Antioxidant, Tyrosinase, α-Glucosidase, and Elastase Enzyme Inhibition Activities of Optimized Unripe Ajwa Date Pulp (Phoenix dactylifera) Extracts by Response Surface Methodology

The Ajwa date (Phoenix dactylifera L., Arecaceae family) is a popular edible fruit consumed all over the world. The profiling of the polyphenolic compounds of optimized unripe Ajwa date pulp (URADP) extracts is scarce. The aim of this study was to extract polyphenols from URADP as effectively as possible by using response surface methodology (RSM). A central composite design (CCD) was used to optimize the extraction conditions with respect to ethanol concentration, extraction time, and temperature and to achieve the maximum amount of polyphenolic compounds. High-resolution mass spectrometry was used to identify the URADP’s polyphenolic compounds. The DPPH-, ABTS-radical scavenging, α-glucosidase, elastase and tyrosinase enzyme inhibition of optimized extracts of URADP was also evaluated. According to RSM, the highest amounts of TPC (24.25 ± 1.02 mgGAE/g) and TFC (23.98 ± 0.65 mgCAE/g) were obtained at 52% ethanol, 81 min time, and 63 °C. Seventy (70) secondary metabolites, including phenolic, flavonoids, fatty acids, and sugar, were discovered using high-resolution mass spectrometry. In addition, twelve (12) new phytoconstituents were identified for the first time in this plant. Optimized URADP extract showed inhibition of DPPH-radical (IC50 = 87.56 mg/mL), ABTS-radical (IC50 = 172.36 mg/mL), α-glucosidase (IC50 = 221.59 mg/mL), elastase (IC50 = 372.25 mg/mL) and tyrosinase (IC50 = 59.53 mg/mL) enzymes. The results revealed a significant amount of phytoconstituents, making it an excellent contender for the pharmaceutical and food industries.


Introduction
Antioxidative phenolics found in the tissues of many plant species are thought to be responsible for their medicinal actions. They play a variety of purposes in plants, from structural to defensive [1]. However, studies have demonstrated phenolics' preventive significance in diabetes, chronic cardiovascular illnesses, cancer, and aging cases [2,3]. Their positive effects on human health have thus far undergone substantial study. The study of polyphenolic compounds is gaining popularity, and the first and most crucial stage in extracting and purifying polyphenolic compounds from plant sources is extraction [4], given that the extraction of polyphenol is influenced by several factors, including the chemical makeup of the sample, the solvent employed, agitation, extraction time, solute/solvent ratio, and temperature [5,6]. Furthermore, phenolic molecules should not be oxidized because they participate in the enzymatic browning reaction and lose their phenol activity and antioxidant capacity [7]. Additionally, phenolic compounds' structural and physicochemical diversity precludes a uniform extraction methodology and necessitates a addition, it contains abundant bioactive components such as polyphenols, including phenolic acids, flavonoids, and lignans [20].
To the best of our knowledge, this is the first report that uses RSM to improve the extraction conditions so that more polyphenolic components may be extracted from the pulp of unripe Ajwa dates (URADP). The goal was to obtain the highest polyphenolic content possible from URADP by investigating and optimizing extraction parameters such as extraction temperature and duration, as well as ethanol concentration, using the RSM central composite design (CCD) tool. The RSM-CCD approach's projected values accurately reflect the actual findings, and this statistical technique can be used to maximize the extraction of URADP polyphenolic compounds.

Results and Discussion
Response surface methodology (RSM) is a collection of mathematical and statistical methods built on fitting polynomial equations to experimental data. It accurately describes the behavior of data collection designed to produce statistical predictions. It is better than traditional single-parameter optimization since it takes less time, space, and raw materials [18,19].
Scientific information dealing with optimization of the extraction of polyphenols from unripe Ajwa date pulp (URADP) extracts is very inadequate. Mounting evidence has revealed the optimization of ultrasonic assistance extraction, microwave-assisted extraction, and supercritical fluid extraction procedures that were performed to extract polyphenols from different varieties of dates except from Ajwa dates [4,[27][28][29]. To the best of our knowledge, this is the first report dealing with the optimization of heat extraction on individual biologically active polyphenols as dependent variables. Table 1 lists the experimental conditions and findings for each extraction scenario. All response variables were transformed into second-order quadratic polynomials to account for extraction factor effects. The statistical significance of the fitted second-order quadratic model equations was assessed using ANOVA. The fitness of the model was evaluated using the regression coefficient (β), adjusted correlation factor (R 2 ), coefficient of variation (CV), and adequate precision ( Table 2). The non-significant terms (p > 0.05) were removed to enhance the models' fit and predictions. p values were used to assess each coefficient's significance. The model terms were statistically significant, extremely significant, and impressively significant when the p values were less than 0.05, 0.01, and 0.001, respectively.

