Process optimization for simultaneous production of phenolic acids and enzymes with high transfructosylation activity in cupuassu (Theobroma grandiflorum) residue by submerged fermentation with Aspergillus carbonarius

Abstract

Cupuassu (Theobroma grandiflorum) generates a large amount of waste, which can be better used to obtain products with high added value through biotechnological processes. Thus, the present study aimed to obtain optimized conditions for the simultaneous production of phenolic acids, invertases and transferases enzymes in cupuassu residue with Aspergillus carbonarius. The main methodologies used to select the variables that influence the system were a Plackett–Burman design, followed by a Central Composite Rotational Design. The optimal conditions were use of 17.3% sucrose, 5.1% residue and 4.6% yeast extract to produce 2204.89 ± 5.75 mg GAE/100 g, 39.84 ± 2.08 U/mL of hydrolytic activity, 168.09 ± 3.81 U/mL of transfructosylation activity and 4.23 ± 0.19 of transfructosylation and hydrolytic activity ratio. Among the phenolic acids identified by the UFLC-DAD system, there was an increase of 148.17% in gallic acid and 205.51% in protocatechuic acid. The antioxidant activities also showed changes after fermentation, with an increase of 350% for the ABTS assay, 51.97% for FRAP, 22.65% for ORAC and 16.03% for DPPH. To the best of our knowledge, this is the first time that cupuassu residue is fermented with Aspergillus carbonarius to obtain invertases and transferases enzymes and phenolic acids.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-022-05418-z.

Introduction

Biotechnology is a highly interdisciplinary field of technology-oriented towards industrial applications of microorganisms for the conversion of waste into high value-added compounds. The number of substances produced by microorganisms has not yet been fully explored but compounds are classified as simple compounds (lower alcohols, acids, etc.) and complex compounds (natural and cellulosic products) or as preliminary products and compounds that are generated from secondary metabolism (Zentou et al. 2019).

Fungi are remarkable microorganisms that produce a wide range of natural extracellular products and are considered beneficial due to their medical, industrial and agricultural importance. These compounds include different types of enzymes (lipases, proteases, amylases, transferases, invertases) and biologically active molecules (phenolic compounds, steroids, terpenes and polyketides) that may have different bioactivities, including anticancer, bifidogenic, antioxidant and immunosuppressive activities (Ahmed and Al-Shamary 2021).

Several fermentation procedures are used for the production of these products and organic chemical compounds from renewable sources (Sheikha & Ray, 2022). However, to achieve high productivity of natural compounds from various substrates, production conditions must be optimized. The traditional method of parameter optimization involves incrementing one parameter at a time (Chergui et al. 2021). However, this procedure, in addition to consuming time, also involves an incomplete understanding of the interaction between various factors which need to be evaluated. Thus, different types of planning are applied to obtain the ideal conditions for cultivation and compound production (Noman et al. 2020).

The Plackett–Burman experimental design is a screening method used to detect potentially significant variables in a complex group of interactions, and thus, it is an important tool to select variables having a wide range of factors with few groups of tests, allowing the proper choice of parameters that influence the process. However, this evaluation does not analyze the interactive effect of each parameter and, hence further optimization is needed (Saleh et al. 2019). Th us, the Response Surface Methodology (RSM) is used as a complementary resource, as it allows evaluating the influence of several variables and the interactions between different physiological and nutritional parameters. RSM can predict untested points and reduce the number of treatments needed in the studied range, being widely used to find a combination of factor levels that produce an optimal response (Khodashenas and Mohammad, 2020). Th erefore, it has become the most popular optimization technique which has been used successfully to model and optimize biochemical and biotechnological processes (Salar et al. 2016).

Several strains of filamentous fungi belonging to the Aspergillus genus (Domain: Eukarya; Kingdom: Fungi; Edge: Ascomycota; Class: Eurotiomycetes; Order: Eurotiales; Family: Trichocomaceae) are producers of invertases, transferases (Cunha et al. 2019; Barros et al. 2020a; Noman et al. 2020) and phenolic compounds (Salar et al. 2016; Gulsunoglu et al. 2020; Dudeja et al. 2021). Many efforts are dedicated to optimize the nutritional and culture parameters of the fermentation processes to increase the enzyme yield or the production of bioactive compounds, especially when linked to the use of agro-industrial residues (Cunha et al. 2019).

Aspergillus carbonarius is a strain belonging to the Nigri sections (Gerin et al. 2021), with genotypic and phenotypic characteristics well determined by molecular methods (Samson et al. 2007; Dachoupakan et al. 2009). This microorganism was reported to be very promising in obtaining invertases and transferases from low-cost carbon sources, such as agro-industrial residues, and so far, few studies have evaluated its use for this purpose (Nascimento et al. 2019).

Cupuassu (Theobroma grandiflorum) is a native specie of the Amazon region, which presents a great economic potential, due to the multiple uses of its pulp by the food, pharmaceutical and cosmetic industries (Costa et al. 2019). However, during the processing of its fruits, a large amount of waste such as husks and seeds is generated (70 tons per harvest), which are generally eliminated, although they are rich in nutrients and antioxidants, that can be used to obtain biologically active products through microbial cultures, such as the Aspergillus genus (Barros et al. 2020a).

