Optimization of the Process for Obtaining Antioxidant Protein Hydrolysates from Pumpkin Seed Oil Cake Using Response Surface Methodology

Abstract

Pumpkin seed cake, a byproduct of cold-pressed oil production, represents a food waste material with a great potential for valorization. The objective of the present study is to optimize the papain enzymatic hydrolysis process of pumpkin seed cold-pressed oil cake (CPC) to obtain protein hydrolysates with the highest antioxidant activity. Box–Behnken Response Surface Methodology (RSM) was used to optimize the simultaneous effects of an enzyme concentration of papain, a temperature, and a reaction time on the process of enzymatic hydrolysis on pumpkin seed cold-pressed oil cake (CPC). For these three input factors, different values are used—1, 2, and 3% for papain concentration, 20, 30, and 40 °C for temperature, and 60, 120, and 180 min for hydrolysis time. Thus, the design generated a total of 21 experimental runs. The aim is to obtain protein hydrolysates with the highest antioxidant activity. The responses DPPH and ABTS were calculated and the determined regression models were statistically analyzed and validated. The results revealed that optimal conditions included a papain concentration of 1.0%, a temperature of 40 °C, and a hydrolysis time of 60 min to retrieve the highest level of bioactive compounds.

1. Introduction

Pumpkin (Cucurbita pepo; Cucurbita maxima; Cucurbita moschata) is a traditional culture for Bulgaria with a wide culinary and industrial application. The increased interest in the consumption of healthy and minimally processed foods has also led to the increase in the production and consumption of cold-pressed pumpkin seed oil, which is rich in bioactive compounds such as unsaturated fatty acids (mostly linoleic and oleic acid), tocopherols, and phytosterols [1,2]. The utilization of the protein-rich residue in the production of this oil has great potential for valorization [3,4]. Currently, the pumpkin seed cold-pressed oil cake (CPC) is mainly used for animal feed or crop fertilizer, resulting in a great waste of resources [4]. However, due to the high protein content, availability, and low cost, CPC is a valuable alternative raw material for obtaining protein isolates, protein hydrolysates, and bioactive compounds (mainly oligopeptides) that possess a wide range of dietary and therapeutic functions. The major proteins in pumpkin seeds are globulins accompanied by glutelins and smaller amounts of albumins and prolamins [5,6].

The pumpkin seed protein isolate is rich in essential amino acids and contains proteins of good quality in terms of digestibility and nutritional properties and can be consumed as a dietary supplement or incorporated into other food products [7]. Vinayashree and Vasu found that the functional properties of the protein isolate from Cucurbita moschata seeds were compatible with those of soy protein [8]. Methods for the separation of proteins from defatted pumpkin seeds meal include extraction at pH 9.0 and fractionation with sodium chloride solutions of different concentrations. Microwave and ultrasonic extraction were also experimented, with the highest protein yield found in alkaline extraction with ultrasonic treatment [9,10].

On the other hand, a lot of research has shown that pumpkin seed proteins can be considered an innovative source of bioactive peptides with beneficial effects on human health [11,12]. Enzymatic hydrolysis is preferable because it achieves mild processing conditions and more specific protein cleavage than chemical hydrolysis. As a result of the enzymatic hydrolysis of the pumpkin protein, peptides of small size are obtained, which have been reported to have antioxidant, antihypertensive, and antihyperglycemic activities [11,12,13,14]. The main factors influencing the process are the type of enzyme used, enzyme/substrate (E/S) ratio, pH, temperature, and hydrolysis time. These factors affect not only the degree of hydrolysis, but also the molecular weight distribution of the peptides in the hydrolysates and their bioactive and functional properties [13].

In published studies, the proteases commonly used to hydrolyze pumpkin seed proteins are of animal (pepsin, trypsin, chymotrypsin) or microbial origin (Alcalase, Neutrase, Flavourzyme) [12,14,15]. Venuste et al. analyzed the influence of four different microbial enzymes (Alcalase, Flavourzyme, Protamex, and Neutrase) on the protein hydrolysis process of Cucurbita moschata seeds [16]. They found that Alcalase was the most suitable of all the enzymes tested to achieve the highest degree of hydrolysis and the highest proportion of low molecular weight peptides in the hydrolysate [16].

