Next Article in Journal
Impacts of Low-Order Stream Connectivity Restoration Projects on Aquatic Habitat and Fish Diversity
Previous Article in Journal
Capture and Maintenance of Balistes capriscus for Aquaculture and Conservation
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Modeling the Hydrolysis of Soybean Flour Proteins Digested with Gastric Proteases of the Marine Fish Sparus aurata and Commercial Non-Starch Polysaccharidases

1
Departamento de Biología, Escuela de Biología, Facultad de Ciencias Naturales y Exactas, Universidad Autónoma de Chiriquí, David 0426, Chiriquí, Panama
2
Centro de Investigación, Innovación y Creación (CIIC-UCT) Núcleo de Investigación en Producción Alimentaria Facultad de Recursos Naturales, Universidad Católica de Temuco, Temuco 4781312, Chile
3
Centro de Investigación en Agrosistemas Intensivos Mediterráneos y Biotecnología Agroalimentaria (CIAMBITAL), Departamento de Biología y Geología, Universidad de Almería, 04120 Almería, Spain
*
Authors to whom correspondence should be addressed.
Fishes 2025, 10(7), 320; https://doi.org/10.3390/fishes10070320
Submission received: 28 April 2025 / Revised: 23 June 2025 / Accepted: 24 June 2025 / Published: 2 July 2025
(This article belongs to the Section Nutrition and Feeding)

Abstract

Soybean flours are widely used as a protein-rich ingredient in fish aquafeeds, and to obtain value-added compounds after a previous treatment with proteases. Additionally, non-starch polysaccharidases (NSPases) enhance dietary protein bioaccessibility and have been used as feed additives. In this study, defatted soybean flour was hydrolyzed using Sparus aurata gastric proteases and varying doses of a commercial blend of acidic NSPases. Reactions occurred at 25 °C for 3 h under typical fish stomach pH conditions (3.5–5.6). We modeled the hydrolytic process using response surface methodology, focusing on the released peptides and carbohydrates. The main finding was the efficient control of the degree of protein hydrolysis. We achieved 6–25% hydrolysis for peptides below 10 kDa by adjusting the carbohydrase dose and reaction pH. This work confirms that acidic commercial NSPases improve soybean flour protein hydrolysis when combined with S. aurata gastric proteases.
Key Contribution: Acidic non-starch polysaccharidases efficiently improve the digestion of soybean flour proteins with gastric proteases of Sparus aurata at a physiological temperature.

1. Introduction

Soy is one of the most important crops in terms of production volume, production area, and protein yield per hectare [1]. Soybean seeds yield food products such as soy milk and okara. However, much of the world’s crop yields soybean oil and protein-enriched foods. These include defatted soybean flour (DSBF) [2], soybean protein concentrates (SBPC), and isolates (SBPI) [3]. These protein ingredients are frequently used at high inclusion rates in fish diets [4]. They appear in aquafeeds for salmonids [5,6] and marine species such as gilthead seabream (Sparus aurata) [7]. DSBF is especially rich in non-starch polysaccharides (NSPs) [2]. These compounds comprise about one-third of the soybean cotyledons [8] and can protect soybean proteins from digestive protease activity [9,10]. Robaina et al. [11] reported a 6% decrease in protein digestibility. This occurred when soybean meal inclusion increased from 0% to 30% in juvenile Sparus aurata diets. The problem was not only the decrease in digestibility but also the decrease in growth rate due to the antinutritional effects of soybean meal, including NSPs. Consequently, Robaina et al. [11] recommended that the dietary content of soybean meal should be below 30%.
Supplementing fish diets with exogenous non-starch polysaccharidases (NSPases), including commercial blends, can overcome NSPs’ antinutritional effects [2,12]. Some authors have added NSPases to suspensions of plant-seed ingredients in order to extract proteins, which were hydrolyzed during a subsequent step [13]. Thus, plant cell wall NSPs can affect the digestibility of dietary proteins in animals. On the other hand, plant primary cell walls contain glycoproteins with structural function [14], whose polypeptide backbones can hardly be attacked by digestive carbohydrases.
Commercial NSPases, particularly fungal ones, are mainly acidic. Their optimal pH falls within the acid range. Therefore, they should be active during gastric digestion. Stites et al. [15] recently reported increased protein digestibility in Trachinotus carolinus. This occurred when fish ate a soybean meal-based diet supplemented with acidic endo-xylanases and endo-glucanases. Romero-García et al. [16] found that pretreating a microalgae-based ingredient with acidic NSPases (Viscozyme L®) made proteins more accessible to subsequent alkaline proteases. This increased proteolysis yield from 42% to 76% in in vitro assays.
Digestion in S. aurata occurs first under acidic stomach conditions, similar to many other marine fish. Considering this, our main goal was to test acidic NSPases. We combined them with S. aurata gastric proteases to model their potential effects on protein digestion in vitro.
Response-surface methodology (RSM) is an effective statistical tool for improving multifactor processes. RSMs include a collection of statistical and mathematical tools that are useful for describing and optimizing the yield of a process by performing a minimal number of experiments [17]. This methodology is particularly appropriate for studies with many factors and potential interactions. RSMs can be combined with multivariate statistics to analyze several dependent variables simultaneously. For example, optimizing a group of dependent variables is possible even when the optimal conditions for single variables do not coincide [18]. RSM has been used to model the human digestion process, considering factors like pH and enzymatic dose [19] or the enzymatic hydrolysis of legume flours [12]. Therefore, another goal of this investigation was to apply the RSM to model the hydrolysis of DSBF with the gastric proteases of S. aurata in the presence of commercial NSPases.