The present work is justified by the need to increase the viability and highlight the potential for using little explored agro-industrial residues such as cupuassu together with high potential strains such as Aspergillus carbonarius, and in this context, this study aimed to optimize the cultivation conditions of these biological materials to obtain phenolic compounds and extracellular enzymes with the simultaneous production of hydrolytic and transfructosylation activity. To the best of our knowledge, for first time, cupuassu residue is fermented with Aspergillus carbonarius to obtain invertases and transferases enzymes and phenolic compounds.

Materials and methods

Sample and inoculum

The cupuassu (Theobroma grandflorum) residue was obtained from Pomar Polpas de Frutas, Aracaju, Sergipe, Brazil. The residue was transported in plastic containers maintained at 5 °C to the Laboratory of Flavor and Chromatographic Analysis, Federal University of Sergipe, São Cristóvão, Brazil. Subsequently, the residue was dried in a forced air circulation oven (Marconi, MA 035/5) at 40 °C for 24 h to a constant mass and later the dried part was triturated in a Willey type knife mill (SOLAB SL-31) until it reached a particle size of about 2 mm. The powder was packed in an amber glass vial and stored in a glass desiccator at room temperature (25 ± 2 °C).

The Aspergillus carbonarius strain (IOC 4612) was obtained from the collection of filamentous fungi from the Oswaldo Cruz Foundation (Rio de Janeiro, Brazil). Pre-inoculum development was performed by transferring a spore loop obtained from five-day cultures of strains grown in YM (1% glucose, 0.5% peptone, 0.3% yeast extract, 0.3% malt extract and 2% agar) in 100 mL of sucrose-yeast broth (SYE) (1% sucrose and 0.2% yeast extract, pH 5.5) and incubated at 28 °C on a rotary shaker for 24 h (SOLAB Incubator SL 222) at 200 rpm. At the end of the process, the cells were counted with the aid of a Neubauer chamber and the inoculum was adjusted to a concentration of 4 × 10⁷ cells/mL (Barros et al. 2020a).

Experimental design

Seven independent variables (sucrose, pH, yeast extract, residue, MgSO₄.7H₂O, KH₂PO₄ and NaCl) were investigated to their influence on the enzymatic activity (hydrolytic and transfructosylation) and the levels of phenolic compounds. The matrix was composed of 16 trials, coded in minimum (− 1), medium (0) and maximum (+ 1) values. The ranges (minimum, medium and maximum) of the levels evaluated were based on the studies by Barros et al. (2020a). Table S1 presents the 7 factors evaluated and their corresponding levels, and Table S2 presents the tests carried out with the respective concentrations of the substrates which constitute the culture medium.

Based on the determination of the most influential independent variables of the process on the response variables, it was possible to establish a subsequent experimental design to improve and optimize the production of enzymes and phenolic compounds in relation to the factors that interfered in the process. Thus, a Central Composite Rotational Design (CCRD) elaborated from the significant independent variables selected in the Plackett–Burman design was adopted.

All substrates used in the fermentations to evaluate the experimental designs were added to the residue extracts, previously prepared with distilled water, at 121 °C for 15 min and the pH was adjusted to 5.5. Then, a new sterilization was performed in an autoclave under the same conditions (121 °C for 15 min). 10 mL of 24 h inoculum were transferred to 100 mL of culture medium, which were incubated at 28 °C, 200 rpm for 72 h, in a dark room (Barros et al. 2020a).

Enzymatic activity

The transfructosylation and hydrolytic activities were determined through the methodology of concentration of glucose (G) and reducing sugars (R) proposed by Ganaie et al. (2013) and Miller (1959), respectively. The absorbance of the released glucose was measured at 505 nm and the absorbance of the reducing sugars was measured at 540 nm. The results were calculated through correlation by constructing the standard curve of glucose, varying in concentrations (0.01 mg/L to 1 mg/L).

Fructose (F) and transferred fructose (F_T) concentrations in the reaction medium were computed using Eqs. (1) and (2).

$$\left[F\right]=\left[R\right]-\left[G\right]$$ 1

$$\left[F_T\right]=\left[G\right]-\left[F\right]$$ 2

Transfructosylation activity (At) and hydrolytic activity (Ah) were defined as the amount of enzyme that transfers one µmol of fructose or releases one µmol of fructose, respectively, per min under the chosen experimental conditions.

Production of fructooligosaccharides

The fructooligosaccharides were obtained by the methodology described by Ganaie et al. (2013).

