Isolation and identification of ACP-producing bacteria

The program for isolation methods utilized sewage samples from the paper and pulp mills in Alexandria, and 20 distinct morphotypes of isolates were obtained. The ACP-producing bacteria with organic acid-producing potentialities were identified and picked up according to the halo zone development on PKV agar medium and the production of a pink-coloured product in culture broth upon cleavage of an artificial chromogenic substrate as shown in Fig. 2a. On PKV's plates, the isolate entitled ACP2 demonstrated the furthermost apparent clusters with a noticeable clear zone, as well as the highest obviousness of purple colour intensity in PDP-LB culture broth, and the most increased ACP activity of 39 U L−1 min−1 amongst the chosen isolates shown in Fig. 2a. Based on this data, it is reasonable to conclude that the most potent chosen isolate has varied organic acids producing efficacy. As a result, the ACP2 isolate was selected for further investigation. Prior to sequencing, the 16S rRNA gene was extracted from genomic DNA and purified. The 16S rRNA array of ACP2 isolates yielded a nucleotide sequence of 1334 bp, which was determined and then submitted to a BLAST search in the GenBank database. ACP2 was shown to have 100% sequence identities to B. sonorensis, B. licheniformis, B. haynesii, and B. paralicheniformis, indicating that it was genetically linked to members of the Bacillus genus. In addition, the strain's total and maximum score of 2464 was recorded, and the analysis showed that it had 100% query coverage for all related species. Accession number MZ723116 has been allocated to the strain by GenBank.

Fig. 2

Isolation, screening, and identification of PSB and ACP-producing bacteria. a Isolation and qualitative screening of CaCO3-solubilizing and ACP-producing bacteria. b A phylogenetic tree based on 16S rDNA gene sequence analysis shows the relationship of B. sonorensis strain ACP2 with reference strains (NCBI GenBank) constructed using the neighbor-joining method with the aid of the MEGA X program. c Gram-stain of the B. sonorensis strain ACP2 (using magnification, oil lens 100 x). d SEM micrograph of B. sonorensis strain ACP2 showing cell morphology at a magnification of 5000 and 10,000 × with 15 kv. e Cultural feature of B. sonorensis strain ACP2 agar plate

Further confirmation was provided by the neighbour-joining dendrogram tree, which revealed that the strain is phylogenetically in the clade of the genus Bacillus and formed distinct groups with other species of the genus Bacillus, as shown in Fig. 2b. Phylogenetic analysis based on 16S rRNA gene sequence analysis indicated that the ACP2 strain is identified in a clade with B. sonorensis NRRL B-23154 T; this phyletic track, jointly with B. licheniformis BCRC 11702, was constantly detected in the same clade as B. sonorensis strain. Despite having a low bootstrap value (47%), the ACP2 strain was shown to cluster always with B. haynesii NRRL B-41327, as determined by various treeing approaches, and B. swezeyi NRRLB-41294 formed a distinct clade with it. Based on taxonomic criteria, the strain ACP2 was given the suggested name B. sonorensis strain ACP2 since it closely resembled B. sonorensis. During the microscopical description of strain ACP2, spore-forming bacteria and rod-shaped bacteria measuring within 1.2 to 1.6 µm in height and 0.73 to 0.86 µm in diameter were found (Fig. 2c and d). Cells with movable and catalase-positive characteristics are often found alone, while they can occasionally be seen in chains of two to four cells. Amorphous and mucous slime is formed in the form of mounds or lobes on LB agar by colonies that are white, irregularly shaped, and have a rough, wrinkled-like labyrinth pattern, textured surface with ridges and furrows as illustrated in Fig. 2e.

Conventional physical parameter optimization for ACP synthesis

According to the results of this investigation, the B. sonorensis strain ACP2 produced the highest amount of ACP (39.14 U L−1) when grown at 45ºC. Transport of substances across the cells is inhibited at temperatures below the optimum (37ºC), and a smaller amount of enzymes is obtained (34.4 U L−1). Additionally, the activated pre-cultures were kept at 5% (v/v) inoculum size to get the highest ACP output (41.8 U L−1); ACP throughput was felled down to 38.8 U L−1 when 10% of the inoculum volume was utilized. Moreover, the ACP output trend steadily increases from pH 3 (36.35 U L−1) to pH 7 (43.11 U L−1) before decreasing to 35.9 U L−1 at pH 8; it seems that the isolated bacterium is mostly neutrophilic and requires an ideal pH of 7.0 for growth and ACP productivity. When it comes to maximum ACP production (46.9 U L−1), however, compared to the adjusted pH medium with 1N HCl, it appears that the unchanged pH medium (approximately pH 6.7–6.9) exhibits the best and optimal circumstances (43.11 U L−1). As a result, a pH-unadjusted medium was employed in all subsequent experiments conducted as part of this work.