2. Materials and Methods

2.1. Enzymes and Substrates

The ingredient under investigation was defatted soybean flour (Glycine max), sourced from a local supplier (ESASA, Valladolid, Spain). The flour comprised small particles with an average diameter of 0.5 mm and a dry matter content of 92.0%. According to the supplier, its proximate composition was 48% crude protein, 2.0% crude lipid, 4% crude fiber, and 6% ash.
Acid proteases were extracted from the gastric contents of 30 gilthead seabream (Sparus aurata) weighing between 100 and 600 g. Fish were obtained from a local commercial fish farm immediately before market dispatch to ensure freshness. Stomachs were homogenized in 5 mM Gly-HCl buffer, pH 2.5 (1:3 w/v ratio), using an IKA Ultraturrax T18 homogenizer and subsequently centrifuged at 12,000 rpm for 4 °C. The supernatants were collected individually and stored. Protease activity in each extract was determined using the Anson method [20] (detailed in Section 2.5).
The commercial carbohydrase mixture used was Viscozyme® L (Novozymes Inc., Bagsvaerd, Denmark). This product contained various hydrolytic activities, including cellulase, hemicellulase, xylanase, and arabinase. Viscozyme L exhibited an activity of 112 fungal β-glucanase units (FBGU) per gram. One FBGU is defined as the activity of carbohydrases producing a reducing power equivalent to 1 µmol of glucose per minute when acting on a 0.083% barley β-glucan solution at pH 5.0 and 30 °C.
To understand the effect of non-starch polysaccharide (NSP)-degrading enzymes on response variables (detailed in Section 2.4), several preliminary assays were conducted. In all cases, a control (with its corresponding blank) was run without Viscozyme L, whereas treatments included a dose of Viscozyme L within the studied pH range. The results indicated that Viscozyme supplementation accelerated the release of amino acids and protein solubilization from soybean flour during in vitro acid hydrolysis by seabream gastric enzymes. In addition, the percentage of solubilized dry matter and the degree of hydrolysis of NSPs increased. Based on these preliminary findings, modeling of soybean flour hydrolysis was performed.

2.2. Experimental Design

The experiment followed a central composite design (CCD) with two factors and two levels per factor for the factorial points and with the addition of central and axial points (Supplementary Material Figure S1). This constituted a 2 × 2 CCD according to Montgomery [21]. The factors investigated were (i) the pH of the reaction medium and (ii) the dose of Viscozyme L. The central point was replicated 12 times, while factorial and axial points were replicated twice (Supplementary Material Figure S1), resulting in 28 experimental runs.

2.3. Setting Factor Levels

The minimum dose of Viscozyme L was determined in a preliminary unifactorial experiment, based on the method described in [22]. This preliminary assay involved hydrolyzing 150 mg of defatted soybean flour (DSBF) with increasing doses of Viscozyme L at 25 °C and pH 5.5 for 180 min. The amount of reducing sugars released after 180 min was measured, and a range of carbohydrase doses within the plateau region were selected (Supplementary Material Figure S2).
The pH levels tested were chosen based on the optimal pH range for Viscozyme L (3.3–5.5), as provided by the supplier. This range aligns well with the physiological gastric pH during digestion in S. aurata, which is reported to be 3.6–5.6 [23]. The caseinolytic or hemoglobinolytic activity of fish gastric proteases is high at pH 3.5 (50–75% of maximum) and low at pH 5.5 (approximately 10% of maximum) [24,25].

2.4. Protocol for Each Experimental Run

For each experimental run, blanks and controls were prepared in addition to the treatment assays. Controls contained the following: reaction medium, substrate (150 mg of defatted soybean flour), and S. aurata gastric enzyme extract. The control blank was identical, but the gastric extract was previously inactivated. For each treatment assay, a corresponding blank (referred to as “black treat”) was prepared containing reaction medium, substrate, S. aurata gastric extract, and the specific Viscozyme L dose, with all enzymes inactivated at time 0. In all cases, the absorbance of the respective blanks was subtracted from that of the control or treatment assay.
Hydrolytic reactions were carried out in 100 mM citrate buffer supplemented with an aliquot of S. aurata gastric extract equivalent to 4.62 U of protease activity (1 U = 1 µmol Tyr/min), representing a volume of 0.5 mL per assay. The final reaction volume was 3 mL per assay. The substrate consisted of 150 mg of DSBF, and the protease/substrate ratio was fixed at 64 U/g protein. This ratio was calculated by measuring the total protease activity per stomach (according to the Anson protocol, see below) in 10 fish weighing between 100 and 250 g. The result was then divided by the estimated protein ingestion, assuming a meal ration of 1.5% of body weight and a crude protein content in the diet of 45%. The Viscozyme L dose varied between 4.6 and 10.3 FBGU per assay, which corresponded to approximately 1.1–2.5% (v/v) dilution of the commercial blend. The pH was adjusted to values between 3.2 and 6.0 as dictated by the central composite design. Reactions were maintained in a rotatory shaker at 5 cycles per minute for 3 h at 25 °C, a temperature within the thermal preference range for juvenile S. aurata [26]. This physicochemical environment allows fish pepsins to operate under physiological gastric conditions, promoting the production of small polypeptides [27]. At the end of the digestion process, the assay containers were placed on ice for 10 min, then centrifuged at 4000 rpm and 4 °C for 15 min. A 500 µL subsample of the supernatant was ultra-filtered using VIVASPIN tubes with a 10 kDa molecular weight cut-off (MWCO). Thus, each in vitro digestion assay yielded three subsamples for analysis: (i) ultrafiltrate of the reaction medium supernatant, (ii) supernatant of the ultrafiltrate, and (iii) solid pellet in the reactor.
The dependent variables measured in the ultrafiltrate (soluble molecules below 10 kDa) were as follows: (i) degree of hydrolysis of proteins, measured as amino acids and small peptides, DH(aa), based on the reaction between amino acids and OPA (ortho-phthaldialdehyde); (ii) degree of hydrolysis of NSPs, measured as reducing sugars, DH(rs), based on the DNSA (dinitrosalicylic acid) assay; and (iii) degree of hydrolysis of NSPs, measured as pentoses, DH(pen), based on the phloroglucinol assay. The dependent variables measured in the supernatant of the ultrafiltrate (soluble molecules above 10 kDa) were as follows: (i) the percentage of soluble proteins in the assay supernatant relative to the total proteins in the substrate (excluding proteins in the enzymatic extracts), Sprot, determined according to Bradford [28]; (ii) the mass (mg) of solubilized peptides in the assay supernatant, measured with the OPA reaction and calculated as the difference between the experimental and control assays. Finally, the dependent variable measured in the pellet was the percentage of solubilized/hydrolyzed dry matter (SHDM) relative to the dry matter content in the original ingredient (defatted soybean meal).