Determination of the contents of phenolic compounds

The total content of phenolic compounds was determined according to the methodology reported by Moo-huchin et al. (2014). To minimize errors caused due to color and turbidity, the protocol of Jouki et al. (2021) was used. An aliquot (50 μL) of the fermented product, previously centrifuged (10,000g for 15 min), was mixed with deionized water (3 mL) and the 1 N phenol Folin-Ciocalteu reagent (250 μL). Then, 20% sodium carbonate (750 µL) was added to the solution and homogenized. The volume of the solution was adjusted to 5 mL with deionized water and the mixture incubated in the dark for 30 min. Absorbance was measured at 765 nm using a spectrophotometer (Molecular Devices, Sunnyvale, CA, USA; SpectraMax M2). The total content of phenolic compounds was expressed in mg of gallic acid equivalents/100 g. The calibration curve (y = 0.9195x + 0.0188, r² = 0.9989) was constructed by analyzing gallic acid (GAE) in the concentration rage of 0 to 10 ppm.

Identification and quantification of phenolic compounds by UFLC-DAD

The chromatographic separation of the fermented product was performed on a Shimadzu HPLC system (LC-20AD) equipped with a pump system (20AT), a degasser (DGU-20A3), an automatic sampler (SIL-20AT) and a diode array detector (SPD-M20A). The chromatograms were evaluated by the 'LC Solution' Software (version 1.24 SP2) from Shimadzu Technologies. Compounds were measured in the range of 190–800 nm using a diode array detector (DAD).

Chromatographic separation was performed as described by Rajan et al. (2019). Quantification was done from calibration curves constructed for each of phenolic acid standards and concentrations which were determined through the areas of the compounds as a function of the calibration curve. The standard concentrations varied from 0.03 to 2 mg/mL. The limits of detection were calculated, taking into consideration a signal-to-noise (S/N) ratio of > 3. The analytes concentration that were above the detection limit, but below the limits of quantification, were designated as in traces (TR). The calibration curves for standard compounds were: A—Gallic acid (y = 1e + 006 x—14,380, r² = 0.9959), B—Protocatechuic acid (y = 7e + 006x—79,785, r² = 0.9987), C—Vanillin (y = 6e + 006x—358,295, r² = 0.9988) and D—Benzoic acid (4e + 006x—1876.5, r² = 0.9987).

Antioxidant activity

ABTS assay

The method was based on as reported by Moo-Huchin et al. (2014). The absorbance was measured in a spectrophotometer (Molecular Devices, Sunnyvale, CA, USA; SpectraMax M2) at 734 nm after 6 min. The calibration curve (y = − 0.0003x + 0.6508, r² = 0.9959) was prepared from the standards varying from 25 to 350 ppm of Trolox and the antioxidant capacity was expressed in μmol equivalents of Trolox (TE)/g.

DPPH assay

The method was developed according to the protocol of Thaipong et al. (2006). The absorbance was measured in a spectrophotometer (Molecular Devices, Sunnyvale, CA, USA; SpectraMax M2) at 515 nm after 30 min. Percent inhibition of the DPPH assay was calculated using the following equation:

$$\text{DPPH}\text{inhibition}(\%)=\left[\left(\text{A}0-\text{A}1\right)/\text{A}0\right]\times 100$$

where A0 is the absorbance of the control and A1 is the absorbance of the sample. The antioxidant capacity was expressed as the percentage of DPPH radical inhibition per mg.

FRAP assay.

The FRAP assay was performed as described by Thaipong et al. (2006) and the fermented product purification as reported by Hashemi et al. (2021). Absorbance was measured at 593 nm (Molecular Devices, Sunnyvale, CA, USA; Specrophotometer SpectraMax M2). The calibration curve for Trolox was prepared from its solutions varying from 20 to 800 mM. Results were expressed in µmol TE/g.

ORAC assay.

The ORAC assay was performed according to the methodology reported by Moo-huchin et al. (2014). The absorbance was accessed according to fluorescence conditions: excitation at 485 nm and emission at 520 nm (Molecular Devices, Sunnyvale, CA, USA; SpectraMax M2). The calibration curve (y = 0.4333x + 0.507, r² = 0.9932) for Trolox was prepared from solutions varying from 0 to 50 mM. Results were expressed in µmol TE/g.

Statistical analysis

Statistical analysis was performed using analysis of variance (ANOVA) by the SAS system (SAS Institute, Cary, NC) version 9.1.3. Significant differences between the mean values were determined using the Tukey´s test at a 95% confidence level (p ≤ 0.05). Data on the production of phenolic compounds and enzymatic activity obtained through the experimental designs Plackett–Burman and CCRD were analyzed using the Statistica™ software (version 13.0, Dell Inc., Canada). All samples were analyzed in triplicate.

Results and discussion

Plackett–Burman experimental design

Table S3 shows the Plackett–Burman experimental design matrix and the response values of the dependent variables such as Total Phenolics (TP), hydrolytic activity (Ah), transfructosylation activity (At) and transfructosylation activity and hydrolytic activity ratio (At/Ah). The yield obtained by the TP variable ranged from 333.90 ± 5.48 mg GAE/100 g to 1320.71 ± 4.35 mg GAE/100 g, the Ah from 10.15 ± 0.72 U/mL to 220.23 ± 16.72 U/mL, the At from 4.76 ± 0.22 U/mL to 220.56 ± 1.02 U/mL and the At/Ah ratio variation was from 0.13 ± 0.01 to 6.22 ± 0.56.