Fitting of the RSM Models
From Table 2, smaller probability values (p < 0.0001) indicate that the model terms are significant. In general, proceeding with exploration and optimization of a fitted response surface may produce poor or misleading results unless the model exhibits an adequate fit [7]. The developed regression models have a high degree of statistical significance, as indicated by their R 2 values (0.9706 and 0.9968). The appropriate precision value is an indicator of the signal-to-noise ratio. It is preferable to have a ratio of >4 [25]. Here, the ratios were 15.9930 and 49.6969, suggesting a sufficient signal, indicating that the model is suitable for this procedure. The coefficient of variation (CV) is a measure of a model's reproducibility and describes the extent to which the data were dispersed. The CV for total phenolic content (TPC) and total flavonoid content (TFC) of URADP was within the acceptable range (Table 2). Since CV is a measure expressing standard deviation as a percentage of the mean, the small values of CV give better reproducibility. In general, a high CV indicates that variation in the mean value is high and does not satisfactorily develop an adequate response mode [7]. The modified R 2 (R 2 ≥ 0.80) was well within acceptable limits in this study, showing that the experimental data fit second-order polynomial equations satisfactorily. To demonstrate the interactions between the independent variables, 3D surfaces and contour plots were constructed using multiple linear regression equations. The main and cross-product effects of the independent variables on the response variables are more easily understood from these 3D charts ( Figure 1A,B).

Model Validation
The parameters were forecasted using Derringer's desirability function, allowing for the multivariate analysis to discover the ideal level for all responses in a single extraction [37]. Figure 2 shows the contour plot as a function of ethanol concentration, extraction time and temperature. In this study, the following conditions, (X1, 52%), (X2: 81 min), and

Effect of Extraction Parameters on TPC and TFC
Phenolic chemicals are secondary metabolites that plants produce under oxidative stress and are necessary to adapt to various adverse situations [1]. In the current investigation, TPC was measured using the Folin-Ciocalteu reagent, and it was discovered that the TPC ranged from 5.41 to 23.92 mgGAE/g (Table 1). According to earlier research, the total phenol content of Ajwa fruit ranged from 2.45 to 4.55 mgGAE/g. In contrast, this study found that URADP had a more significant percentage of total phenolic compounds [30,31]. Numerous studies have shown that the extraction solvent is crucial in the extraction of phenolic compounds. Compared to alcoholic extracts, the contents in hydroalcoholic extracts are always higher [32]. In addition, Eid et al. [33] stated that the phenolic content in Ajwa dates is also varied according to the ripening stage. Unripe Ajwa dates contain higher amounts of phenolic content than ripe fruits. Our experimental results also support this statement. In addition, flavonoids are the most abundant polyphenolic compounds found in Ajwa dates with pervasive dispersal. These polyphenolic compounds are mainly present within fruit skins in high concentrations with immense health benefits such as antioxidant and free radical scavenging activities [31,33]. In URADP extracts, TFC ranged from 6.81 to 24.20 mgCAE/g, which also agrees with the previous work [34].
As shown in Table 2, the linear effects of ethanol concentration (X 1 ), extraction temperature (X 3 ), quadratic component of (X 1 2 ), (X 2 2 ), and (X 3 2 ) and interaction of (X 1 X 2 ), (X 1 X 3 ) and (X 2 X 3 ) exhibited significant effects on both TPC and TFC, except for the interaction of (X 2 X 3 ), which has no significant effect on TPC. In addition, the regression coefficient (β) values verified the effect of extraction parameter on both TPC and TFC in the following order: TPC: X 1 2 > X 2 2 > X 3 > X 3 2 > X 1 X 3 > X 1 X 2 > X 1 and TFC: X 2 2 > X 1 2 > X 3 2 > X 3 > X 2 X 3 > X 1 X 2 ∼ = X 1 X 3 > X 1 ( Table 2). The following second-order polynomial equations shown in Equations (1) and (2) demonstrate the relationships among TPC, TFC and their variables.
Three-dimensional response surface plots ( Figure 1A,B) were constructed based on Equations (1) and (2), respectively, and were applied to clarify the interactive effects of the three variables on the TPC and TFC of URADP, respectively. The ethanol concentration (X 1 ), extraction time (X 2 ) and extraction temperature (X 3 ) showed an interactive effect on both TPC and TFC, which increased readily with increasing ethanol concentration up to 60%, extraction time up to 90 min and extraction temperature up to 65 • C, followed by a decrease (Figure 1A,B). This could be because a medium concentration of ethanol may make the solvent more polar and dissolve more polyphenols, both polar and moderately polar ones [4]. Experiments in a previous comparative study revealed that the extraction of polyphenols from green tea leaves using a high hydrostatic pressure procedure augmented with the percentage of ethanol in the solvent; peaked at 50% ethanol and dropped after that [35]. Hence, the extraction of polyphenols in hydroalcoholic solution is highly efficient, as the polyphenols are highly soluble in these solutions. Furthermore, when ethanol is present at a moderate quantity in water, it can disrupt and break the architecture and structure of phospholipids that make up the lipid bilayer of membranes, affecting the penetrability of plant cells and thereby allowing for better extraction and diffusion of the polyphenolic compounds [36].