2.5. Measurement Protocols for Biochemical Variables

The activity of each extract was determined according to Anson [20]. Briefly, the extract was incubated with 5 g L-1 bovine hemoglobin in 100 mM Gly-HCl buffer pH 2.5 at 25 °C for 20 min. The enzymatic assay was stopped by adding 20% (w/v) TCA, centrifuged at 12,000 rpm and 2 °C for 15 min, and the absorbance of the supernatant at 280 nm was determined using a microplate reader. Absorbance values were compared to calibration curves prepared using commercial L-tyrosine solutions of known concentrations.
The degree of hydrolysis of proteins, DH(aa), was determined using the modified OPA method described by Nielsen et al. [29]. The OPA method measures free amino acids and/or short peptides with free amino groups. The method was adapted to the experimental conditions: 30 µL of sample or blank and 225 µL of OPA reagent were added to each of three wells per sample or blank (96-well microplates) and allowed to react for 3 min at room temperature. The absorbance was measured at 340 nm. The %DH was calculated as described by Adler-Nissen [30]. Free amino groups were assayed using OPA methods and converted to ‘hydrolysis equivalents’ (h mEquivalents α-NH2 per g of protein N × 6.25) using a calibration curve. From the value of h, the degree of hydrolysis was calculated according to the formula:
D H a a ( % ) = 100 × h h t o t a l
h = [ S e r i n e N H 2 ] β α
[ S e r i n e N H 2 ] = O D t r e a t O D b l a n k m + b × d f × V r × 1 P
where h is the number of hydrolyzed bonds in solubilized peptides below 10 kDa (mEquivalents per g of protein N × 6.25); htotal is the total number of peptide bonds per protein equivalent (7.8 mEq g−1 specific to soy protein); [Serine NH2] was the concentration of serine (mEq g−1); α and β are estimated to be 1.00 and 0.400, respectively. [Serine NH2] is calculated from the 340 nm absorbance measurement of the samples compared to a standard using a calibration line (ΔOD340 nm = m[Serine NH2](mEq L−1) + b, where m = 0.5409, b = −0.0213, R2 = 0.9982); df = dilution factor; P = protein (g) in the reaction reactor with a final volume of 0.003 L (Vr).
This DH(aa)(%) represents the percentage of amino groups in small (<10 kDa) soluble molecules resulting from enzymatic activities, relative to the total potentially hydrolyzable amino groups in insoluble and soluble proteins, plus large (>10 kDa) peptides present in the reactor at the beginning of the reaction.
The degree of hydrolysis of NSP based on the reducing sugars, DH(rs), was calculated as
D H r s = 100 r s c o n t r s t r e a t r s c o n t
where rscont is the amount of reducing sugars in the pellet plus reducing sugars above 10 kDa in the supernatant of the control assay; rstreat is the amount of reducing sugars in the pellet plus reducing sugars above 10 kDa in the supernatant of the treatment assay (after in vitro digestion). Pellets from the control and treatment assays were subjected to a saccharification process consisting of strong chemical hydrolysis with 2M H2SO4 at 100 °C for 60 min, and the content of reducing sugars was determined according to the dinitrosalycilic acid (DNSA) protocol [31]. The reducing sugar content in the filtrate of the control and treatment assays was also quantified following the DNSA protocol. Finally, rscont and rstreat were calculated as follows: (reducing sugars in 150 mg of soybean flour) − (reducing sugars below 10 kDa in the filtrate of 3 mL). For the DNSA protocol, the reaction medium consisted in 30 μL of the filtrate added to 300 μL of 50 mM buffer. Afterwards, 150 μL of DNSA reagent was added, and the mixture was incubated at 100 °C for 10 min, then cooled at ambient temperature for 15 min. The mix was diluted with 1.5 mL of distilled water, and the increase in absorbance at 530 nm was measured.
The degree of hydrolysis of NSP’s based on pentoses, DH(pen), was calculated following the same protocol used for DH(rs), but pentoses were determined instead of reducing sugars. The formula used was
D H p e n = 100 P e n c o n t P e n t r e a t P e n c o n t
where Pencont is the amount of pentoses in the pellet plus pentoses above 10 kDa in the supernatant of the control assay; Pentreat is the amount of pentoses in the pellet plus pentoses above 10 kDa in the supernatant of the treatment assay (after in vitro digestion). After saccharification of the control and treatment pellets (see section for DH(rs)), the concentration of pentoses in the filtrates (<10 KDa) was measured according to the protocol described by Eberts et al. [32]. Briefly, the reaction medium comprised 50 μL of the sample under analysis, 50 μL of distilled water, and 1.9 mL of phloroglucinol reagent. The reagent consisted of 1 g of phloroglucinol in 200 mL glacial acetic acid plus 20 mL concentrated HCl. Each reaction mixture was incubated at 100 °C for 5 min and cooled in water for 10 min; afterwards, the absorbance at 554 nm was measured. A calibration line was obtained from a series of D-xylose dilutions.
The soluble protein content was measured according to the Bradford method [28] in the supernatant of the ultrafiltrate (soluble molecules above 10 kDa). Briefly, 10 μL of the supernatants was added to 190 μL of Bradford reagent [28], and the absorbance at 595 nm was read in microplates after incubating 10 min at the ambient temperature. No detergents or substances known to significantly interfere with the Bradford method were added to the samples. The calibration curve was constructed by subjecting solutions of different concentrations of bovine serum albumin (BSA) in distilled water to the same protocol. The percentage of soluble protein in the supernatants was calculated as [12]
S P r o t = 100 S o l u b l e   p r o t e i n   i n   t h e   s u p e r n a t a n t p r o t e i n   i n   t h e   e n z y m e T o t a l   p r o t e i n   o f   d e f a t t e d   s o y b e a n   f l o u r   i n   t h e   a s s a y
The mass (mg) of solubilized peptides in the supernatant of the ultrafiltrate was measured using the adapted OPA method [29] described for DH(aa) determination. The absorbance readings were compared with a calibration line based on a series of L-serine solutions. The mass of solubilized peptides was calculated using the following equation:
S P e p   m g = m g   p e p t i d e s   i n   t h e   s u p e r n a t a n t   o f   t r e a t m e n t   a s s a y m g   p e p t i d e s   i n   t h e   s u p e r n a t a n t   o f   c o n t r o l   a s s a y
To determine the percentage of solubilized matter, dry matter in the pellet obtained after in vitro digestion was measured. Finally, the variable SHDM was calculated as
S H D M = 100 D M D S B F D M p e l l e t D M D S B F
where DMDSBF is the dry matter of the flour initially added to the in vitro reactor; DMpellet is the dry matter in the pellet within the reactor after in vitro digestion.

2.6. Validation Assays

Validation assays (in triplicate) were conducted similarly to the previous runs for the central composite model, but with the pH and Viscozyme L dose set at 3.6 and 6.7 FBGU, respectively. The observed responses for dependent variables (DH(pen) was not measured) are presented in Supplementary Tables S9 and S10 and are compared with the confidence and tolerance intervals associated with the CCD analysis.