Table 1 presents the data on analysis of variance of the experimental results using the F Test to verify the main effects of the independent variables and identify the factors that significantly affect the process from the F values and their respective p-values. The F test relates the calculated F value to the tabulated F value. If the calculated F value is greater than the tabulated F, the variation of the factor analyzed had a significant effect on the response variable studied in the confidence interval applied (Gupta and Guttman 2017). The F value presented for the residue and yeast extract variables in the total phenolics response is greater than the F-value tabled (5.59). The same is true for the variables sucrose (response Ah—F tabled at 5.59), sucrose and yeast extract (response At—F tabled 3.58) and yeast extract (response At/Ah—F tabled at 3.58). Thus, considering the proposed levels, the experimental data obtained by the Plackett–Burman experimental design allowed the selection of dependent variables conditions as related to the independent variables.

Table 1.

Estimation of the main effects of Plackett–Burman design for total phenolics compounds, hydrolytic activity (Ah), transfructosylation activity (At) and transfructosylation activity and hydrolytic activity ratio (At/Ah) responses

Factors	Phenolics (mg GAE/100 g)		Ah (U/mL)		At (U/mL)		At/Ah
	F test	p-value**	F test	p-value**	F test	p-value*	F test	p-value*
Curvature	8.56299	0.022142	0.009700	0.924305	3.738114	0.094446	3.519461	0.102765
(1) Sucrose	1.63053	0.242354	9.303618	0.018578	4.019131	0.085018	0.873073	0.381209
(2) pH	0.39497	0.549646	5.512838	0.051242	2.132358	0.187596	0.468554	0.515669
(3) Yeast extract	28.69107	0.001057	0.000723	0.979299	5.546694	0.050702	3.725489	0.094901
(4) Residue	39.12949	0.000422	0.017124	0.899569	0.891593	0.376488	0.036572	0.853768
(5) MgSO4.7H2O	1.29533	0.292518	0.378893	0.557671	0.836493	0.390818	0.002647	0.960402
(6) KH2PO4	0.94827	0.362602	0.031389	0.864393	1.330394	0.286594	0.006812	0.936533
(7) NaCl	0.84935	0.387396	1.861953	0.214642	2.303613	0.172864	0.564921	0.476789

^** 95% confidence level; * 90% confidence level

Values in bold were statistically significant (p ≤ 0.05) between samples

The p-value denotes the probability evaluation that an independent variable has no effect on the dependent variables. Thus, low p-values, when related to a specific confidence interval, mean that there is a high probability that a change in the independent variable will produce a significant change in the dependent variable (Gupta and Guttman 2017). The p-value analysis proves that, for significances of 95% (total phenolic variables and Ah) and 90% (variables At and Ah/At), the yeast extract parameters (total phenolics, At and At/Ah), residue (total phenolics) and sucrose (Ah and At) are statistically interfering in the process of obtaining phenolic compounds and enzymes with hydrolytic and transfructosylation activities, as they presented p-values below the analyzed significance levels of 0.05 (95%) and 0.10 (90%).

Figure S1 produces the Pareto diagram, which illustrates the seven parameters evaluated together with their estimated effects. Thus, it was found that the residue and yeast extract had a significant negative effect (coefficient of − 6.25) and positive effect (coefficient of 5.35), respectively, at a confidence level of 95% for the variable total phenolics. For the Ah variable (hydrolytic activity), the sucrose variable showed a significant positive effect (coefficient 3.05) also at a 95% confidence margin. For the transfructosylation activity, the parameters sucrose (coefficient 2.00) and yeast extract (coefficient 2.35) had a positive significant effect at 90% confidence, as well as yeast extract (coefficient 1.93) for hydrolytic and transfructosylation activities ratio.

Thus, the data represented by the estimation of main effects are in line with the Pareto diagram and suggest that the reduction in the percentage of residue and the increase in the percentage of yeast extract favor the production of total phenolic compounds, while the increase in the percentage of sucrose benefits the responses of the hydrolytic activity and transfructosylation variables. The same effect occurs for the dependent variables transfructosylation activity and At/Ah ratio for the yeast extract parameter.

Central Composite Rotational Design (CCRD)

With the significant variables selected in the Plackett–Burman design, it was possible to improve the detection of optimal regions in a sequential optimization, using the CCRD and response surface methodology. Table S4 reports the coded levels of the selected variables with the minimum (− 1.68), medium (0) and maximum (+ 1.68) values and the interpolation between them for the factorial points (+ 1, − 1). The CCRD full factorial 2³ results in eight tests at the factorial points (combination between levels + 1 and − 1), in addition to four axial points (variables at levels + 1.68 and − 1.68) and four repetitions at the central point (level 0), totaling the 18 experiments presented in Table S5.

Table S6 presents the responses obtained for the independent variables residue, sucrose and yeast extract in relation to the content of phenolic compounds, transfructosylation activity, hydrolytic activity and transfructosylation and hydrolytic activity ratio. It was observed that the concentration of total phenolic compounds ranged from 450.48 ± 2.09 mg GAE/100 g to 2189.00 ± 49.79 mg GAE/100 g, the hydrolytic activity ranged from 10.07 ± 0.24 U/ mL to 151.08 ± 1.92 U/mL, the transfructosylation activity ranged from 6.71 ± 0.22 U/mL to 94.44 ± 0.34 U/mL and the transfructosylation and hydrolytic activity ratio varied from 0.05 ± 0.01 to 7.45 ± 0.27. The four central points did not present significant differences (p > 0.05) in their responses, which indicate a good repeatability of the fermentation process.