Statistical tuning of B. sonorensis strain ACP2's ACP synthesis

Using the PBD, an established experimental design matrix of eighteen trials was created to prescreen the impact of eleven distinct medium ingredients, namely glucose, sodium glutamate, NH4NO3, NH4Cl, eggshell powder, KCl, MgSO4•7H2O, NaH2PO4, ammonium molybdate, CrCl3•6H2O, FeSO4•7H2O, corresponding to X1–X11, at their bottommost and uppermost factor points on ACP production. Table 1 exhibits that ACP's productivity varies widely across the design array, underscoring the necessity of medium improvement in boosting ACP efficacy. Both glucose (X1) and NaH2PO4 (X8) are required for the induction and synthesis of ACP; the fact that trials number 6 and 16 had the highest ACP activity (~ 51.4–55.8 U L−1) and had high amounts of both nutritive media (10 and 0.2 g L−1, respectively) shows this as presented in Table 1. Additionally, as a result of the use of the lowest concentrations of glucose and NaH2PO4 (1 and 0.02 g L−1, respectively), the ACP throughput decreased by ~ 30.2–30.7 U L−1 as seen in trial numbers 14 and 15, respectively. This finding, in turn, highlighted and emphasized the value of glucose and NaH2PO4 in strengthening ACP production.

Results of the mathematical multiple regression study of PBD

The obtained coefficients and the t- and p-values are provided in Table 2. It is effective for identifying substantial main effects but not significant interactions between factors due to the design's peculiarities. Table 2 and Fig. 3a depict the significant impacts of all independent variables on ACP production. As a strategy for developing optimal settings for ACP output, it was appropriate to consider the sign (+ or -) and the degree of the main effect. The results demonstrated that the glucose followed by MgSO4•7H2O, NaH2PO4, and CrCl3•6H2O demonstrated a noteworthy increase in ACP output, suggesting that the highest concentration of these parameters is needed for optimal production of ACP. Sodium glutamate, ammonium molybdate, KCl, and NH4Cl2 significantly affected ACP synthesis, whereas FeSO4•7H2O had a negligible influence. However, the eggshell powder should be kept to a minimum for enhanced ACP output. The ANOVA findings also support this since the p-value of 1.88 E−05 and t-value of 12.16 indicates that glucose (X1) substantially influences the production process (a contribution percentage of 29.6); this is evident in Table 2. MgSO4•7H2O (X7) and NaH2PO4 (X8) both exhibited confidence levels of 99.88 and 99.79 percent, respectively; they also had a considerable influence on ACP production with a t-value of 5.82, 5.17, a p-value of 0.0011, 0.0020, and an involvement percentage of 14.17 and 12.59; respectively. On the other hand, CrCl3•6H2O was statistically significant at 92.99 percent with a p-value of 0.07, t-value of 2.2, and contribution percent of 5.35; it also substantially influenced ACP production. There was no noticeable effect on ACP yield from the other coefficient components included in this model.

Table 2 Statistical analysis of PBD showing coefficient values, t- and p-values for each variable affecting ACP productionFig. 3

PBD results: a Main effect of culture variables. b Pareto chart illustrating the order and significance of the variables affecting ACP production by B. sonorensis strain ACP2. c The first-order polynomial equation determines the standard probability plot of the residuals for ACP production. d Correlation between the residual and observation order. e Correlation between the residual and predicted values(f) Box-Cox plot