Model Validation
The parameters were forecasted using Derringer's desirability function, allowing for the multivariate analysis to discover the ideal level for all responses in a single extraction [37]. Figure 2 shows the contour plot as a function of ethanol concentration, extraction time and temperature. In this study, the following conditions, (X 1 , 52%), (X 2 : 81 min), and (X 3 , 63 • C), were used to achieve the maximal overall desirability D = 0.977. Under these optimal conditions, the predicted values for TPC and TFC are 23.98 mgGAE/g and 23.39 mgCAE/g, respectively. To verify the sufficiency of the model equations, a triplicate experiment was conducted in the optimal conditions predicted by Derringer's desire model and it found the TPC and TFC values to be 24.20 ± 0.096 mgGAE/g and 22.92 ± 1.19 mgCAE/g, respectively. As stated in Table 3, the relative standard deviations (RSDs) of TPC and TFC showed that the predicted values for all groups were very similar to the experimental results. This result is also supported by a prior report [38]. The suitability of the response surface methodology model for quantitative predictions was verified by a satisfactory agreement between the predicted and measured values.

Comparison of Optimized Extraction Condition with Other Extraction Methods Using Different Solvents
To demonstrate the effectiveness of the optimized method in extracting TPC and TFC, a comparative study was performed. As shown in Figure 3A, higher yields of TPC and TFC were obtained using hydroalcoholic solvent in heat extraction instead of methanol, ethanol and water for heat and maceration extraction. The extraction efficiency of TPC and TFC of different solvents and conditions are presented as heat extract with optimized condition (OP) > heat extract with 100% H2O (HW) > heat extract with 100% methanol (HM) > maceration extract with 100% methanol (MM) > heat extract with 100% ethanol (HE) > maceration extract with 100% H2O (MW) > maceration extract with 100% ethanol (ME) and OP > HM > HW > MM > HE > MW > ME, respectively. This result indicated that hydroalcoholic solvent with heat extraction was more efficient than that of other solvents with both heat and maceration techniques. The results also coincided with those obtained for the extraction of TPC and TFC from dates [30][31][32].
In addition, the pharmacological properties, such as antioxidant, tyrosinase, α-glucosidase, and elastase enzyme inhibitory activities, of various URADP extracts were intensively examined to determine their potential for application. Antioxidant components often have a potent ability to scavenge free radicals, preserving DNA and proteins from damage. Therefore, antioxidant chemicals have been utilized to treat a variety of diseases. DPPH • and ABTS •+ has been frequently used as a representative reagent for examining the free radical scavenging activities of bioactive compounds. To quantify the antioxidant activities of different extracts/compounds, the concentration of the samples required to scavenge 50% of radicals (IC50) was measured. A smaller IC50 value indicates an increase in free-radical scavenging ability [38].