The Pareto diagram (Figure S2) shows the estimated effects of the independent variables that affect the process of obtaining phenolic compounds and enzymes with hydrolytic and transfructosylation activity. The parameters that presented p-value ≤ 0.05 were considered significant for the total phenolic and transfructosylation activity (At) variables, while for the hydrolytic activity (Ah) and transfructosylation and hydrolytic activity ratio (At/Ah) variables, the significant values considered were 0.05 ≤ p ≤ 0.1, due to the different nature of the factors studied and their variability.

For the phenolic compounds response, the linear and quadratic effects of the residue variable and the linear effect for the yeast extract parameter were significant (p ≤ 0.05). For the hydrolytic activity, the linear and quadratic effects for the sucrose and yeast extract variables, as well as the interactions between the sucrose and yeast extract and sucrose and residue effects were significant (p ≤ 0.05). The transfructosylation activity response showed a significant effect for the linear term of yeast extract and quadratic for sucrose at the significance level of 5%. Furthermore, high interaction between the variables sucrose and residue and yeast extract and residue were identified. Finally, the At/Ah ratio showed a significant effect (p ≤ 0.10) for the linear term of yeast extract and quadratic for sucrose and yeast extract.

Table 2 reports the analysis of variance of the experimental data using the F Test, to verify the main effects of the studied independent variables and their interactions, and to identify the factors that significantly affect the process. In addition, it also presents the R² and the lack of fit test of the models, which show the degree of adjustment of the regression model to the experimental data.

Table 2.

Estimation of the main effects of the Central Composite Rotational Design (CCRD) planning for the total phenolics responses, hydrolytic activity (Ah), transfructosylation activity (At) and transfructosylation and hydrolytic activity ratio (At/Ah)

Factors	Phenolics (mg GAE/100 g)		Ah (U/mL)		At (U/mL)		At/Ah
	F test	p-value**	F test	p-value*	F test	p-value**	F test	p-value*
Model	136.78067	< 0.00001	5.680248	0.006568	14.05529	0.0001194	14.198570	0.000158
Linear
Sucrose (X1)	ns	ns	3.54194	0.086544	ns	ns	ns	ns
Yeast extract (X2)	287.95244	0.000446	13.06001	0.004070	5.59533	0.034226	3.72814	0.074009
Residue (X3)	1980.55414	0.000025	ns	ns	ns	ns	ns	ns
Quadratic
Sucrose (X12)	ns	ns	3.64123	0.082793	24.93996	0.000246	30.60593	< 0.0001
Yeast extract (X22)	ns	ns	9.17586	0.011466	ns	ns	14.35550	0.001994
Residue (X32)	376.91877	0.000299	ns	ns	ns	ns	ns	ns
Interaction
1 by 2 (X1X2)	ns	ns	3.29664	0.096750	ns	ns	ns	ns
1 by 3 (X1X3)	ns	ns	3.03333	0.109425	18.72025	0.000822	ns	ns
2 by 3 (X2X3)	ns	ns	ns	ns	6.96561	0.020425	ns	ns
Lack of fit	7.932391463	0.057306784	1729.35843	< 0.0001	68.663114	0.0025621	28.514979	0.0093004
R2	0.96701		0.756		0.8122		0.7526

^**95% confidence level; * 90% confidence level

ns not significant

Among variables that significantly influenced the total phenolic compounds, only the residue (quadratic) and yeast extract (linear) showed a positive effect. The residue (linear) had a negative effect on obtaining these compounds. The model for the coded values can be seen in Eq. (3):

$$Y{log}_{\mathit{TF}}=2.847950+0.058587X_2-0.153651X_3+0.067075X_3^2$$ 3

The F value (136.78067) of the model presented was highly significant (< 0.00001) and the R² was 96.70% which suggests that the model justifies the experimental data. According to the response surface graph (Fig. 1a, b and c), the maximum content of phenolic compounds can be obtained with a residue concentration of 5.1–7.6% and of extract of 4.5–6.0% yeast.

Fig. 1.

Response surface graphs with the interaction effects of the variables total phenolics (a, b, c), hydrolytic activity—Ah (d, e, f), transfructosylation activity—At (g, h, i) and transfructosylation and hydrolytic activity ratio – At/Ah (j, k, l)

Equation 4 describes the hydrolytic activity predicted by the model according to the variables yeast extract, sucrose and interactions. Sucrose (linear and quadratic), yeast extract (quadratic) and the interaction between sucrose and residue had a positive effect, while yeast extract (linear) and the interaction between sucrose and yeast extract had a negative effect on this dependent variable.

$$Y_{\mathit{Ah}}=36.8542+20.0770X_1+20.6827X_1^2-38.5523X_2+32.8327X_2^2-25.3072X_1{X}_2+24.2755X_1{X}_3$$ 4

The F value of the model (5.680248) is higher than the tabulated value (2.38) and the R² value was 75.60%, which indicates that the model is significant to explain the hydrolytic activity in relation to parameters studied. Response surface plots (Figs. 1d, e, f) displayed values between 2 and 2.5% of yeast extract, 35–40% of sucrose and 5.1–7.6% of residue for maximum values of hydrolytic activity.