Enzymatic hydrolysis of cucurbitin (11S globulin) obtained from CPC (Cucurbita pepo L. c.v. Olinka) with Alcalase, Flavourzyme, and pepsin was carried out under optimal reaction conditions for each enzyme. Alcalase treatment was found to result in a higher degree of protein hydrolysis than pepsin and Flavourzyme. The hydrolysates obtained by Alcalase and pepsin showed antioxidant activity whereas Flavourzyme hydrolysates did not. The authors suggest that this is due to the specificity of the enzymes, which hydrolyze different peptide bonds and, accordingly, the obtained hydrolysates have a different peptide composition [15]. Nourmohammadi et al. experimented with the hydrolysis of protein from defatted pumpkin meal by using two enzymes—trypsin and Alcalase and varying enzyme concentration, temperature, and hydrolysis time. Antioxidant activity was higher in hydrolysates obtained by Alcalase treatment [14].

The possibilities of the enzymatic hydrolysis of CPC proteins by the proteases of plant origin (especially papain) have been less investigated. Lu et al. applied hydrolysis with five different proteases of pumpkin seed protein and found that papain treatment produced peptides that had the highest zinc ion binding capacity and could be used as a functional food supplement providing high zinc bioavailability [17]. In Ningrum et al., 2023, papain was applied to obtain protein hydrolysates from okara (a solid byproduct created during the processing of soy milk). The authors also found that the amount of enzyme and the period of hydrolysis had a significant effect on the antioxidant activity [18].

Papain is a cysteine protease isolated from Carica papaya and is classified as a proteolytic enzyme that requires a free sulfhydryl group for activity. It has been extensively studied for diverse applications in the food industry, meat tenderizing, drug design, and pharmaceutical preparations [19].

This study aimed to optimize the papain enzymatic hydrolysis process of CPC to obtain protein hydrolysates with the highest antioxidant activity. The Box–Behnken design was applied to model prediction and to optimize the hydrolysis conditions (i.e., the main process variables temperature, time, and enzyme concentration (E/S ratio)). All the scientific studies cited so far on the preparation of bioactive hydrolysates from CPC involve a preliminary step of protein extraction and concentration. To the best of our knowledge, no studies have been conducted on the direct enzymatic hydrolysis of proteins in defatted pumpkin seed flour.

Therefore, the present work can serve as a foundation for other researchers.

2. Materials and Methods

2.1. Materials

Pumpkin seed (Cucurbita pepo L.) cold-pressed oil cake (CPC) with particle sizes below 0.500 mm was obtained from the local company (VA Trade Ltd., Stryklevo village, Bulgaria) that used thecold-pressed technology for the production of vegetable oils. The pumpkin fruits were harvested from the Silistra (Bulgaria) region in autumn 2022. Papain EC 3.4.22.2, activity—67,550 U/g, supplied by MP Biomedicals, LLC (Santa Ana, CA, USA); DPPH (1,1-diphenyl-2-picrylhydrazyl), ABTS (2,2′-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid), Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), and methanol were provided by Merck KGaA (Darmstadt, Germany). All the reagents and chemicals used were analytical grade.

2.2. Physicochemical Analysis of CPC

Moisture content—by express method with Sartorius Thermo Control YTC 01L balance;

Total protein content—automatically, with a Kjeldahl protein determination apparatus UDK-129 Distillation Unit (VELP Scientifica), according to ISO 1871:2009 [20] and the manufacturer’s instructions;

Total fats—by extraction with hexane in a Soxtec Avanti 2055 apparatus (Foss Tecator, Denmark), according to EN ISO 659:2009 [21] and the manufacturer’s instructions;

Total ash—by sample mineralization in a muffle oven, according to ISO 936:1998 [22].

2.3. Enzymatic Hydrolysis

CPC samples were dispersed in phosphate buffer, 0.1 M, and pH 6.5 to obtain a mixture with a protein content of 6.25 g/100 mL. The enzyme (papain) was added in concentrations of 1, 2, or 3% of the amount of protein. Hydrolysis was carried out in the temperature range of 20 to 40 °C for 60 min, 120 min, or 180 min in a shaking water bath, VLSB18 (VWR International). After enzymatic hydrolysis, the samples were heated at 85 °C for 15 min to inactivate papain, cooled to room temperature, and centrifuged at 4250× g for 15 min at 4 °C. The precipitate was discarded and the supernatant was collected and filtered. The resulting liquid protein hydrolysates were frozen and lyophilized in a laboratory freeze-drier, model LYOBETA 6PL of the Telstar company (Barcelona, Spain). Lyophilized protein hydrolysate (LPH) samples were stored at (−28 °C) until analyses were performed.