Figure 3b indicates that effects above the Bonferroni limit are likely to be significant, effects above the t-value limits are possibly significant (and should be evaluated if they haven't already been chosen), and the Pareto diagram indicates that effects below the t-value limits are unlikely to be significant. The glucose (X1), MgSO4•7H2O (X7), and NaH2PO4 (X8) effects cross the Bonferroni limit; that is, it showed a significant influence on the ACP production. Figure 3c depicts the typical plot of the standardized impact of adequate nutrients, representing the greatness and guidance of their powerful impacts. It was noticed that near the center (zero) line, most effects cluster around the fitted standard model straight line [22]. The residuals are shown against the experimental run order, as illustrated in Fig. 3d. Furthermore, the constant variance assumption may be confirmed since the residuals in Fig. 3e are oriented against the expected response. The experimental runs' points were all randomly given. All values fell between 3 and -3, indicating that the models suggested by the PBD were appropriate and that the constant variance assumptions were reasonable. Figure 3f shows the smallest values and lambda, representing the 95 percent confidence interval. The findings in Table 2 demonstrate that the present model can account for almost 90% of the response variation. It was discovered that the correlation coefficient (R2) was 97.89 percent. A 95 percent confidence level was achieved for each regression model, and the F-value was found to have a very high value (24.2) with a low probability (0.0004), indicating that the regression model is statistically significant. This means that 97.89 percent of the experimental data were consistent with the model, with only 2.11 percent of the variation not explained by the model. It also reveals a strong relationship between model importance, the investigated variables, and ACP production, given the high adjusted coefficient of determination value (Adj. R2 = 0.9625).

Regression equation

According to the results of the ANOVA, the first-order model describing the relationship between the eleven factors evaluated over 18 tryouts and the ACP yield may be stated as the following equation:

$$_= 38.38+6.07_-2.019_+0.630_-0.856_-0.517_-1.405_+2.908_+2.585_-2.391_+1.098_-0.032_$$

The Plackett–Burman design's findings were confirmed by conducting a validation experiment. At 45°C and 200 rpm, the ideal culture conditions for the production of ACP were estimated to be (g L−1): glucose, 10; NH4NO3, 0.5; eggshell powder, 0.2; MgSO4•7H2O, 0.2; NaH2PO4, 0.2; CrCl3•6H2O, 0.0025; FeSO4•7H2O, 0.0015, and 5% activated inoculum volume for 24 h incubation time. The maximal ACP activity was 72.4 U L−1, more significant than the activity obtained before the PBD was employed (43.11 U L−1) by 1.67 fold.

Response surface methodology (RSM)

The glucose, MgSO4•7H2O, NaH2PO4, and CrCl3•6H2O dimensionless coded variables (with confidence levels of 99.99, 96.88, 99.79, and 92.9%, respectively) investigated via the preceding PBD were subjected to further examination to assess their interaction and the modelling process, the RSM model was constructed using an empirical half-factor OCCD. Thirty-six experiments were done utilizing a precision random array of eight axial, sixteen factorials, and twelve midpoints by the OCCD. For the selected variables, consideration was given to parameter symbols, values, the layout of the array, and actual and anticipated outcomes (all displayed in Table 3). It was shown that the efficiency of ACP activity varied significantly based on the four parameters described above. It is shown in Table 3 that at the axial level of the NaH2PO4, with the minimal concentration of 0.15 g L−1 in experimental trials (number 22), 204.44 U L−1 of ACP was produced (predicted to be 188.71). Alternatively, run 31 produced the most negligible ACP activity of 128 U L−1 when the other predictors were kept at zero, except glucose, which was supplied at its minimal axial level (-2) at an amount of 8 g L−1. An important finding from this study shows that glucose was needed to stimulate ACP synthesis by the B. sonorensis strain ACP2.

Table 3 Matrix designed for B. sonorensis strain ACP2 OCCDANOVA and multiple regression analysis

The analysis and interpretation of the CCD experimental data results were done using multiple regression statistical analysis and ANOVA calculations, which are essential techniques for assessing the significance and practicality of the quadratic regression model (Table 4). Only 8.2% of the total difference generated by variables could not be described by this model, nor could ACP activities be explained, according to the ANOVA, which indicated a determination coefficient R2 of 0.912, meaning that the model equation could explain 91.2 percent of the whole variance in the data. They were using an adj. R2 of 0.854, the model's relevance and precision, and the little coefficient of change (CV = 4.77 percent) of a tryout's data can be shown. According to the model, a signal-to-noise ratio of 11.43 indicates a sufficient signal to ensure the model can move through the design space. According to Fisher's F-test of 15.73 and 4.48E−08, the model is entirely meaningful with the slightest standard deviations (7.99), as demonstrated by the mean unbiased residuals to mean square regression ratio. It is calculated that the ACP productivity model has a residual error number of squares (PRESS) value of 7720. As a result of the results presented in Table 4, the positive coefficients for the linear effects of the variables X1, X2, and X4, as well as the positive coefficients for the mutual effects of the variables X1X2, X1X3, X1X4, X2X4, and X3X4 show that these variables have synergistic effects that increase the production of the ACP. This is supported by the data on their F-values, p-values, t-values, and contribution percent. However, the antagonistic impact was seen in the linear (X3) and mutual interaction (X2X3), as well as the quadratic effects (X12, X22, X32, and X42) of the variables under investigation. It was determined that they had made no substantial contribution to boosting ACP production by B. sonorensis strain ACP2, based on the negative sign of their coefficients and computational indications for their significance degrees. Regression coefficients were used to fit an equation with a second-order polynomial (Table 4). ACP production (Y) by B. sonorensis strain ACP2 may be stated as a regression equation as follows:

Table 4 ANOVA for the response surface of ACP production by B. sonorensis strain ACP2 obtained by OCCD. “Std. Dev. is the standard deviation, the coefficient of determination (R2), Adj R2 is the adjusted-R2, and PRESS is the prediction error sum of squares, C.V is the Coefficient of variation”

$$_=190.45+4.095_+6.979_-5.549_+3.23^_+4.616__+2.290__+2.508__-0.799__+0.799__+4.289__-12.69_^-7.68_^-3.21_^-10.51_^$$

In this case, the independent variables glucose, MgSO4.7H2O, NaH2PO4, and CrCl3.6H2O are coded at values X1, X2, X3, and X4, respectively, and Y represents the expected response (ACP activity).

Model suitability assessment

A typical linear distribution for the residuals is shown by the compact grouping of data points along the straight line, as seen in Fig. 4a. As illustrated in Fig. 4b; the residuals were drawn versus their anticipated response, which confirmed the assumption of constant variance. Furthermore, the uniform distribution of the data points across a 45-degree line is shown in Fig. 4c. All the data points may be obtained, showing the model's validity. Figure 4d provides a better understanding of the Box-Cox graph. The green line represented the best λ-value (2.05), the blue line represented the transformation (λ = 1), and the lines in red represented the confidence intervals' lowest and most significant values (-0.04 and 4.24, respectively). Consequently, these model diagnostic charts showed that the fitted model for the response met its assumptions and that, on the whole, the model matched the data well.

Fig. 4

Model adequacy checking of OCCD: a Normal probability plot of the residuals. b Externally studentized residuals versus predicted ACP production. c Plot of predicted versus actual ACP production. d Box-Cox plot. e The optimization plot displays the desirability function and the optimum predicted values for the maximum ACP production

Optimization using the desirability function (DF)

Experimental design aims to attain the conditions that will provide the best results by predicting the optimum ones. The desirability function and an optimization plot with the most significant predicted values for the ideal production of ACP are shown in Fig. 4e. At 45°C and 200 rpm for 24 h, the ACP2 strain of B. sonorensis reported the maximum foreseen value of ACP (195.45 U L−1) in the existence of g L−1: glucose (25.24), NH4NO3 (0.5), eggshell powder (2), MgSO4•7H2O (0.637), NaH2PO4 (0.455), CrCl3•6H2O (0.0056) and FeSO4•7H2O (0.0015) without pH adjustment. The experimental findings and predicted values were fully promising, indicating that the DF could accurately compute the ideal anticipated conditions for ACP synthesis by B. sonorensis with an accuracy of almost 99%.

Three-dimensional (3D) plots and contours

The three-dimensional plots have established two independent factors of the four variables (glucose, MgSO4•7H2O, NaH2PO4, and CrCl3•6H2O) and ACP activity (U L−1). Figure 5 illustrates this by charting the ACP activity on the Z-axis versus each of them while keeping all other variables at zero. It was feasible to show how glucose and MgSO4.7H2O affect ACP synthesis simultaneously using a 3D surface map (Fig. 5a). ACP activity was at its greatest near of the glucose center point. In contrast, outside of this region, ACP production was insignificant. MgSO4•7H2O boosted ACP activity to a maximum near the MgSO4•7H2O center points, although a greater level of MgSO4•7H2O supporting low ACP activity was seen. 192.84 U L−1 of predicted ACP activity was achieved at the optimum projected glucose (26 g L−1) and MgSO4•7H2O (0.6357g L−1) at zero level of 0.75 and 0.006g L−1 for NaH2PO4, and CrCl3•6H2O, respectively. A comparable trend in ACP effectiveness was observed regarding the other paired arrangement of the factors under study.