The coded model that describes the transfructosylation activity can be seen in Eq. 5. Only the interaction between sucrose and the residue had a positive effect on the variable. Sucrose (quadratic), yeast extract (linear) and the interaction between yeast extract and residue exhibited a negative effect.

$$Y_{\mathit{At}}=36.2746-30.8956X_1^2-14.6240X_2+34.9495X_1{X}_3-21.3189X_2{X}_3$$ 5

The F value of the model (14.05529) is higher than the tabulated value (3.17) and the R² value was 81.22%, which suggests that the model is adequate to validate the transfructosylation activity in relation to analyzed data. The response surface graph (Figs. 1g, 1h, 1i) indicates that the highest transfructosylation activity can be obtained with a sucrose concentration of 0 to 20%, residue of 5.1 to 7.6% or 20 to 25% and yeast extract from 2 to 3% or 5 to 6%.

The results obtained are in agreement with those reported by Maso et al. (2021) that established the optimum conditions for obtaining the enzymes fructosyltransferase (transfructosylation activity) and β-fructofuranosidase (hydrolytic activity) by the filamentous fungi Aspergillus niger and Penicillium brasilianum as the use of 20% sucrose and 0.5% yeast extract. However, different species of microorganisms, including the Aspergillus genus, can present different behaviors in relation to enzyme production, as shown by Cunha et al. (2019) that optimized the fructosyltransferase enzyme for the Aspergillus oryzae strain and detected optimal sucrose values ranging from 30 to 47%.

The At/Ah ratio is described by Eq. 6 for the coded values. The sucrose (quadratic) and yeast extract (quadratic) variables showed a negative effect, while the yeast extract (linear) had a positive effect on the variable. The F value of the model (14.198570) was greater than the tabulated value (2.52) and the R² value was 75.26%, therefore, the model is adequate to validate the relationship between enzymatic activities in relation to variables investigated.

$$Y_{At/Ah}=3.74086-1.20791X_1^2+0.41493X_2-0.82726X_2^2$$ 6

The response surface graphs (Fig. 1j, k, l) show a maximum ratio between transfructosylation and hydrolytic activities with a yeast extract concentration ranging from 3.5 to 5.0% and sucrose concentration from 15 to 25%.

Desirability function

When using a complex system, several experimental factors must be optimized, so it is essential to evaluate alternative analytical procedures according to various criteria. Determining the optimum conditions for the input variables requires the simultaneous consideration of all responses. To obtain a satisfactory answer, the desirability approach is used as a powerful tool in multi-response systems (Rezende et al. 2017).

The desirability function is based on transforming all responses obtained at different scales into an unscaled value. The values of the desirability functions are between 0 and 1, where the value 0 is assigned when the factors give an undesirable response and the value 1 corresponds to the optimal performance for the studied factors (Lee et al. 2018). Thus, the desirability function is related to numerical optimization, which is based on the idea that the quality of a product or process that has multiple responses needs to find the process variations that satisfy all restrictions applicable to the final product, combining the adjusted models individually for each answer in a single univariate answer. Thus, the optimization results in conjunction with this function enable the establishment of ranges for the most influential variables that control the process (Rezende et al. 2017).

For the desirability function, the operational conditions that lead to the optimization of the process of obtaining phenolic compounds and enzymes with low hydrolytic activity and high transfructosylation activity, as well as a high ratio of transfructosylation and hydrolytic activity (At/Ah) were considered. This criterion stems from the importance of the At/Ah ratio, since high At and low Ah values reflect high At/Ah ratio values, culminating in a greater transfructosylation activity in the reaction medium. This high transfructosylation activity, in turn, allows a high conversion of sucrose into fructooligosaccharides, while a high At/Ah ratio is necessary to avoid hydrolysis of the fructooligosaccharide molecule (Cunha et al. 2019).

The results revealed that a maximum desirability function D of 0.91518 (Figure S3) was achieved when the optimized process conditions were employed, i.e. 17.3% (− 0.2242) sucrose, 4.6% (0.50454) yeast extract and 5.1% (− 1.682) residue. Thus, the use of such experimental conditions results in an extract with the desired concentrations of total phenolics of 2139.93 mg GAE/100 g, hydrolytic activity of 39.51 U/mL, transfructosylation activity of 172.33 U/mL and ratio transfructosilation activity and hydrolytic activity of 3.85.

Validation of optimized conditions

Table 3 presents data on predicted and experimentally obtained values in the validation test of optimal conditions as predicted by the desirability function.

Table 3.