2.4. Antioxidant Activity of Protein Hydrolysates (LPH)

2.4.1. DPPH Radical Scavenging Capacity

The analysis was carried out according to the method of Brand-Williams [23], with slight modifications. First, 0.250 g of LPH from all test groups was dissolved in 10 mL of distilled water. The aliquots of the solutions were diluted 25 times with 80% methanol. After 20 min of incubation at room temperature, they were centrifuged at 8240× g for 10 min at 10 °C (Beckman J2-21M, USA). The precipitate was discarded and the supernatant was collected and analyzed. Subsequently, 1.2 mL of DPPH solution in methanol (0.2 mM) was mixed with 1.8 mL of methanol and 1.0 mL of the respective sample. The mixture was homogenized and incubated in the dark at room temperature for 60 min. Absorbance was measured at 517 nm against methanol (Libra S 22 UV-Vis spectrophotometer, Biochrom, Holliston, MA, USA). In the control, the sample solution was replaced with 1.0 mL of 80% methanol. Standard solutions of Trolox (concentration from 1.0 to 15 µg/mL) were used as a positive control.

The following formula is used to calculate the DPPH radical inhibition percentage (%RSA):

$$\%\mathrm{R}\mathrm{S}\mathrm{A}\left(\mathrm{D}\mathrm{P}\mathrm{P}\mathrm{H}\right)=\frac{(Acontrol-Asample)}{Acontrol}\times 100$$

(1)

2.4.2. ABTS Radical Scavenging Capacity

The analysis was performed according to [24], with some modifications: The solutions of 7 mM ABTS (in 0.1 M acetate buffer, pH 4.5) and 2.45 mM potassium persulfate (in distilled water) were prepared. Equal amounts of the two solutions were mixed and the mixture was left in the dark at room temperature for 12–16 h, during which radical cation ABTS was generated. Before analysis, 4.0 mL of the ABTS solution was diluted to 140 mL with methanol to obtain an ABTS working solution with an absorbance of 0.700 ± 0.025 at 734 nm. A fresh ABTS working solution was prepared for each analysis.

First, 0.250 g of LPH from all experimental groups were dissolved in 10 mL of distilled water. The aliquots of the solutions were diluted 10 times with 80% methanol. After 20 min incubation at room temperature, the samples were centrifuged at 8240× g, for 10 min at 10 °C (Beckman J2-21M, Brea, CA, USA). The precipitate was discarded and the supernatant was collected and analyzed.

In addition, 2850 mL of ABTS working solution was mixed with 150 μL of the sample (supernatant) and allowed to stand in the dark for 60 min. Absorbance was measured at 734 nm against methanol. The control was developed in the same way but the sample solution was replaced with 150 μL of methanol. Standard solutions of Trolox (concentration from 6.25 to 125 µg/mL) were used as a positive control.

The following formula is used to calculate the ABTS radical inhibition percentage (%RSA):

$$\%\mathrm{R}\mathrm{S}\mathrm{A}\left(\mathrm{A}\mathrm{B}\mathrm{T}\mathrm{S}\right)=\frac{(Acontrol-Asample)}{Acontrol}\times 100$$

(2)

2.5. Experimental Design and Statistical Analysis

In this study, Response Surface Methodology (RSM) was used. Using the Box–Behnken design (BBD), the influence of three independent variables—Papain concentration (X₁), Temperature (X₂), and Hydrolysis time (X₃)—on the DPPH and ABTS radical scavenging activity of obtained LPH was investigated. The coded and actual levels of the independent variables used in the RSM design are listed in

Table 1. Coded and decoded levels of independent variables used in the RSM design.

Independent Variables	Symbols	Levels
		−1	0	+1
Papain concentration (%)	X1	1.0	2.0	3.0
Temperature (°C)	X2	20	30	40
Hydrolysis time (min)	X3	60	120	180

. The dependent variables in this study are DPPH and ABTS. They are denoted from Y₁ and Y₂, respectively. A second-order polynomial equation was used to express these dependent variables as a function of the independent variables as follows:

$$Y_j={\beta }_0+\sum _{i=0}^k{\beta }_i{X}_i+\sum _{i=1}^k{\beta }_{ii}{X}_i^2+\sum _{i>0}^k{\beta }_{ij}{X}_i{X}_j+E,$$

(3)

where Y_j (j = 1, 2) represents the responses to be modeled; β₀ is the constant coefficient; β_i is the coefficient of the linear effect; β_ij is the coefficient of the interaction effect; β_ii is the coefficient of the squared effect; k is the number of variables; and X_i and X_j define the independent variables (papain concentration (X₁), Temperature (X₂), and Hydrolysis time (X₃)). The statistical significance of the coefficients was verified using the Student’s t-test (α = 0.05), goodness-of-fit was established as the determination coefficient (R²), and the model consistency was established by the Fisher F test (α = 0.05).