Fig. 5

3D response surface representing ACP activity yield (U L−1 min−.1) from B. sonorensis strain ACP2 as affected by culture conditions: a Interaction between MgSO4.7H2O and glucose, b Interaction between CrCl3.6H2O and glucose, c Interaction between NaH2PO4 and glucose, d Interaction between NaH2PO4 and MgSO4.7H2O, e Interaction between CrCl3.6H2O and MgSO4.7H2O, and f Interaction between CrCl3.6H2O and NaH2PO4

Furthermore, as shown in Fig. 5b, lower and higher glucose (X1) and CrCl3•6H2O (X4) concentrations are linked to decreased ACP activity. There is a clear correlation between the highest ACP activity (190.79 U L−1) and both glucose (X1) and CrCl3•6H2O's (X3) central point. This finding highlights the importance of glucose (X1) and CrCl3•6H2O (X3) in producing ACP. According to Fig. 5c, increasing NaH2PO4 (X3) to 0.499 g L−1 and glucose (X1) to nearly its center point level (24.688 g L−1) caused ACP production to peak (192.94 U L−1), demonstrating how closely the process of synthesis depends on the starting concentrations of glucose and NaH2PO4, with the two other variables maintained at zero values. 3D and contour plots (Fig. 5d) showed that MgSO4•7H2O (X2) and NaH2PO4 (X3) affected ACP production at 24 g L−1 of zero-level glucose (X1) concentration and 0.006 g L−1 of zero-level CrCl3•6H2O (X4). When NaH2PO4 (X3) concentrations were dropped to near their axial point (0.471 g L−1), followed by a rise in MgSO4•7H2O (X2) concentrations to their optimum (0.63 g L−1), ACP activity steadily increased; after that, ACP output dropped. The maximum ACP throughput (194.78 U L−1) was supported at the center level of MgSO4•7H2O (X2), crossing the ideal points of its concentration; any change therein would result in a loss of productivity. Figure 5e shows that increasing MgSO4•7H2O (X2) concentration slightly beyond its center point (0.621 g L−1) boosted ACP production (192.048182 U L−1) when CrCl3•6H2O (X4) was present at or near its mid-point (0.006 g L−1) while maintaining glucose (X1) and NaH2PO4 (X3) at their zero levels. Conversely, rising both concentrations over their recorded points led to a decline in ACP throughput.

On the other hand, it was noticed that when using glucose (X1) and MgSO4•7H2O (X2) held at their zero levels (Fig. 5f), the ACP production efficiencies rose linearly with increasing CrCl3•6H2O (X4) concentration, accomplishing the pinnacle of efficiency (193.23 U L−1) at the CrCl3•6H2O (X4) center point (0.0059 g L−1). Furthermore, when the concentration of CrCl3•6H2O continues to rise, the production of ACP decreases stepwise. However, the lowest level of NaH2PO4 (X3) (0.449 g L−1) was found near its axial point, which helped to promote the production process. Despite this, Fig. 5f plot analysis shows that the above and below the optimum recorded point for NaH2PO4 (X3) concentrations yielded little ACP production. The findings showed that none of the factors had a substantial relationship with each other or with boosting the production of ACP overall, as indicated by the three-dimensional (3D) contour plots.

Scale-up fermentation approaches for enhancing ACP production

For ACP's industrial-scale commercialization, an engineering perspective and a scaling-up approach were both established as part of the effort to increase scientific understanding.

Shake-flask batch conditions: cell growth kinetics and ACP production

To ascertain whether there is a relationship between the rate of ACP output and a culture's specific growth, the production of extracellular ACP was monitored during the growth of B. sonorensis strain ACP2 on a boosted medium in a shake flask under ideal cultivation settings. Table 5 demonstrates the effects of various cultivation approaches on the B. sonorensis strain ACP2's proliferation dynamics and ACP synthesis properties.

Table 5 Kinetic parameters of cell growth and ACP production by B. sonorensis strain ACP2 as affected by different cultivation strategies. Xmax., maximal cell dry weight; \(\frac\), cell growth rate; µ, specific growth rate; Pmax, maximal ACP production; Pmax specific, specific productivity; \(_\), ACP production rate; \(_\), substrate consumption rate; \(_\frac\), U g−1 of ACP produced per g biomass; \(_\frac\) U g−1 of ACP produced per g substrate consumed; \(_\frac\) g g−1, of biomass produced per g substrate consumed

