	0.76 ± 0.01	SD	24	0.06 + 0.13
BD	48	0.77 ± 0.21	TD	48	0.78 ± 0.01	SD	48	1.25 + 0.00
BD	72	1.06 ± 0.32	TD	72	0.77 ± 0.00	SD	72	0.33 + 0.00
BD	96	2.68 ± 1.97	TD	96	0.78 + 0.01	SD	96	1.56 + 0.78
BD	120	3.17 ± 0.33	TD	120	0.76 + 0.01	SD	120	5.54 + 4.81
BD	144	4.99 ± 1.03	TD	144	0.78 + 0.01	SD	144	7.50 + 3.82
BD	168	6.55 ± 0.27	TD	168	0.77 + 0.00	SD	168	7.60 + 5.66

and Figure 3). This value was not statistically different from 120 h (6.56 ± 1.82 g KA/L) and 168 h (7.61 ± 2.36 g KA/L) for the same conditions, nor from 144 h (4.99 ± 1.03 g KA/L) and 168 h (6.55 ± 0.27 g KA/L) of the BD treatment.

3.5. Reducing Sugar Consumption and Biomass Formation

During the production kinetics, the amount of glucose consumed (Figure 4A) in the liquid medium by the A. oryzae fungus is an important factor, as it determines the microorganism’s growth (Figure 4B) and the subsequent synthesis of the metabolite. The treatment with the lowest sugar consumption was the SD treatment (15% consumption), indicating unfavorable conditions for microbial growth. Meanwhile, the TL condition showed the highest sugar consumption (57% consumption), which indicates that the metabolic conditions were more suitable for cellular growth. The bubble conditions showed values that were very close to each other, with 31% consumption for BD and 23% consumption for BL. The sugar consumption behavior between light and darkness conditions was very similar, with tangential agitation being the most efficient, followed by bubbling, and finally static agitation. In general, the interactions that were most consistent were those with bubbling, although there was no significant difference when comparing its condition in light and darkness, the most efficient being BL at 7.78%, compared to BD.

When comparing microorganism growth by taking the maximum biomass and the biomass at the start of the exponential phase, it was found that the TL treatment had an increase of 402%, the largest increase of the four treatments, followed by the BL condition with 249%. The least efficient systems were the SD (173%) and BD (138%) conditions.

3.6. Kinetic Parameter Estimation

Kinetic production parameters for KA were estimated (

Table 4. Kinetic parameters involved in the production of kojic acid in liquid medium fermentation with the A. oryzae strain, under different agitation and light conditions.

	µmax (h−1)	YP/X (g KA/g X)	qP (g KA/g X·h)
TL	0.007	0.79	0.005
BD	0.019	7.59	0.147
BL	0.029	4.78	0.138
SD	0.121	0.85	0.103

where µ_max—maximum growth velocity, Y_P/X—product-to-biomass yield, q_P—product formation rate, KA—kojic acid, X—biomass.

), as reported by Quiterio et al., [20] for the significant treatments. The treatment with the highest growth rate was the SD condition, followed by the BL and BD conditions. Regarding the best metabolite production yield per biomass, the BD condition was the best, followed by BL. The condition with the best product formation rate was BD.

4. Discussion

4.1. Strain Cultivation and Genetic Potencial

Fungi from the Aspergillus genus are among the most important for protein production, the regulation of cellular processes, and secondary metabolite production, with the latter being where they have the greatest diversity of compounds [24]. A very interesting aspect of the Aspergillus oryzae strain is that it has a larger genetic sequence, about 20% larger compared to other species such as A. nidulans and A. fumigatus [25], which suggests that it could produce even more proteins or be modified for a greater KA production capacity [26].

4.2. Effect of Agitation on Fermentation Efficiency

KA production is influenced under different growth conditions, such as agitation and aeration, so modifying the type of agitation or the media used for fermentation could cause a change in the production behavior [20]. Tangential agitation is mechanical agitation, meaning it is always associated with mechanical and thermal stress processes [27], which influence biological production. This type of situation significantly affects filamentous fungi like Aspergillus oryzae in their cellular morphology [28]. In reactors, the processes of agitation and aeration determine the mechanical stress effects; these factors directly affect biomass behavior and consequently KA production [29]. This may explain why bubble agitation produces more KA, as both the aeration and agitation processes are controlled under this system, which generates less mechanical stress compared to tangential agitation, which only controls agitation.