Comparison of Optimized Extraction Condition with Other Extraction Methods Using Different Solvents
To demonstrate the effectiveness of the optimized method in extracting TPC and TFC, a comparative study was performed. As shown in Figure 3A, higher yields of TPC and TFC were obtained using hydroalcoholic solvent in heat extraction instead of methanol, ethanol and water for heat and maceration extraction. The extraction efficiency of TPC and TFC of different solvents and conditions are presented as heat extract with optimized condition (OP) > heat extract with 100% H 2 O (HW) > heat extract with 100% methanol (HM) > maceration extract with 100% methanol (MM) > heat extract with 100% ethanol (HE) > maceration extract with 100% H 2 O (MW) > maceration extract with 100% ethanol (ME) and OP > HM > HW > MM > HE > MW > ME, respectively. This result indicated that hydroalcoholic solvent with heat extraction was more efficient than that of other solvents with both heat and maceration techniques. The results also coincided with those obtained for the extraction of TPC and TFC from dates [30][31][32].
In addition, the pharmacological properties, such as antioxidant, tyrosinase, α-glucosidase, and elastase enzyme inhibitory activities, of various URADP extracts were intensively examined to determine their potential for application. Antioxidant components often have a potent ability to scavenge free radicals, preserving DNA and proteins from damage. Therefore, antioxidant chemicals have been utilized to treat a variety of diseases. DPPH • and ABTS •+ has been frequently used as a representative reagent for examining the free radical scavenging activities of bioactive compounds. To quantify the antioxidant activities of different extracts/compounds, the concentration of the samples required to scavenge 50% of radicals (IC 50 ) was measured. A smaller IC 50 value indicates an increase in free-radical scavenging ability [38]. As anticipated, OP showed the lowest IC50 values (87.56 ± 1.21 mg/mL) for DPPH-, whereas HM had the lowest IC50 values (105.56 ± 0.98 mg/mL) for ABTS-radical scavenging activity. In addition, OP had the lowest IC50 values of 59.53 ± 1.02 mg/mL and 221.59 ± 2.52 mg/mL for tyrosinase and α-glucosidase enzyme inhibition, respectively. In contrast, the IC50 values (299.05 ± 2.52 mg/mL) for elastase enzyme inhibition were achieved by HW. To calculate the correlation between phenols, flavonoids, antioxidant and enzymes inhibition activity of different enriched products, the Pearson coefficient (ρ) method (supplementary data Table S2) was assessed. A negative ρ value (−1) represents the perfect positive correlation between polyphenols, free radical scavenging and enzyme inhibition ability using IC50. The results revealed very strong correlations for DPPH-radical scavenging and tyrosinase inhibition activity (p < 0.01) with TFC and (p < 0.05) for TPC. In contrast, there was no strong correlation shown between polyphenolic content with ABTS-radical scavenging, α-glucosidase and elastase enzyme inhibition activity. These data are in accordance with other studies that show that higher phenol content augments the antioxidant activity [39,40].

Chemometric Analysis
Chemometric analysis is the process of better understanding chemical information using mathematical and statistical methods. It is also the process of correlating quality characteristics to analytical instrument data. It has been used to investigate the relationship between antioxidant components and the antioxidant potentiality of various plant extracts [41]. This study used two chemometric techniques-principal component As anticipated, OP showed the lowest IC 50 values (87.56 ± 1.21 mg/mL) for DPPH-, whereas HM had the lowest IC 50 values (105.56 ± 0.98 mg/mL) for ABTS-radical scavenging activity. In addition, OP had the lowest IC 50 values of 59.53 ± 1.02 mg/mL and 221.59 ± 2.52 mg/mL for tyrosinase and α-glucosidase enzyme inhibition, respectively. In contrast, the IC 50 values (299.05 ± 2.52 mg/mL) for elastase enzyme inhibition were achieved by HW. To calculate the correlation between phenols, flavonoids, antioxidant and enzymes inhibition activity of different enriched products, the Pearson coefficient (ρ) method (supplementary data Table S2) was assessed. A negative ρ value (−1) represents the perfect positive correlation between polyphenols, free radical scavenging and enzyme inhibition ability using IC 50 . The results revealed very strong correlations for DPPH-radical scavenging and tyrosinase inhibition activity (p < 0.01) with TFC and (p < 0.05) for TPC. In contrast, there was no strong correlation shown between polyphenolic content with ABTS-radical scavenging, α-glucosidase and elastase enzyme inhibition activity. These data are in accordance with other studies that show that higher phenol content augments the antioxidant activity [39,40].