2.7. Data Analysis

Bifactorial regression linear models for second-order surface response of single dependent variables were obtained following the criteria sequence outlined in [17]: higher F0 of the model → lower F0 of loss of adjustment → higher R2 adjusted → Adeq. precision (assessing the signal-to-noise ratio, where a value > 4 indicates a reliable parameter space [17]). The p-values of the terms in the model equation were also reviewed (significance at p < 0.05). The equation chosen for each model was refined by discarding terms with a p-value > 0.05 to obtain the simplest model that suitably describes the hydrolysis process. Finally, univariate models were plotted as 2-axis (pH and Viscozyme L dose) contour plots.

3. Results

3.1. Measurements Derived from Chemical Analysis of the Filtrate

3.1.1. Degree of Hydrolysis of Proteins Based on Amino Acids and Small Polypeptides (<10 kDa), DH(aa)

The design matrix for the whole experiment, and experimental responses for all dependent variables are shown in Supplementary Table S1.
The data for the DH variable (aa) were normalized using the Box–Cox transformation: DH(aa)’ = ln(DH(aa) + 0.12); for lambda = 0 [21]. The model fitting for the transformed DH(aa) was highly significant (p = 0.001) (Supplementary Table S2), with an adequate precision exceeding 24. The adjusted R2 was above 80%. Although the lack of fit suggested that including cubic terms could improve the fit, central composite models do not provide single solutions for cubic terms [17]. Therefore, the linear model was selected because the second-order terms were not significant. According to this model, both factors, pH and Viscozyme L dose, exerted significant effects.
The response surface appeared nearly planar, without significant curvature, within the studied intervals of pH and Viscozyme L dose (Figure 1). DH(aa) increased as pH decreased and as the enzymatic dose increased, with both effects appearing additive (second-order terms and the interaction were not significant). The values of DH(aa) ranged from 6 to 20% of the initial dietary protein levels. The following equation was selected to describe the response surface:
ln D H a a + 0.12 = 3.08 0.30   p H + 0.11   D o s e

3.1.2. Degree of Hydrolysis of NSPs Based on Reducing Sugars, DH(rs)

Supplementary Table S3 presents the ANOVA results for DH(rs). The fitting of a second-order model was highly significant (p < 0.001), and adequate precision showed a suitable value above 20. The adjusted R2 was 95%, and the lack of fit was non-significant. Therefore, the second-order model was selected.
The response surface of DH(rs) exhibited a clear curvature with a maximum value (Figure 2). This curvature was apparent with respect to both factors: pH and Viscozyme L dose. Analysis of the stationary point [17] indicated that the conditions for maximizing DH(rs) were a Viscozyme L dose of 7.8 FBGU and a pH of 3.8. The maximum DH(rs) value at this point was 84.4%. Within the factor region studied, DH(rs) ranged from 65% to 84%. Lower values were obtained at high pH (5.5, 5.8) and at lower or higher Viscozyme L doses (5.0, 9.5 FBGU). Notably, DH(rs) was positively and significantly correlated with DH(aa) (Table 1). The equation for the response surface of DH(rs) is
D H r s = 52.02 + 31.23   p H + 19.69   D o s e 0.56   p H × D o s e 3.54   p H 2 1.12   D o s e 2

3.1.3. Degree of Hydrolysis of NSP’s Based on Pentoses, DH(pen)

The ANOVA results for the dependent variable DH(pen) are presented in Supplementary Table S4. The quadratic model was highly significant (p < 0.001), and the adequate precision was higher than 14. The adjusted R2 was close to 75%, and the lack of fit was not significant. Thus, the second-order model was selected.
The response surface of DH(pen) resembled that of DH(rs) (Figure 3). It displayed curvatures with respect to both factors (pH and dose), exhibiting a maximum value. Analysis of the stationary point indicated that the conditions for achieving maximum DH(pen) were a Viscozyme L dose of 8.51 FBGU at a pH value of 4.53. At this point, DH(pen) reached a maximal value of 85.9%. Furthermore, DH(pen) ranged from approximately 28% to 86% in the region of factor levels tested. Lower percentages for DH(pen) were associated with extreme pH values (3.5, 5.8) and low Viscozyme L doses (5.0 FBGU). DH(pen) was positively and significantly correlated with DH(rs) and, to a lesser extent, with DH(aa) (Table 1). The equation describing the response surface for DH(pen) is
D H p e n = 486.78 + 176.42   p H + 40.67   D o s e 19.47   p H 2 2.39   D o s e 2

3.2. Measurements Derived from the Chemical Analysis of the Supernatant

3.2.1. Soluble Proteins According to the Bradford Assay, SProt

The ANOVA for the analysis of SProt data is presented in Supplementary Table S5. The quadratic model was clearly significant (p < 0.001), and the adequate precision reached a value close to 36. The adjusted R2 was 95%, and the lack of fit was only marginally significant. The quadratic model was chosen.
The results supported a significant effect of pH, whereas the Viscozyme L dose had practically no effect on the content of soluble proteins. The response surface exhibited mild curvature in the pH range of 3.5–5.8 (Figure 4). SProt values ranged from 9% to 12%, with higher values obtained at high pH (5.8). SProt was negatively and significantly correlated with DH(aa), DH(rs), SHDM, and to a lesser extent with SPep (Table 1). The equation chosen to describe the surface response of SProt is
S p r o t   % = 10.74 1.19   p H + 0.04   D o s e + 0.23   p H 2

3.2.2. Soluble Polypeptides (>10 kDa), SPep

The fitting of the quadratic model was significant, with a p-value below 0.001 (Supplementary Table S6), and the adequate precision was approximately 18. The R2 was above 84%, and the lack of fit was not significant. Both factors showed significant effects, so the quadratic model was chosen. The response surface exhibits curvature with respect to both pH and enzyme dose. A minimum value of 6.1 mg was inferred at pH 5.14 and an enzyme dose of 6.56 FBGU (Figure 5). In the investigated region of factor levels, higher SPep values were obtained at the lowest pH (3.5) and at extreme enzyme doses (5.0, 9.5 FBGU). The range of SPep values ranged from 6 to 9 mg, and the equation for the surface response of SPep is
S P e p m g = 26.8 6.68   p H 1.05   D o s e + 0.65   p H 2 + 0.08   D o s e 2

3.3. Measurements Derived from the Physical Analysis of the Pellet: Percentage of Solubilized and/or Hydrolyzed Dry Matter, SHDM

The fitting of the quadratic model for SHDM was significant (p < 0.001) (Supplementary Table S7), and the adequate precision was above 14. The R2 exceeded 76%, and the lack of fit was significant. The second-order model was selected for SHDM.
The response surface of SHDM displayed clear curvatures with respect to both pH and Viscozyme L dose, resembling a saddle-type surface (Figure 6). Both factors exerted highly significant effects, including squared and interactive terms. In the tested region of factor levels, the amount of solubilized and hydrolyzed dry matter increased with increasing Viscozyme L dose only when the pH was sufficiently low (<4.0). However, the effect of the dose was mildly negative at pH 4.5 and clearly negative at pH 5.5. The SHDM values ranged from 25% to 42% of the initial dry matter. The equation to describe the surface response of SHDM is
S H D M % = 42.87 + 22.22   p H + 11.29   D o s e 1.83   p H × D o s e 1.35   p H 2 0.25   D o s e 2
A summary of statistics of models for the response variables, DH(aa), DH(rs), DH(pen), SProt, SPep, and SHDM, is presented in Supplementary Table S8.