The experimental design and statistical analysis were performed using Design-Expert software (Version 13.0.5.0, State-Ease Inc., Minneapolis, MN, USA). The complete experimental design consisted of 21 experimental runs, taken in a random order. The Center Points per Block were established at 5 to be able to estimate the pure error sum of squares.

3. Results and Discussion

3.1. Physicochemical Analysis of CPC

The moisture and residual lipid contents of CPC were 4.65 g/100 g and 12.76 g/100 g, respectively (

Table 2. Physicochemical analysis of CPC.

Composition	Content (g/100 g)
Moisture	4.65 ± 0.08
Fat	12.76 ± 0.02
Protein	46.98 ± 0.30
Ash	5.65 ± 0.61

Values represent means ± standard deviations (n = 3).

). These values coincide with those obtained in [9], while [25,26] reported higher values for residual lipids, 16.68 and 14.23 g/100 g, respectively. Data in the literature on the protein content of press cake from pumpkin seed oil production vary between 38.3 and 62.3% [9,25,26,27]. These differences are most likely due to the species and variety diversity and the climatic conditions during the cultivation of the plant raw material. The protein content is 46.98 g/100 g, which is in agreement with the results obtained by other researchers [26], and shows that CPC can successfully act as a protein substrate for the hydrolytic action of papain.

3.2. Optimization of the Enzymatic Hydrolysis Process

A Box–Behnken design (BBD) was used to analyze the experimental data. BBD belongs to Response Surface Methodology (RSM), which is a collection of statistical and mathematical techniques useful for process development, improvement, and optimization [28]. In general, RSM has two main types of designs—the Box–Behnken design (BBD) and the central composite design (CCD). BBDs differ from CCDs in that they use fewer series and only three levels, compared to CCD’s five [29]. For this reason, BBD was preferred in the present study.

All experiments were repeated three times and the means and standard deviations were calculated. Experimental data were evaluated statistically using Design-Expert software (version 13.0.5.0).

Table 3. Effect of independent variables X₁ (papain concentration), X₂ (temperature), and X₃ (hydrolysis time) on responses: DPPH radical scavenging activity (Y₁) and ABTS radical scavenging activity (Y₂) of obtained LPH.

Codded Variables			Response
X1	X2	X3	Y1: DPPH (RSA%)	Y2: ABTS (RSA%)
0	0	0	58.12	38.40
−1	1	0	77.08	35.86
0	0	0	54.28	37.18
0	−1	1	59.87	28.07
1	1	0	71.75	42.54
0	−1	−1	55.67	20.49
0	0	0	58.12	37.18
−1	0	1	54.94	42.10
0	0	0	54.28	38.40
−1	0	−1	54.56	34.87
0	0	0	56.10	37.18
0	0	0	58.12	38.40
0	0	0	56.10	38.40
0	1	−1	70.05	37.60
0	1	1	71.04	39.46
0	0	0	53.10	38.40
1	0	−1	58.04	37.77
−1	−1	0	49.48	29.26
0	0	0	56.10	37.18
1	−1	0	51.43	28.97
1	0	1	58.76	39.44

presents the results of the studies on the radical scavenging activity of the obtained hydrolysates, against DPPH and ABTS.

DPPH and ABTS tests are some of the most widely used methods to assess antioxidant activity. The radical scavenging activity to DPPH in the existing LPH sample varied from 49.48 to 77.08%. Under the conditions of the analysis, the concentration of the standard antioxidant Trolox, at which 50% inhibition of the DPPH radical is reached, is 13.07 µg/mL. When analyzed with ABTS, the RSA% values of the hydrolysates ranged from 20.49 to 42.54%. Trolox inhibited 50% of the ABTS radical at a concentration of 64.70 µg/mL. As can be seen from Table 3, all tested LPH samples inhibited both radicals, but to a different extent depending on the applied enzymatic hydrolysis parameters.

Table 4. Coded dependent variables used in the RSM design.