Chemometric Analysis
Chemometric analysis is the process of better understanding chemical information using mathematical and statistical methods. It is also the process of correlating quality characteristics to analytical instrument data. It has been used to investigate the relationship between antioxidant components and the antioxidant potentiality of various plant extracts [41]. This study used two chemometric techniques-principal component analysis (PCA) and hierarchical cluster analysis (HCA)-to find how the extraction method affected TPC, TFC, antioxidant effects, and other enzyme-inhibitory activities of URADP. PCA analysis reduces the dimensions of the data set and analyzes the responses based on the correlation between data samples. PCA could also find the variable that makes the most difference in the data set [41]. The loading plots were used to determine correlations between the study's variables. The antioxidant activity, TPC, TFC, and other enzyme inhibitory activities were all included in these loading plots ( Figure 3C). A total of 64.6% of the data set's variability was accounted for by the first principal component (PC1), which also had the highest eigenvalue of 4.52. Meanwhile, 20.6% of the variability was represented by the PC2, which had an eigenvalue of 1.44. According to Figure 3C, the TPC and TFC, which point in the opposite direction from the IC 50 loading vectors, may have the most significant potential to contribute to DPPH-, ABTS-radical scavenging, and tyrosinase inhibitory capacities. According to Pearson's correlation analysis, the TPC and TFC were strongly linked with the antioxidant and tyrosine kinase inhibitory actions, supporting the PCA result (Table 2). However, neither TPC nor TFC substantially impacted the activities of elastase and α-glucosidase. Additionally, all variables resulting from comparing the first two PCs ( Figure 3C) revealed the existence of three different extract sample groups. Due to their high bioactive component concentration, antioxidant, and tyrosinase inhibitory activity, OP and HM made up Cluster I. In contrast, HE, HW, MM, and ME were made up of Cluster II since they had a mixed record regarding bioactive chemicals, antioxidant activity, and enzyme inhibition. Due to its inferior performance in TPC, TFC, antioxidant, and enzyme inhibition potentiality, extract MW made up Cluster III. Based on similarities, HCA was used to classify distinct solvent-based extraction techniques under research ( Figure 3D).

Secondary Metabolites Profiling in URADP by High-Resolution Mass Spectrometry
Secondary metabolites in the URADP extracts were identified using ESI-MS/MS in the negative ionization modes. As indicated in Table 4, seventy (70) compounds were identified in the negative mode using MS n data from the mass of the precursor ion, fragments, recognized fragmentation patterns for the given classes of compounds, and neutral mass loss, and from comparisons with the existing literature and searches in online databases. Furthermore, the significance of these results was determined by finding the confidence level. Level 3 denotes a tentative candidate, whereas level 2 indicates the probable structure of the identified compound [42].

Flavonoids
Numerous studies demonstrated that each subgroup of flavonoids exhibits a different fragmentation behavior in MS 2 analysis. The cleavage of the C-ring bonds (retro-Diels-Alder, i.e., RDA mechanism) produces ions with the A-or B-ring and some part of the C-ring, which is the most common fragmentation of flavonoids, and notable losses of small neutral molecules, such as CO (28 Da), C 2 H 2 O (42 Da), COO (44 Da), and 2CO (56 Da). [5,42,43]. Based on a comparison of the fragmentation patterns with those previously published in the literature, compounds 10-15 were identified as luteolin, catechin or epicatechin, chrysoeriol, quercetin, epigallocatechin, and methoxysinensetin, respectively [5,[42][43][44][45].  Figure 4D). This compound was also identified for the first time in RADP. Compounds 41-48 were confirmed as sugar molecules from comparison of their deprotonated ion mass and fragmentation behaviors with those reported in the literature and online databases [42,43,[48][49][50].

Carboxylic Acids and Fatty Acids
From comparisons of the mass and the fragmentation behaviors of the precursor ion based on mass spectroscopic analysis reported in the literature and various online databases [42,43,[48][49][50], compounds 50-70 were identified as carboxylic acids and fatty acids (Table 4).