3.4. Validation Results

In general, the dependent variables measured in the validation assays were in good agreement with the 95% confidence interval (95% CI) and/or the 95% confidence tolerance interval for 99% of the population (95-99% TI) generated by the statistical analysis of the central composite design (Supplementary Tables S9 and S10). The mean degree of protein hydrolysis in the validation assays (DH(aa) = 14.69%) was within the 95% CI. The means for the percentage of solubilized proteins (SProt = 9.59%) and soluble peptides (SPep = 7.23 mg) were very close to the lower bounds of their respective 95% CIs (9.62% and 7.30 mg). Additionally, all three replicated values of each dependent variable measured in the validation assays consistently fell within their respective 95–99% TIs.

4. Discussion

4.1. Hydrolysis of Proteins by Simultaneous Action of Viscozyme L and Fish Gastric Extracts in Defatted Soybean Flour

Adding Viscozyme L improves the degree of protein hydrolysis in soybean flour. This is likely due to the softening of the barrier created by NSPs [2]. In this study, the degree of hydrolysis based on amino acid release (DH(aa)) ranged from 5% to 25% under optimal conditions. This falls within the range of total peptide production reported by other authors using similar substrates and various proteases, including animal digestive proteases. For instance, Wang et al. [33] reported a maximum DH of 35% with an alkaline proteinase on a commercial soy protein isolate (pH 9.0, 50 °C, 8 h). However, a similar dose of papain (pH 6.5, 60 °C, 6 h) or porcine trypsin (pH 7.0, 37 °C, 4 h) yielded only 5% DH. Daliri et al. [34] obtained an intermediate DH of 15% when treating soy protein isolate with the commercial protease Prozyme 2000P (pH 7.0, 55 °C, 1 h). Lee et al. [35] achieved DH values between 12% and 15% when HCl-acidified defatted soybean flour was treated with Alcalase (pH 6.0–6.2, 50 °C, 3 h). A subsequent 21 h treatment of the hydrolysate with Alcalase and Flavourzyme resulted in DH values between 20% and 30% (see Figure 7 belonging to ref. [35]). These results lead to several inferences:
1.
The hydrolysis degree depends on the specific enzyme, protocol, and physicochemical reaction conditions, according to previous literature.
2.
Adding acidic NSPases to the proteolytic reaction (as in this study) is as efficient as increasing the reaction temperature. It does not necessarily require longer processing times. This suggests dietary acidic NSPases enhance protein digestion at physiological temperatures when included in DSBF-containing aquafeeds.
The greater degree of carbohydrate hydrolysis compared to proteins is explained by the diversity of NSPases in Viscozyme L. The combined action of these NSPases efficiently hydrolyzes carbohydrate chains at physiological temperature and pH. Conversely, rainbow trout gastric proteases (various pepsin types) primarily produce polypeptides between 300 and 1700 Da, with almost no free amino acids, when acting on a soybean protein concentrate (63% protein dry weight) [27]. This is likely because pepsins are endo-proteases.
Our choice of NSPases with a pH range overlapping that of S. aurata gastric proteases is also noteworthy. This overlap enables the simultaneous action of both enzymatic activities. Within the dietary matrix of partially ruptured plant cells, NSPs can prevent protease access to protein. Similarly, protein particles can hinder the contact between carbohydrases and NSP fiber surfaces. Furthermore, the potential synergy between proteases and NSPases is crucial for breaking down cell walls that contain tightly associated NSPs and glycoproteins [14].
However, in live fish, gastric pH decreases as gastric chyme is evacuated. Gastric evacuation and gastric pH decrease rate vary among studies. For example, Nikolopoulou et al. [23] reported a 50% evacuation time of about 180 min in 150 g Sparus aurata reared at 26 °C. In contrast, Álvarez et al. [36] reported about 12 h for 50% evacuation in 200 g S. aurata fed a commercial diet at 25 °C. Minimal gastric pH and the rate of gastric pH decrease also vary. Nikolopoulou et al. [23] reported a minimal pH of 3.8–4.0 after 10 h of digestion whereas Deguara et al. [37] found a minimal gastric pH of 2.5 at the 8th hour of digestion in 150 g S. aurata reared at 21 °C. Therefore, our results are more applicable to in vivo conditions when gastric evacuation is slow and gastric pH decreases rapidly. This effect depends on several factors; for example, dietary NSPs can slow gut transit time.

4.2. Hydrolysis of NSPs by Simultaneous Action of Viscozyme L and Fish Gastric Extracts in Defatted Soybean Flour

The existence of an optimum Viscozyme L dose for NSP hydrolysis is particularly interesting. The literature also shows an optimal Viscozyme dose in several cases, mainly regarding protein extraction from plant matrices [12,38]. Retro-inhibition by the final product of a single carbohydrase in Viscozyme L can not fully explain this optimum. Such inhibition implies a gradual reduction in enzymatic activity over time not a reduction in the maximal product released. Viscozyme L contains a mix of carbohydrases. They cooperate to dismantle the cell wall architecture and degrade NSP components. These components are not equally susceptible to enzymatic hydrolysis [39]. Efficient cell wall degradation requires sequential hydrolysis of pectins, hemicellulose, and cellulose fibers. Hydrolysis is generally accepted to begin with pectic polymers [40]. Thus, enzyme inhibition by a compound released from a second enzyme [41] could lead to an optimum. This occurs if, for example, the inhibited enzyme stops early in the cooperative hydrolytic process. Another potential mechanism is competition among NSPases for binding sites on carbohydrate fiber surfaces. Maurer et al. [42] described the competition between two fungal glucanases (an exoglucanase and an endoglucanase) for binding sites on cellulose fibers. This mechanism has implications for hydrolysis optimization. The shape and size of cell wall particles would determine the surface area available for enzyme adsorption and the hydrolysis rate.