Experimental validation data and predicted values of the optimized conditions

Variables	Experimental	Desirability	CV(%)
Total Phenolics (mg GAE/100 g)	2204.89 ± 5.75	2139.93	2.11
Ah (U/mL)	39.84 ± 2.08	39.51	0.59
At (U/mL)	168.09 ± 3.81	172.33	1.76
At/Ah	4.23 ± 0.19	3.85	6.55

CV Coefficient of variation

For all response variables, the experimental results were similar to the predicted results. The coefficients of variation ranged from 0.59 to 6.55%; these values being low in the desired region where responses are maximized. These results demonstrate the usefulness and validity of using the CCRD experimental design, response surface methodology and desirability function to individually optimize the conditions for obtaining total phenolic compounds, hydrolytic activity, transfructosylation activity and transfructosylation activity and hydrolytic activity ratio, or optimize multiple responses by establishing conditions that ensure greater production of bioactive compounds together with desirable responses of hydrolytic activities, transfructosylation and the ratio between them.

Identification and quantification of phenolic compounds by UFLC-DAD

Figure 2 illustrates the identified polyphenols that showed an increase in their total content in the final optimized product. It was observed that under optimal conditions the gallic acid content increased from 92.13 ± 13.48 µg/g to 228.65 ± 21.9 µg/g, and the protocatechuic acid content from 4.74 ± 0.25 µg/g to 14.49 ± 0.46 µg/g, about 148.17% and 205.51% increase, respectively. Traces of benzoic acid and vanillin were also found in the analyzed samples.

Fig. 2.

Phenolic compounds identified in the control and optimized fermentations

Saeed et al. (2021) observed a 100% increase in the production of gallic acid after a 72 h fermentation with black plum seed and Aspergillus niger. Torres-León et al. (2019) also reported the increase in gallic acid in its fermentation with mango seed and Aspergillus niger. Rashid et al. (2019) evaluated the changes in the profile of phenolic compounds in rice bran fermented with Aspergillus oryzae, and detected a 217.98% increase in protocatechuic acid in the fermented product, which increased its concentration from 2.78 to 8.84 µg/g.

The increase in gallic acid concentration can be justified, since microorganisms, such as those belonging to the genus Aspergillus spp., produce tannase, an enzyme that hydrolyses tannins present in the plant matrix (Aharwar and Parihar 2018). Barros et al. (2020b) evaluated the amount of tannins present in the cupuassu sample and detected 186.49 ± 6.17 mg CA/100 g of condensed tannins and 3946.91 ± 68.60 mg TAE/100 g of hydrolyzed tannins. In addition, the fermentation process produces other enzymes, with cellulolytic, ligninolytic and pectinolytic activities, which can hydrolyze plant cell walls, and generate the release of phenolic compounds (Huynh et al. 2014).

Regarding the increase in protocatechuic acid levels, the same can be explained by the benzoic acid metabolic pathway. According to Lubbers et al. (2019), benzoic acid can be converted by strains of the Aspergillus genus to protocatechuic acid in two steps: through p-hydroxylation carried out by the enzyme benzoate-4-monooxygenase (BphA) which produces p-hydroxybenzoic acid and then, through m-hydroxylation of p-hydroxybenzoic acid by the enzyme p-hydroxybenzoate-m-hydroxylase (PhhA) forming protocatechuic acid. Furthermore, hydroxycinnamic acids, such as cinnamic and p-coumaric acid, in alternative routes, can be converted to p-hydroxybenzoic acid and protocatechuic acid through the PhhA enzyme (Lubbers and Vries 2021).

In biosynthesis processes of phenolic compounds, cinnamic acid is one of the precursors of benzoic and p-coumaric acids (Lubbers et al. 2019). Cupuassu residue has significant amounts of p-coumaric acid (Barros et al. 2020b; Costa et al. 2019), and thus we infer that cupuassu residue may also have significant concentrations of cinnamic acid or even benzoic acid and these, in turn, may have been used as substrates for the synthesis of protocatechuic acid. In addition, the fermentation process produces enzymes, with cellulolytic, ligninolytic and pectinolytic activities, which can hydrolyze plant cell walls, and generate the release of bound phenolic compounds, which increase antioxidant activities (Hashemi et al. 2019; Adebo and Medina-Meza 2020).

With respect to therapeutic effects, several beneficial effects are reported for gallic acid, including antioxidant, anti-inflammatory and anti-neoplastic properties (Kahkeshani et al. 2019). Furthermore, research suggests that this compound has an action against gastrointestinal, neuropsychological, metabolic and cardiovascular disorders (Yang et al. 2020). Protocatechuic acid, on the other hand, exerts multiple biological properties, such as anti-cancer, anti-atherosclerosis, anti-diabetes and neuroprotection (Zheng et al. 2019).

Antioxidant capacity

Table 4 presents the data on antioxidant activity of the residue and the fermented product under optimized conditions, using the ORAC, FRAP, ABTS and DPPH assays. The results obtained show that the antioxidant activity of the fermented product compared to control 1 showed an increase of 350% for the ABTS method, 51.97% for FRAP, 22.65% for ORAC and 16.03% for DPPH.

Table 4.