The agitation speed used in this study was selected based on values reported in the literature to ensure fungal growth and kojic acid production without causing excessive shear stress [20]. Lower agitation speeds have been reported to hinder kojic acid production, while higher speeds may induce shear stress and negatively affect the process [30]. Therefore, a speed of 125 rpm was chosen for all treatments involving tangential agitation.

4.3. Influence of Light on Metabolite Biosynthesis

Kojic acid (KA) production under dark conditions could be diminished due to the absence of light, as exposure to light, especially in the UV spectrum, generates reactive oxygen species (ROS). These ROS can induce oxidative stress in the microorganism, leading to a redistribution of cellular energy towards protecting against oxidative damage, rather than the production of secondary metabolites such as KA. This oxidative stress, associated with UV light, can directly inhibit KA synthesis by prioritizing cellular repair over metabolite production [31].

However, in the visible light (BL) treatment, the higher KA production suggests that light, far from causing damage, could have positive regulatory effects. Visible light could activate genes such as LeaA, which is known to regulate secondary metabolite biosynthesis in Aspergillus oryzae [32]. This type of genetic activation not only increases KA production but also indicates that ROS generated by visible light may act as metabolic signals rather than destructive agents. This signaling mechanism is important because the small amounts of ROS generated by visible light may induce biosynthetic pathways for metabolites that favor KA production without causing excessive cellular damage.

This behavior suggests that while dark conditions may limit KA production by reducing the activation of important metabolic pathways, light, especially visible light, seems to have a positive effect by properly regulating the biochemical pathways involved in secondary metabolite biosynthesis. In conclusion, visible light not only promotes growth and biomass accumulation but also plays a key role in optimizing KA production by appropriately regulating the involved pathways.

4.4. Carbon Source Utilization and Biomass Formation

The preferred carbon source for microorganisms is glucose, as it is the easiest form to integrate into metabolic and energy processes, which translates into greater growth and secondary metabolite production [33]. This justifies the reduction of glucose in the culture medium over time, as it is used for the production of secondary metabolites. The SD treatment showed the lowest reducing sugar consumption, which also coincides with the second-highest amount of KA produced and the lowest biomass. This could be because the fungus experienced stress conditions due to insufficient oxygen and contact with nutrients in the medium [34]. On the other hand, the BL treatment exhibited higher efficiency in sugar consumption, with more cells in contact with the carbon source and dissolved oxygen in the liquid medium, leading to a higher potential for KA production [35]. The treatments that stood out most in biomass production were those with light, which also had the highest reducing sugar consumption. This suggests that the fungus was under favorable agitation conditions for proper biomass growth, leading to higher sugar consumption and the generation of a good base number of cells for metabolites [24,36]. With favorable growth conditions, the microorganism did not need to produce large amounts of KA, as secondary metabolites are typically produced under stress conditions for the fungus [4]. Both agitation treatments greatly surpassed the static control, which may be because the agitation allowed the fungus to come into better contact with the nutrients in the medium [13].

4.5. Kinetic Parameters and Treatment Comparison

The maximum growth rate (µ_max) observed in the SD treatment was higher than in other treatments, suggesting that the static agitation conditions may have favored a more balanced growth of Aspergillus oryzae. However, despite higher growth, the efficiency in KA production was greater in the agitation and light treatments, as reflected by the product yield over biomass (Y_P/X) and the product formation rate (q_P). These kinetic parameters indicate that increased agitation and the presence of light not only promote growth but also optimize the production of the metabolite of interest [37].

5. Conclusions

This study demonstrates that agitation conditions are of vital importance for the optimal growth of Aspergillus oryzae and the production of kojic acid, as observed in the bubbling agitation treatment under light conditions after 144 h, which resulted in the highest production of the kojic acid biomolecule, reaching 7.86 ± 2.21 g/L. The results from the different research stages, such as strain cultivation, agitation evaluation, KA quantification, and kinetic analysis, show that a low level of mechanical stress combined with adequate aeration influences kojic acid biosynthesis, which could be easily scaled up to larger volumes in industrial fermentation processes. These results provide a solid foundation for the production of this metabolite; however, future research is needed to understand the exact metabolic and genetic mechanisms of how agitation and aeration influence the biosynthesis of kojic acid.