4.3. Soluble Protein and Dry Matter Hydrolysis/Solubilization After the Simultaneous Action of Viscozyme L and Fish Gastric Extracts on Defatted Soybean Flour

Our results indicate that S. aurata gastric proteases alter the solubility pH-profile of soybean proteins. Typically, unhydrolyzed soybean meal proteins exhibit minimum solubility at pH 4.0–5.0 [43]. However, in this work, protein solubility (polypeptides above 10 kDa in the supernatant) increased with pH in the 3.2–6.0 range after enzymatic treatment. Champagne and Phillippy [44] found constant solubility for polypeptides above 10 kDa in the pH 3.0–5.0 range and an increase in the 5.0–6.0 range after treating a soybean protein isolate with porcine pepsin. This process is driven by dissociating phytate–protein insoluble complexes at pH values below 5.0 [43].
Another interesting finding is the drop in dry matter solubilization (dependent variable SHDM) with increasing Viscozyme L dose when pH is above 4.5 (Figure 6). This trend is difficult to relate to other dependent variables. When the pH exceeded 4.5, DH(aa), SProt, SPep, and DH(pen) increased at higher Viscozyme L doses. DH(rs) had an optimal value at intermediate Viscozyme L doses. Although this point warrants further research, we propose that the accumulation of negatively charged, pectin-derived oligosaccharides (due to higher carbohydrase doses) could destabilize the solubility equilibrium among cations (e.g., Mg2+, Ca2+) [45] and organic anions like phytate.

5. Conclusions

This work confirms that Viscozyme L improves soybean flour protein hydrolysis in combination with S. aurata gastric proteases. The response surface methodology (RSM) was useful for modeling hydrolysate production across various factor levels and dependent variables using a minimal number of replicates. The simultaneous action of S. aurata gastric extracts and Viscozyme L under mildly acidic pH (3.2–6.0) at 25 °C allowed peptide bond hydrolysis (peptides below 10 kDa) to range from 6% to 25%. Simultaneously, glucosidic bond hydrolysis into reducing sugars (in oligosaccharides below 10 kDa) remained high, ranging from 66% to 86%.
These results have several implications for future research. The functional and health-promoting effects of soybean protein hydrolysates depend on their hydrolysis degree [46]. We show that NSPase dose effectively controls this. Thus, future studies should investigate how soybean protein hydrolysate functional properties change with Viscozyme L dose. Another future direction involves testing Viscozyme L effects in an in vitro gastric digestion model that simulates the in vivo decreasing pH. Finally, for S. aurata diet formulation, future research should address Viscozyme L’s effects on in vivo digestibility, gut content viscosity, and the absorption and metabolic use of sugars from NSP hydrolysis.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fishes10070320/s1, Table S1. Design matrix and experimental responses. Table S2. ANOVA for lineal model on the transformed response variable DH(aa) = degree of hydrolysis of proteins based on the production of amino acids and small peptides. Transform: Natural Log; Constant: 0.12 (y’ = ln (y + 0.12)). Table S3. ANOVA for quadratic model on the response variable DH(rs) = degree of hydrolysis of polysaccharides based on the production of reducing sugars. Table S4. ANOVA for reduced quadratic model on the response variable DH(pen) = degree of hydrolysis of polysaccharides based on the production of pentoses. Table S5. ANOVA for reduced quadratic model on the response variable SProt = percentage of soluble protein in the supernatant in respect of total protein in the ingredient. Table S6. ANOVA for reduced quadratic model on the response variable SPep = mass of soluble peptides in the supernatant released by the enzymatic treatment. Table S7. ANOVA for quadratic model on the response variable SHDM = solubilized plus hydrolyzed dry matter. Table S8. Model summary statistics for response variables. Fit statistic: R-squared (R2) is that it is the proportion of variance in the response variable y that the regression model can “explain” via the introduction of regression variables; Adjusted R-Squared compares models with different numbers of terms (R2 is penalized each time a new regression variable is added); Predicted R-squared determines how well a regression model makes predictions; “Adeq Precision” measures the signal to noise ratio. A ratio greater than 4 is desirable. The response variable DH(aa) was transformed as ln(DH(aa) + 0.12). Table S9. Experimental responses (triplicate) in validation assays. pH = 3.6, Viscozyme L dose = 6.71 FBGU. Table S10. Confidence intervals (CI) and tolerance intervals (TI) for dependent variables and mean values for validation assays. Figure S1. Central composite design with two numeric factors. Each factor is set to 5 levels: plus, and minus alpha (axial points), plus and minus 1 (factorial points) and the center point. Alpha = 1.41421 rotatable. Replication: 2 blocks and 14 runs per block; 2 replicates of factorial points (black circles), 2 replicates of axial points (grey circles), 6 center points in each factorial block and 6 center points in each axial block. The central point was replicated 12 times (open circle). The first parentheses close to each point represent the combination of pH and carbohydrase dose tested. The second parentheses close to each point represent the number of replicates performed. Figure S2. Preliminary experiment to set the levels of the factor “Enzyme dose”. Reducing sugars produced by Viscozyme L acting upon 150 mg of defatted soybean meal (DSBM) suspended in aqueous medium (3 mL) at pH 5.5 and 25 °C for 180 min. The selected range for the central composite design, 5.4–9.5 FBGU, is indicated in the figure.

Author Contributions

Conceptualization, Ó.M. and M.D.; methodology, Ó.M.; formal analysis, Ó.M., M.D. and L.M.; investigation, Ó.M.; resources, F.J.M. and M.D.; data curation, Ó.M. and M.D.; writing—original draft preparation, Ó.M. and L.M.; writing—review and editing, F.J.M. and M.D.; visualization, M.D.; supervision, M.D.; project administration, M.D.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Gut Modelling Working Groups (AGR152) University of Almeria and an academic excellence grant for doctoral studies, supported by SENACYT (Secretaría Nacional de Ciencia y Tecnología) Panamá.

Institutional Review Board Statement

Not applicable. The fish used in this work were obtained from the production of a local fish farm before being dispatched to markets; the authors did not manipulate any living animal.

Informed Consent Statement

Not applicable.

Data Availability Statement

Relevant information has been added in the article (Supplementary Materials).