Sample Collection and Preparation
A scientific officer at the National Herbarium and Genebank of Saudi Arabia recognized unripe Ajwa date fruits (voucher specimen No. NHG005) obtained from an Ajwa date farm in Al-Madina Al-Munawara, Saudi Arabia, and they were kept in our lab for additional research. Unripe Ajwa date pulp (URADP) was separated, dried outside, chopped into small pieces, and ground in a sterilized laboratory blender (model 7011HS, Osaka Co. Ltd., Kita-Ku, Osaka, Japan). The powdered samples were maintained in an airtight container covered in aluminum foil and chilled before extraction.

Extraction Methods
Two distinct techniques and three different solvents (ethanol, methanol, and distilled water) were used for solvent extractions. The maceration method was primarily chosen because it is straightforward and inexpensive. In contrast, heat extraction was carried out in anticipation of a shorter extraction time since temperature may aid in breaking the plant cell wall of an empty palm fruit during heat extraction.
As stated by Mollica et al. [51], the maceration process was carried out with continuous stirring. Briefly, the plant materials (10 g) were soaked in 200 mL of the solvents, and extractions were performed with stirring at 250 rpm for 24 h at room temperature. Choi et al. [5] stated that 10 g of the extract and 200 mL of the solvents were used for heat extraction, which was carried out at 60 • C for 1 h. Following the extraction process, each extract was filtered using Whatman no. 1 filter paper (Schleicher & Schuell, Keene, NH, USA). The solvents were then removed using a rotary evaporator (Tokyo Rikakikai Co. Ltd., Tokyo, Japan) at 50 • C and 50 rpm. Finally, the extracts were lyophilized using a freeze dryer (Il-shin Biobase, Goyang, Republic of Korea). Before further research, the URADP extract was kept at −20 • C.

Total Phenolic Content (TPC) and Total Flavonoid Content (TFC)
The total phenolic content (TPC) and total flavonoid content (TFC) in URADP extracts were determined by the Folin-Ciocalteu test and the aluminum chloride colorimetric method, respectively [39]. The TPC (y = 0.0512x + 0.0018; r2 = 0.9835) and TFC (y = 0.014x + 0.0021; r2 = 0.9994) were determined using the corresponding regression equations for the calibration curves. The TPC was expressed in terms of the gallic acid equivalent (mg)/dry weight sample (g) and the TFC in terms of the catechin equivalent (mg)/dry weight sample (g).

Antioxidant Assay and Enzyme Inhibitory Effects
The antioxidant and enzyme inhibitory capability of various URADP extracts was evaluated using the procedures outlined in earlier publications [8,39,52,53]. Antioxidant experiments employed ascorbic acid as a positive control. In contrast, specific enzyme inhibitors, including arbutin, acarbose, and epigallocatechin gallate (EGCG), were utilized for the mushroom tyrosinase, α-glucosidase, and elastase enzyme assays, respectively. The percentage inhibition of DPPH-and ABTS-scavenging, mushroom tyrosinase, α-glucosidase, and elastase activity was calculated using Equation (3).
where Abs control and Abs sample are the absorbance of the control and absorbance of the sample, respectively. Each sample was examined three times. Each sample's 50% inhibitory concentration (IC 50 ) value was also computed to compare various extraction method efficacies.

Experimental Design of RSM for the Extraction Process
The hot extraction method was used to optimize the extraction procedure of polyphenolic compounds from URADP. The RSM model was designed to extract phenolic chemicals from URADP using ethanol concentration (X 1 ), extraction duration (X 2 ), and temperature (X 3 ) as independent process factors. Respondent factors included TPC and TFC (Y 1 and Y 2 , respectively). A three-component, five-layer CCD was employed for the extractions (supplementary data Table S1). The second-order polynomial model equation (Equation (4)) describes the link between independent factors and replies.
where Y is the response variable and X i and X j are the independent coded variables; β 0 denotes the constant coefficient, and β i , β ii , and β ij denote the coefficients of linear, quadratic, and interaction effects, respectively. Design Expert 11 was used for the RSM analysis and multiple linear regression (Stat-Ease, Minneapolis, Minnesota, USA). The model's adequacy was tested using the determination coefficient (R 2 ), the adjusted determination coefficient (Adj.R 2 ), and the lack of fit test. The F value with p < 0.05 indicated statistical significance. The interaction outcome of each factor on the response value was represented in three-dimensional (3D) surface plots.