Antioxidant activity of the optimized fermented product and fructooligosaccharides

Assays	Samples
	Control 1*	Optimized fermentation	Control 2**	Fructooligosacharides
ABTS (µmol TE/g)	28.97 ± 0.20b	130.40 ± 5.89ª	1.90 ± 0.38b	5.68 ± 0.03a
FRAP (µmol TE/g)	78.57 ± 0.14b	119.41 ± 1.53ª	0.28 ± 0.01a	0.38 ± 0.01a
ORAC (µmol TE/g)	449.41 ± 13.29b	551.24 ± 4.68a	17.60 ± 0.21b	41.27 ± 2.01ª
DPPH (%)	57.86 ± 0.32b	67.14 ± 3.67ª	17.49 ± 4.09b	52.07 ± 0.07a

^*Control 1 – Unfermented culture medium

^**Control 2—Citrate buffer + 50% sucrose + enzyme

Results are expressed as mean ± standard deviation values (n = 3)

Mean values on the same line followed by different lowercase letters were significantly different (p ≤ 0.05) between samples

Gulsunoglu et al. (2020) evaluated the increase in the antioxidant capacity of apple residue through the DPPH methodology using different strains of the genus Aspergillus spp. After 72 h of fermentation, the samples showed an increase of 103.23% in the antioxidant capacity for Aspergillus tubingensis, 106.86% for Aspergillus niger, 15.93% for Aspergillus japonicus and 8.93% for Aspergillus aculeatus.

Dulf et al. (2017) mea sured the increase in the antioxidant activity of apricot residue fermented with the Aspergillus niger strain by 18% after 48 h using the DPPH methodology and Torres-León et al. (2019) verified an increase in antioxidant activity by the DPPH and ABTS methods for mango residues and the Aspergillus niger strain by 8% and 10%, respectively.

The difference between the behavior of the antioxidant capacities of fermented products may be related to the difference between the mechanisms accessed by the different techniques used. In the FRAP methodology, the antioxidant capacity is based on the ability of the antioxidant to reduce the Fe³⁺ ion to Fe²⁺ (Gülçin, 2014). The DPPH method is based on the antioxidant's ability to transfer hydrogen atoms to free radicals. The ABTS test measures the ability of the antioxidant to donate electrons and reduce the ABTS^•+ radical (Schaich et al. 2015). The ORAC protocol measures the ability of the antioxidant compound to eliminate oxygen and peroxyl free radicals, through hydrogen radical transfer or radical addition (Prior et al. 2003). Therefore, the phenolic compounds that access the FRAP methodology are not the same that access the ABTS, ORAC or DPPH methods.

Furthermore, antioxidant activity was identified in the solution containing the fructooligosaccharides for all evaluated methods and it was observed that when compared to control solution 2, the antioxidant capacity of the sample showed an increase of 198.94% for ABTS methodology, 35.71% for FRAP, 134.48% for ORAC and 197.71% for DPPH. Ojwach et al. (2020) detected antioxidant activity of fructooligosaccharides synthesized from fructosyltransferase obtained from A. niger by FRAP (15%) and DPPH (42%) methodologies, corroborating the results found in the present study.

Conclusion

In conclusion, this study reported the optimized conditions (17.3% sucrose, 5.1% residue and 4.6% yeast extract) to obtain total phenolic compounds (2204.89 mg GAE/100 g), gallic acid (228.65 µg/g), protocatechuic acid (14.49 µg/g), hydrolytic activity (39.84 U/mL) and transfructosylation activity (168.09 U/mL) through submerged fermentation of cupuassu residue with Aspergillus carbonarius. Associated with these results, there was an increase in antioxidant activities (350% for ABTS, 16.03% for DPPH, 51.97% for FRAP, 22.65% for ORAC) for the fermented products as well as for the solution containing fructooligosaccharides. Furthermore, purification protocols for enzymes and phenolic acids, as well as in vitro and in vivo studies are essential to fully corroborate the potential for biotechnological application of these bioactive products.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

DPPH

3,5-Dinitrosalicylic acid, 2,2-difenil-1-picrilhidrazilo

TPTZ

2,4,6-Tripyridyl-s-triazine

Trolox

6-Hidroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid

AAPH

2,2′-Azobis(2-methylpropionamidine) dihydrochloride

ABTS

2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonate)

ORAC

Oxygen radical absorbance capacity

FRAP

Ferric reducing antioxidant power

CCRD

Central composite rotational design

HPLC

High performance liquid chromatography

UFLC

Ultra fast liquid chromatography

DAD

Diode array detector

Authors' contributions

RGCB: Conceptualization, Formal analysis, Project administration, Writing: original draft; UCP: Investigation and Formal analysis; JKSA: Investigation and Formal analysis; CSO—Investigation and Formal analysis; JPN: Investigation and Formal analysis and NN: Fund resources, Writing: Review and editing and Supervision.

Funding

This work was supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) (Project 465335/2014–4), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Finance Code 001) and Fundação de Apoio à Pesquisa e a Inovação Tecnológica do Estado de Sergipe (FAPITEC).

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Declarations

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The authors declare no conflict of interest.

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