Figure 6a shows that the ACP2 strain of B. sonorensis appropriately developed, and ACP was formed simultaneously with cell growth. Cells grew exponentially subsequent to a lag time, with a growth rate of 0.315 (g L−1 h−1) and a specific growth rate (µ) of 0.098 h−1. Biomass production (5.98 g L−1) reached its zenith at 22 h of the cultivation time with a noticeable yield coefficient Yx/s (0.19 g g−1), propelling cell growth to the stationary phase. It was found that the expression of the genes encoding acid phosphatase was progressive from the commencement of the cultivation to its apex (194.26 U L−1), at which point the throughput rate (Qp) was 5.023 U L−1 h−1. This elevation occurred late in the stationary phase (28 h). The cells have more time to synthesize ACP early in the culture phase since they grow more slowly. It was found that the ACP's yield coefficients Yp/s and ACP’s specific productivity Pmax were (4.89 and 134.21 U g−1, respectively). The protein concentration trends also showed these findings; it crested (1.44 g L−1) at the end of the B. sonorensis strain ACP2 stationary phase, while ACP production peaked (at 28 h). Since then, ACP activity and protein quantity have declined over time. With a mean consumption rate of 0.678 g L−1 h−1, the glucose concentration dropped from its beginning level of 18.82 g L−1 at the beginning of the time of cultivation to barely 0.019 g L−1 at the end. This decline was caused by the proliferation of cells and ACP's critical role in the transportation and utilization of phosphate. In the meantime, the quantity of phosphate dropped sharply from the beginning level of 0.315 g L–1 to 0.008 g L–1 at the end of the time of cultivation due to ACP's critical role in the transportation and utilization of phosphate, suggesting the vital function of phosphate for the proliferation of bacteria and the process of ACP output.

Fig. 6

Monitoring of B. sonorensis strain ACP2 growth and ACP productivity in (a) A shake-flask scale cultivation condition. b A 7 L stirred-tank bioreactor under uncontrolled pH conditions. c Online data (DO, agitation, aeration, and pH) as a function of time during batch fermentation in the bioreactor under uncontrolled pH conditions

A drop in pH was seen in Fig. 6a due to the CaCO3 solubilization process, which was associated with producing organic acids, resulting in a gradual decline in the pH of B. sonorensis strain ACP2 cultural medium from 6.45 to 5.85 during the first 16 h of cultivation, followed by a gradual rise back to pH 7.36 at the end of the cultivation. Incremental bacterial growth, ACP throughput, and protein quantity increases coincided with the pH change. This result was caused by how pH affects the development of bacteria or how pH regulates the expression of genes that produce enzymes, as explained by Qureshi et al. [23].

Heavy metals survey

AAS was used to examine and identify heavy metals in the eggshell residues from the shake-flask cultivation. Leftover eggshell samples from the shake-flask cultivation process showed that excessive amounts of K+, Fe+, Cd2+, Cr+, Zn+, Mn2+, Sn+, Ag+, and Cu2+ were solubilized and accumulated during the fermentation process, resulting in an increase in their concentrations by a factor of 4.437, 2.162, 1.157, 11.367, 1.765, 2.383, 89.166, 150.65, and 2.806; respectively, when compared to their early amounts beforehand fermentation at zero time as cited in Table 6. The findings indicate that during the B. sonorensis strain ACP2's solubilization process, the metals Ag+, Sn+, and Cr+ were most effectively liberated from eggshells. The growth of B. sonorensis strain ACP2, on the other hand, was associated with a drop in concentrations of cobalt, nickel, magnesium, and lithium as well as calcium and sodium during the fermentation process, by factors of 89.8, 1.82, 1.72, 1.747, 1.89 and 1.91; respectively compared to those noticed in an eggshell leftover sample at zero time incubation. This result emphasizes the critical role that these metal ions play in the proliferation and ACP synthesis.

Table 6 Flame AAS for heavy metals analysis of eggshells powder leftover samples during shake-flask cultivation system at zero incubation time and after 32 h of the cultivation processCell growth kinetics and ACP production in the bioreactor under uncontrolled pH batch conditions

B. sonorensis strain ACP2 was cultured in a bench-top 7 L stirred-tank fermenter with uncontrolled pH to facilitate further optimization and boost ACP production. It was evident from Fig. 6b that every pattern noticed in this investigation was quite comparable to the patterns established in its shake-flask equivalent. The top production rate Qp (7.888 U L−1 h−1) and maximal ACP production, with the ACP specific productivity (182.98 U g−1), were detected as 216.37 U L−1 over the beginning of the stationary period (18 h) of B. sonorensis strain ACP2 growth after that, the production curve began to flatten down. The findings obtained were 1.11 times greater in production capability, 1.36 times higher in specific productivity, and 1.57 times faster in production rate than those published for the shake flask model. These results were followed by significant increases of 1.32 and 1.04 over those observed in shake flask mode in the ACP yield coefficients Yp/x and Yp/s, which were 18.2 and 5.12 U g −1, respectively.