Optimal Extraction Condition and Validation of the Model
Derringer's desire function was used to find the ideal conditions for maximizing all replies in a single experiment. Each response is turned into a unique desirability function ranging from 0 to 1 during this procedure. The component functions are then combined to create a total desirability function. The total desirability function is constructed using the following equation [4].
Response surface and desirability function analyses were used to determine the optimal extraction parameters. A triple experiment was carried out under ideal conditions, and the average experimental results were compared to the predicted results to verify the validity of the existing model. In addition, the experimental data were contrasted with the values that the model anticipated. Equation (5) was used to determine the relative standard deviation (RSD) and to compare the experimental and projected results.

RSD (%) =
Standard deviation between predicted and experimental values Mean values between predicted and experimental values × 100 The resulting data were analyzed and optimized for all response circumstances when the RSD% was <10. Additionally, the electrospray ionization mass spectrometry (ESI-MS)/MS profiles of phenolic compounds were found under ideal circumstances.

Analysis of Chemical Compounds by ESI-MS/MS
The Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific INC., San Jose, CA, USA) was used to conduct the negative (−) mode ESI-MS investigations. A 500 µL graded syringe (Hamilton Company Inc., Reno, NV, USA) and a 15 µL/min syringe pump (Model 11, Harvard, Holliston, MA, USA) were used to immerse the sample in the ESI source. The normal negative mode ESI-MS conditions were as follows: mass resolution of 140,000 (full width at half maximum, FWHM), sheath gas flow rate of 5, seep gas flow rate of 0, auxiliary gas flow rate of 0, spray voltage of 4.20 kV, capillary temperature of 320 • C, S-lens Rf level, and automatic gain control of 5 × 10 6 . The MS/MS studies were performed using the same instrument using three distinct stepwise normalized collision energies (10, 30, and 40) [5]. The Xcalibur 3.1 with Foundation 3.1 (Thermo Fisher Scientific Inc. Rockford, IL, USA) was used to process the collected mass spectral data. The m/z peaks were tentatively identified by comparing their calculated (exact) masses of deprotonated (M-H) adducts with the m/z values and ESI-MS/MS fragmentation patterns from the in-house MS/MS database and online databases such as FooDB [49], METLIN [50], CFM-ID 4.0 [48]. The chemical structure was drawn using ChemDraw Professional 15.0 (PerkinElmer, Waltham, MA, USA).

Statistical Analysis
All data were reported as the mean ± standard deviation of at least three independent experiments (n = 3), each with three sample replicates. One-way analysis of variance (ANOVA), followed by Dunnett's multiple comparison test, was executed using SigmaPlot Version 12.5 (Systat Software, Inc., Chicago, IL, USA) to determine statistical significance at p < 0.001, p < 0.01, and p < 0.05. Principle component analysis (PCA) was performed to analyze the effect of the extraction method on TPC, TFC, antioxidant, mushroom tyrosinase, α-glucosidase and elastase enzyme inhibition and to learn the correlations between these variables. PCA was carried out using Minitab Statistical Software (Version 18.0, Minitab Inc., Enterprise Drive State College, PA, USA).

Conclusions
This study was the first investigation on optimizing the solvent extraction conditions on URADP using RSM, and high-resolution mass spectroscopic analysis revealed the presence of phenolic acids, flavonoids, lignans, etc. Optimal conditions (52% ethanol, extraction time of 81 min, and extraction temperature of 63 • C) were determined. Under these conditions, the maximum TPC and TFC were obtained as 24.25 mgGAE/g and 23.98 mgCAE/g, respectively. Optimized extract (OP) and heat extract made using 100% methanol (HM) also showed significant antioxidant and anti-tyrosinase enzyme activity compared to other extracts. Furthermore, on the basis of their bioactive components and biological activities, chemometric analysis showed a substantial association between the HM and OP by grouping them together. However, the mechanism underlying URADP's antioxidant and depigmenting actions is still unknown. The antioxidant and depigmenting actions of URADP are still being confirmed in investigations using cells and animal models. Based on these outcomes, we can conclude that these findings can be used as the basis for a broad commercial application of URADP, a promising candidate for an antioxidant and tyrosinase as enzymatic inhibition functional food, in nutraceutical food and pharmaceutical industries.  Data Availability Statement: Upon request, Authors will provide the data.