Acknowledgments

The present research was performed as part of the work of O. M. to fulfill the requirements to obtain the degree of Doctor of Philosophy issued by the Universidad de Almería (Spain).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
RSMResponse surface methodology
CCDCentral composite design
DSBFDefatted soybean flour
NSPNon-starch polysaccharide
NSPasesNon-starch polysaccharidases
FBGUFungal beta-glucanase unit
DNSADinitrosalycilic acid
OPAOrtho-phthalaldehyde
TCATrichloroacetic acid
TNBSATrinitrobenzenesulfonic acid
DHDegree of hydrolysis
MWCOMolecular weight cut-off

References

  1. Hartman, G.L.; West, E.D.; Herman, T.K. Crops that feed the World 2. Soybean—Worldwide production, use, and constraints caused by pathogens and pests. Food Sec. 2011, 3, 5–17. [Google Scholar] [CrossRef]
  2. Loman, A.A.; Ju, L.-K. Enzyme-based processing of soybean carbohydrate: Recent developments and future prospects. Enzym. Microb. Technol. 2017, 106, 35–47. [Google Scholar] [CrossRef]
  3. Preece, K.E.; Hooshyar, N.; Zuidam, N.J. Whole soybean protein extraction processes: A review. Innov. Food Sci. Emerg. Technol. 2017, 43, 163–172. [Google Scholar] [CrossRef]
  4. Oliva-Teles, A.; Enes, P.; Couto, A.; Peres, H. Replacing fish meal and fish oil in industrial fish feeds. In Feed and Feeding Practices in Aquaculture, 2nd ed.; Davis, D.A., Ed.; Woodhead Publishing: Oxford, UK, 2022; pp. 231–268. [Google Scholar] [CrossRef]
  5. Aas, T.S.; Åsgård, T.; Ytrestøyl, T. Utilization of feed resources in the production of Atlantic salmon (Salmo salar) in Norway: An update for 2020. Aquac. Rep. 2022, 26, 101316. [Google Scholar] [CrossRef]
  6. Aas, T.S.; Åsgård, T.; Ytrestøyl, T. Utilization of feed resources in the production of rainbow trout (Oncorhynchus mykiss) in Norway in 2020. Aquac. Rep. 2022, 26, 101317. [Google Scholar] [CrossRef]
  7. Porcino, N.; Genovese, L. Review on alternative meals for gilthead seabream, Sparus aurata. Aquacult. Res. 2022, 53, 2109–2145. [Google Scholar] [CrossRef]
  8. Mullin, W.J.; Xu, W. A study of the intervarietal differences of cotyledon and seed coat carbohydrates in soybeans. Food Res. Int. 2000, 33, 883–891. [Google Scholar] [CrossRef]
  9. Zahir, M.; Fogliano, V.; Capuano, E. Food matrix and processing modulate in vitro protein digestibility in soybeans. Food Funct. 2018, 9, 6326–6336. [Google Scholar] [CrossRef]
  10. Zahir, M.; Fogliano, V.; Capuano, E. Effect of soybean processing on cell wall porosity and protein digestibility. Food Funct. 2020, 11, 285–296. [Google Scholar] [CrossRef]
  11. Robaina, L.; Izquierdo, M.S.; Moyano, F.J.; Socorro, J.; Vergara, J.M.; Montero, D.; Fernández-Palacios, H. Soybean and lupin seed meals as protein sources in diets for gilthead seabream (Sparus aurata): Nutritional and histological implications. Aquaculture 1995, 130, 219–233. [Google Scholar] [CrossRef]
  12. Rosset, M.; Acquaro Junior, V.R.; Beleia, A.D.P. Protein extraction from defatted soybean flour with Viscozyme L pretreatment. J. Process. Preserv. 2014, 38, 784–790. [Google Scholar] [CrossRef]
  13. Jodayree, S.; Smith, J.C.; Tsopmo, A. Use of carbohydrase to enhance protein extraction efficiency and antioxidative properties of oat bran protein hydrolysates. Food Res. Int. 2012, 46, 69–75. [Google Scholar] [CrossRef]
  14. Liepman, A.H.; Cavalier, D.M.; Lerouxel, O.; Keegstra, K. Cell Wall Structure, Biosynthesis and Assembly. In Annual Plant Reviews Volume 25: Plant Cell Separation and Adhesion; Roberts, J.A., Gonzalez-Carranza, Z., Eds.; Blackwell Publishing: Oxford, UK, 2007; pp. 8–39. [Google Scholar] [CrossRef]
  15. Stites, W.; Weldon, A.; Reis, J.; Ito, P.; Rhodes, M.; Davis, D.A. Evaluation of a carbohydrase (xylanase and glucanase) enzyme complex in diets for Florida pompano Trachinotus carolinus. J. World Aquac. Soc. 2024, 55, e13095. [Google Scholar] [CrossRef]
  16. Romero García, J.M.; Acién Fernández, F.G.; Fernández Sevilla, J.M. Development of a process for the production of L-amino-acids concentrates from microalgae by enzymatic hydrolysis. Bioresour. Technol. 2012, 112, 164–170. [Google Scholar] [CrossRef]
  17. Myers, R.H.; Montgomery, D.C.; Anderson-Cook, C.M. Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 4th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2016; p. 825. [Google Scholar]
  18. Deepanraj, B.; Sivasubramanian, V.; Jayaraj, S. Multi-response optimization of process parameters in biogas production from food waste using Taguchi—Grey relational analysis. Energy Convers. Manag. 2017, 141, 429–438. [Google Scholar] [CrossRef]
  19. Hollebeeck, S.; Borlon, F.; Schneider, Y.-J.; Larondelle, Y.; Rogez, H. Development of a standardised human in vitro digestion protocol based on macronutrient digestion using response surface methodology. Food Chem. 2013, 138, 1936–1944. [Google Scholar] [CrossRef]
  20. Anson, M.L. The estimation of pepsin, trypsin, papain and cathepsin with hemoglobin. J. Gen. Physiol. 1938, 22, 79–89. [Google Scholar] [CrossRef]
  21. Montgomery, D.C. Design and Analysis of Experiments, 8th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2013; p. 752. [Google Scholar]
  22. Namal Senanayake, S.P.J.; Shahidi, F. Lipase-catalyzed incorporation of docosahexaenoic acid (DHA) into borage oil: Optimization using response surface methodology. Food Chem. 2002, 77, 115–123. [Google Scholar] [CrossRef]