Furthermore, there was a tendency for the protein content to increase along with the increase in ACP synthesis gradually; the protein concentration peaked at 1.47 g L−1 and then steadily increased until 28 h later. B. sonorensis strain ACP2 growth patterns in a shaking flask and a stirred-reservoir fermenter are strikingly similar. At 16 h of incubation, or roughly 6.0 h earlier than in the shake-flask, the most significant cell yield of biomass (Xmax; 6.016 g L−1) with a rate of proliferation of 0.374 g L−1 h−1 and a specific growth rate (µ) of 0.1 h−1 occurred. After surpassing its apex point, the stationary stage started at 18 h of incubation. According to the results, the obtained output coefficient Yx/s of 0.27 g g−1 was marginally better than that from the shaking flask by a factor of 0.08. The amount of glucose in the culture media, tracked during the whole cultivation timing, was inversely correlated with ACP2 cell growth and ACP production. Cell growth and ACP production rose 1.66 times compared to shake-flask culture, resulting in a consumption rate (Qs) of 1.13 g L–1 h–1.

By the end of the growth phase at 28 h, the amount of glucose had been entirely consumed, in contrast to the starting glucose concentration of 19.65 g L−1. By the time the shake flask culture system reached the end of its cultivation cycle, the glucose amount had consumed 0.019 g L−1. Additionally, as shown in Fig. 6b, ACP2 cell growth and ACP production were accompanied by the intake of organic and inorganic phosphate media components. This finding resulted in a sharp decline in the level of soluble phosphate at the end of the growing duration, from the starting level of 0.409 to 0.0317 g L–1. The culture medium's pH pattern matched that of the corresponding shaking flask. While in the early log phase, pH values decreased from 6.94 to 5.68 after 4.0 h of incubation, then increased to 7.2 after 7.0 h, then dropped to 5.84 once more after 18 h, and finally stayed steady with some fluctuations until the completion of the cultivation, with pH values in the range of 6.0–6.34. It is believed that the unregulated pH culture condition contributed to improved ACP production. When the B. sonorensis strain ACP2 grew and increased in biomass rapidly during the early stages of fermentation, the resulting rapid fall in dissolved oxygen (DO) content was observed.

Consequently, the amount of DO in the growth medium was evaluated during this study's cultivation period. As the culture progresses into the stationary phase, the air supply may be curtailed, and the pH of the medium may be lowered, both of which may cause stress to the bacterial cell. As illustrated in Fig. 6c, when the DO concentration dropped to 0.9%, the ACP production began to increase gradually, reaching its peak at 18 h of cultivation time when the DO level was 6%; once this point was passed, the ACP production began to decline, and the DO concentration increased to 24% at the end of the cultivation time. A preliminary study found that phosphatase production increased when an organism was moved to anaerobiosis, which is compatible with the results of this study. The PhoP/PhoQ sensor/regulator system seems to have a role in promoting acid phosphatase production in the presence of stress, as discovered in an early study [24].

Cell growth dynamics and the synthesis of ACP in the bioreactor with regulated pH batch settings