Dependent Variables	Symbols	Model
DPPH	Y1	Y1 = 56.36 − 2.26X1 − 3.94X1X2 − 8.80X2X3 + 5.71$X_2^2$
ABTS	Y2	Y2 = 37.05 − 2.38X1 − 1.98X2 − 2.58X3 − 3.09X2X3

summarizes the models obtained in this study. These models were evaluated based on the values of a lack of fit and coefficient of determination (R²). The significance of each coefficient in the model was determined using an F-test obtained from the analysis of variance (ANOVA) generated. Numerical optimization was conducted to obtain the optimal conditions for the enzymatic hydrolysis process.

The software product Design-Expert defines the dependence of DPPH on the input variables as quadratic (

Table 5. ANOVA for Quadratic model for Response 1: DPPH.

Source	Sum of Squares	df	Mean Square	F-Value	p-Value
Model	579.67	9	64.41	5.30	0.0060	significant
A-Papain concentration	40.70	1	40.70	3.35	0.0445
B-Temperature	0.1964	1	0.1964	0.0162	0.9012
C-hydrolysis time	2.18	1	2.18	0.1794	0.6801
AB	62.20	1	62.20	5.12	0.0450
AC	0.4970	1	0.4970	0.0409	0.8435
BC	309.64	1	309.64	25.47	0.0004
A2	17.31	1	17.31	1.42	0.2579
B2	151.65	1	151.65	12.47	0.0047
C2	1.28	1	1.28	0.1051	0.7519

), with statistically insignificant coefficients in front of the added members. The Model F-value of 5.30 implies the model is significant. There is only a 0.60% chance that an F-value this large could occur due to noise. P-values less than 0.0500 indicate model terms are significant. In this case, A, AB, BC, and B² are significant model terms.

The fit statistics for the obtained model are presented in

Table 6. Fit statistics for Response 1: DPPH.

Std. Dev.	3.49	R2	0.8125
Mean	58.00	Adjusted R2	0.6591
C.V. %	6.01	Predicted R2	0.8456
		Adeq Precision	7.9928

. The values of R2 and adjusted R² indicate a good explanation of the variability by the selected model for DPPH. The Adeq Precision value measures the signal-to-noise ratio and it must be greater than 4. The current ratio of 7.993 indicates an adequate signal. Therefore, this model can be used to navigate the design space.

The diagnosis of the model is realized through three types of graphs—a normal probability plot, a plot of the residuals versus the ascending predicted response values, and a graph of the predicted response values versus the actual response values—which are presented in Figure 1. The normal probability diagram (Figure 1a) shows whether the residuals follow a normal distribution. In this case, they follow a straight line with minor deviations, which shows that the residuals have a normal distribution. The residuals vs. predicted plot (Figure 1b) shows that the residuals are randomly located around the line residual = 0. This suggests that the resulting relationship model is reasonable. Also, no residue “stands out” from the underlying random pattern of residues. This assumes there are no outliers. Finally, the actual vs. predicted plot (Figure 1c) shows that the points are not quite close to the diagonal line, but are still within reasonable limits. This is understandable since R² = 0.8125.

In Figure 2, a series of model graphs is presented. The perturbation plot (Figure 2a) shows the influence of the input factors on the corresponding response. In this case, on the DPPH, the most significant influence is factor A (papain concentration), followed by factor B (temperature). The factor C line is almost parallel to the x-axis and therefore has no significant influence.

The response surface plots (Figure 2b–d) visualize the variation of the values of two independent variables within the experimental domain while holding the other one constant. Figure 2b reveals that the maximum value of DPPH can be achieved by keeping the temperature and papain concentration at 40 °C and 2.0%, respectively, while the hydrolysis time is chosen to be a minimum of 60 min. It appears that Factor C has the opposite effect on DPPH. Also shown with flags are the actual and model-predicted values at two experimental points, demonstrating the minimum prediction error. Figure 2c shows that the maximum value of DPPH can be achieved with a minimum hydrolysis time, papain concentration in the range of 1–2.5%, and maximum temperature.

Figure 2d reveals two regions with maximum DPPH values. Getting into one (bottom right) requires a hydrolysis time of over 90 min, a temperature of 20 °C degrees or less (which is outside the considered range), and a maximum papain concentration. To get into the other area (located on the opposite diagonal), a minimum time for hydrolysis (60 min), a temperature above 35 °C, and a minimum concentration of papain are required.