Furthermore, to get a deeper comprehension of the influence of pH on the production process, B. sonorensis strain ACP2 was cultivated in a 7-L bench-top bioreactor with regulated pH settings. The trends of substrate intake, volumetric ACP synthesis, and proliferation of cells that came from the study are shown in Fig. 7a. Similar to other methods of culture; there was no discernible phase lag in the exponential growth of the bacterial cells over time. The cell growth increased gradually over 18 h with a growth rate of 0.396 g L1 h1 and a specific growth rate (µ) of 0.085 h−1, reaching its maximum (Xmax = 6.559 g h L−1) at 18 h. Although the growth duration was comparable to that of uncontrolled pH culture circumstances, this study showed a considerable increase in growth rate over shake-flask cultivation methods. However, the highest biomass production rose by 9.57 and 9%, respectively, over the shake-flask and uncontrolled pH culture approaches. Because of this, an increase in the yield coefficient Yx/s of 0.39 g g−1 was observed, which was significantly more significant than the yield coefficients attained from the shaking flask and uncontrolled pH cultivation modes by two and one-fourth of a factor, respectively. While ACP2 growth was better than under uncontrolled pH cultivation conditions, ACP productivity was 10.0 h behind that of a wild pH system, where ACP volumetric productivity rose steadily throughout the cultivation period with a production rate (Qp) of 1.51, peaking at 64.41 U L–1 at 28 h. Comparing ACP2 cultivation in a shake flask and unadjusted pH conditions, the volumetric yield of ACP descended by 66.84% and 70.22%, respectively, which causes a decrease in the output coefficient Yp/x of the biomass (5.13 U g–1) by 62.63, and 71.81% lower than that obtained through shake-flask and uncontrolled pH systems, respectively. In addition to the preceding, under the above culture condition, a 38.65 and 41.40% reduction in yield coefficient Yp/s of 3 U g−1 was reported compared to earlier cultivation modes, respectively. The protein content trend followed the ACP production pattern and peaked at 1.253 g L−1. At the same time, ACP production peaked at 28 h, allowing for ACP's specific productivity (51.389 U g–1). After this, protein content and ACP activity showed a noticeable gradual decline. It was demonstrated that there was a correlation between substrate consumption and growth as well as ACP production. The levels of glucose and total phosphate went down from their starting levels (19.48 and 0.441 g L−1) to their minimal levels (2.316 and 0.0522 g L−1, respectively) towards the end of the 32-h growing period, with an average glucose consumption rate of 0.4 g L−1 h−1. This decline was because the consumption of nutrients increased in tandem with the proliferation of cells and the synthesis of enzymes. The glucose intake rate showed that, compared to the shake-flask and unregulated pH batch culture approaches, there was a 41 and 64.60% decrease in glucose consumption, respectively. Additionally, as exhibited in Fig. 7b, the percentage of DO decreased dramatically when the bacterial cells approached the exponential phase, reaching 1.6% at 4.0 h of cultivation time due to excessive oxygen consumption. Afterwards, the DO content fluctuated slightly, increasing again, reaching 21% after 15 h of incubation, before decreasing progressively, reaching 5% at the end of the incubation period, which coincided with ACP peak production.

Fig. 7

a Monitoring of B. sonorensis strain ACP2 growth and ACP productivity in a 7 L stirred-tank bioreactor under controlled pH conditions. b Online data (DO, agitation, aeration, and pH) as a function of time during batch fermentation

Morphological structure of the eggshell

Following the completion of fermentation, the residual eggshell powder samples were taken out, weighed, and dried in an oven for a whole night at 60 °C. SEM and EDS analyses were then performed on the collected eggshell powder to investigate the samples' dispersion and surface texture. Throughout the cultivation process, B. sonorensis strain ACP2 solubilized eggshell powder samples, resulting in an alteration in the obtained sample's appearance and metal element composition, as seen in Fig. 8. It can be shown in the SEM micrograph (Fig. 8a) that during zero-time incubation, the eggshell particles had a rough surface with uneven shapes and sizes and a fibre-like network structure encasing CaCO3 particles, which can be seen in the SEM micrograph (100x, 1000x, 2000x, and 5000x). According to Fig. 8c1, the eggshell particle's EDS indicates that its components are calcium (Ca), phosphorus (P), silica (Si), aluminium (Al), magnesia (Mg), oxygen (O), sulfur (S), Chlorine (Cl), potassium (K), and carbon (C). The maxima of absorption associated with Ca (AT% of 45.25 and mass % of 67.88), O (AT% of 34.77 and mass % of 20.82), and C (AT% of 16.64 and mass % of 7.48) are among the most prevalent in the sample. P is associated with the least prominent absorption peak (AT% of 0.14 and mass % of 0.17). Calcite (CaCO3) is the most abundant component in eggshell particles, confirming that calcium carbonate is the most abundant component. Thus, the eggshell hydroxyapatite's Ca: P weight ratio was 323.

Fig. 8

SEM micrograph at a magnification of 100x, 1000, 2000x, and 5000 × for the eggshells leftover of a) zero incubation time; b After 32 h of incubation time. c EDX analysis. d Particle size distribution analysis

On the other side, powdered eggshell morphology changed after 32 h of fermentation time, as shown in Fig. 8b. Bacterial cells were securely attached to the eggshell particles, which had smaller, looser-structured particles with irregular shapes and defined limits that vanished with a softening of the surface edges, as seen at a magnification of 2000 and 5000X, in Fig. 8b. However, the element map Fig.