Two-factor interaction terms have been obtained to describe the dependence of ABTS on the input variables (

Table 7. ANOVA for 2FI model for Response 2: ABTS.

Source	Sum of Squares	df	Mean Square	F-Value	p-Value
Model	174.76	6	29.13	16.95	<0.0001	significant
A-Papain concentration	45.27	1	45.27	26.35	0.0002
B-Temperature	31.22	1	31.22	18.18	0.0008
C-hydrolysis time	53.40	1	53406	31.08	<0.0001
AB	0.0001	1	0.0001	0.0001	0.9942
AC	6.77	1	6.77	3.94	0.0671
BC	38.10	1	38.10	22.18	0.0003

). The Model F-value of 16.95 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. This model includes the A, B, C, and BC terms, which are significant.

The fit statistics for the obtained model are presented in

Table 8. Fit statistics for Response 2: ABTS.

Std. Dev.	1.31	R2	0.8790
Mean	37.05	Adjusted R2	0.8272
C.V.%	3.54	Predicted R2	0.6643
		Adeq Precision	15.8639

. The values of R² and adjusted R² indicate a good explanation of the variability by the selected model for ABTS. The Adeq Precision value of 15.8639 indicates an adequate signal. Therefore, this model can be used to navigate the design space.

The diagnosis of the model is shown in Figure 3. The normal probability plot (Figure 3a) shows that the residuals have a normal distribution because they follow a straight line with small deviations. The plot of the residuals against the predicted (Figure 3b) shows that the residuals are randomly located around the line residual = 0. This suggests that the resulting relationship pattern is reasonable. Also, no residues “stand out” from the random residue pattern. This assumes there are no outliers. Finally, the actual versus predicted plot (Figure 3c) shows that the points are close to the diagonal line.

In Figure 4, a series of model graphs is presented. The perturbation plot (Figure 4a) shows that all three factors have approximately the same influence on the response ABTS, as they have almost the same slope. Figure 4b reveals that the maximum value of ABTS can be achieved by keeping the temperature and papain concentration at 40 °C and 1.0%, respectively, while the hydrolysis time is chosen to be a minimum of 60 min. Figure 4c shows that the maximum value of ABTS can be achieved with a maximum hydrolysis time (180 min), papain concentration in the range of 1–1.5%, and minimum temperature. From Figure 4d, it can be seen that the maximum ABTS value is obtained in the following conditions: the minimum temperature value (20 °C), maximum hydrolysis time (180 min), and 1.0% papain concentration, or at the maximum temperature value (40 °C), minimum hydrolysis time (60 min), and 1.0% papain concentration.

A numerical optimization, which finds a point that maximizes the desirability function, was made to find the optimal process conditions.

Table 9. Numerical optimization solutions.

Number	Papain Concentration	Temperature	Hydrolysis Time	DPPH	ABTS	Desirability
1	−1.000	1.000	−1.000	76,381	41,822	0.860	Selected
2	−1.000	1.000	−1.000	76,381	41,822	0.860
3	−0.985	1.000	−1.000	76,342	41,806	0.859
4	−1.000	1.000	−0.993	76,305	41,791	0.858
5	−0.966	1.000	−1.000	76,287	41,785	0.857
6	−0.944	1.000	−1.000	76,226	41,762	0.855
7	−1.000	0.992	−1.000	76,183	41,813	0.854
8	−0.925	1.000	−1.000	76,169	41,741	0.853
9	−1.000	1.000	−0.966	76,019	41,674	0.849
10	−0.863	1.000	−1.000	75,976	41,675	0.847

presents the specific optimum conditions for obtaining the highest level of bioactive compounds. The Design Expert software returns a table of 100 possible solutions. For brevity, only the first ten of them are presented here.

4. Conclusions

In this work, Response Surface Methodology (RSM) was used to optimize the process of the enzymatic hydrolysis of pumpkin seed cold-pressed oil cake (CPC) to obtain protein hydrolysates with high antioxidant activity. The hydrolysis of CPC proteins was carried out with the enzyme papain using a Box–Behnken experimental design with three variables and three levels. Using the response surface method, the optimal hydrolysis conditions were found to be the following: a papain concentration of 1.0%, temperature of 40 °C, and hydrolysis time of 60 min, which resulted in the maximum DPPH and ABTS radical scavenging activity. These results provide a basis for future research in the field of food technology and the development of new functional foods and nutritional supplements with a positive impact on human health.