  23. Nikolopoulou, D.; Moutou, K.A.; Fountoulaki, E.; Venou, B.; Adamidou, S.; Alexis, M.N. Patterns of gastric evacuation, digesta characteristics and pH changes along the gastrointestinal tract of gilthead sea bream (Sparus aurata L.) and European sea bass (Dicentrarchus labrax L.). Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 2011, 158, 406–414. [Google Scholar] [CrossRef]
  24. Munilla-Morán, R.; Saborido-Rey, F. Digestive enzymes in marine species. I. Proteinase activities in gut from redfish (Sebastes mentella), seabream (Sparus aurata) and turbot (Scophthalmus maximus). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 1996, 113, 395–402. [Google Scholar] [CrossRef]
  25. Alarcón, F.J.; Díaz, M.; Moyano, F.J.; Abellán, E. Characterization and functional properties of digestive proteases in two sparids; gilthead seabream (Sparus aurata) and common dentex (Dentex dentex). Fish Physiol. Biochem. 1998, 19, 257–267. [Google Scholar] [CrossRef]
  26. Kır, M. Thermal tolerance and standard metabolic rate of juvenile gilthead seabream (Sparus aurata) acclimated to four temperatures. J. Therm. Biol. 2020, 93, 102739. [Google Scholar] [CrossRef]
  27. Grabner, M.; Hofer, R. Stomach digestion and its effect upon protein hydrolysis in the intestine of rainbow trout (Salmo gairdneri Richardson). Comp. Biochem. Physiol. A Physiol. 1989, 92, 81–83. [Google Scholar] [CrossRef]
  28. Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976, 72, 248–254. [Google Scholar] [CrossRef]
  29. Nielsen, P.M.; Petersen, D.; Dambmann, C. Improved method for determining food protein degree of hydrolysis. J. Food Sci. 2001, 66, 642–646. [Google Scholar] [CrossRef]
  30. Adler-Nissen, J. Control of the proteolytic reaction and of the level of bitterness in protein hydrolysis processes. J. Chem. Tech. Biotechnol. 1984, 34, 215–222. [Google Scholar] [CrossRef]
  31. Miller, G.L. Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal. Chem. 1959, 31, 426–428. [Google Scholar] [CrossRef]
  32. Eberts, T.J.; Sample, R.H.; Glick, M.R.; Ellis, G.H. A simplified, colorimetric micromethod for xylose in serum or urine, with phloroglucinol. Clin. Chem. 1979, 25, 1440–1443. [Google Scholar] [CrossRef]
  33. Wang, R.; Zhao, H.; Pan, X.; Orfila, C.; Lu, W.; Ma, Y. Preparation of bioactive peptides with antidiabetic, antihypertensive, and antioxidant activities and identification of α-glucosidase inhibitory peptides from soy protein. Food Sci. Nutr. 2019, 7, 1848–1856. [Google Scholar] [CrossRef]
  34. Daliri, E.B.-M.; Ofosu, F.K.; Chelliah, R.; Park, M.H.; Kim, J.-H.; Oh, D.-H. Development of a soy protein hydrolysate with an antihypertensive effect. Int. J. Mol. Sci. 2019, 20, 1496. [Google Scholar] [CrossRef]
  35. Lee, J.-Y.; Lee, H.D.; Lee, C.-H. Characterization of hydrolysates produced by mild-acid treatment and enzymatic hydrolysis of defatted soybean flour. Food Res. Int. 2001, 34, 217–222. [Google Scholar] [CrossRef]
  36. Álvarez, A.; García, B.G.; Valverde, J.C.; Giménez, F.A.; Hernández, M.D. Gastrointestinal evacuation time in gilthead seabream (Sparus aurata) according to the temperature. Aquacult. Res. 2010, 41, 1101–1106. [Google Scholar] [CrossRef]
  37. Deguara, S.; Jauncey, K.; Agius, C. Enzyme activities and pH variations in the digestive tract of gilthead sea bream. J. Fish Biol. 2003, 62, 1033–1043. [Google Scholar] [CrossRef]
  38. de Figueiredo, V.R.G.; Yamashita, F.; Vanzela, A.L.L.; Ida, E.I.; Kurozawa, L.E. Action of multi-enzyme complex on protein extraction to obtain a protein concentrate from okara. J. Food Sci. Technol. 2018, 55, 1508–1517. [Google Scholar] [CrossRef]
  39. Ouhida, I.; Pérez, J.F.; Gasa, J. Soybean (Glycine max) cell wall composition and availability to feed enzymes. J. Agric. Food Chem. 2002, 50, 1933–1938. [Google Scholar] [CrossRef] [PubMed]
  40. Tavares, E.Q.P.; De Souza, A.P.; Buckeridge, M.S. How endogenous plant cell-wall degradation mechanisms can help achieve higher efficiency in saccharification of biomass. J. Exp. Bot. 2015, 66, 4133–4143. [Google Scholar] [CrossRef] [PubMed]
  41. Qing, Q.; Yang, B.; Wyman, C.E. Xylooligomers are strong inhibitors of cellulose hydrolysis by enzymes. Bioresour. Technol. 2010, 101, 9624–9630. [Google Scholar] [CrossRef]
  42. Maurer, S.A.; Bedbrook, C.N.; Radke, C.J. Competitive sorption kinetics of inhibited endo- and exoglucanases on a model cellulose substrate. Langmuir 2012, 28, 14598–14608. [Google Scholar] [CrossRef]
  43. Morales, G.A.; Sáenz de Rodrigánez, M.; Márquez, L.; Díaz, M.; Moyano, F.J. Solubilisation of protein fractions induced by Escherichia coli phytase and its effects on in vitro fish digestion of plant proteins. Anim. Feed Sci. Technol. 2013, 181, 54–64. [Google Scholar] [CrossRef]
  44. Champagne, E.T.; Phillippy, B.Q. Effects of pH on calcium, zinc, and phytate solubilities and complexes following in vitro digestions of soy protein isolate. J. Food Sci. 1989, 54, 587–592. [Google Scholar] [CrossRef]
  45. Powell, D.A.; Morris, E.R.; Gidley, M.J.; Rees, D.A. Conformations and interactions of pectins: II. Influence of residue sequence on chain association in calcium pectate gels. J. Mol. Biol. 1982, 155, 517–531. [Google Scholar] [CrossRef] [PubMed]
  46. Coscueta, E.R.; Amorim, M.M.; Voss, G.B.; Nerli, B.B.; Picó, G.A.; Pintado, M.E. Bioactive properties of peptides obtained from Argentinian defatted soy flour protein by Corolase PP hydrolysis. Food Chem. 2016, 198, 36–44. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Contour map (left) and response surface (right) for the degree of hydrolysis of proteins based on amino acids and small peptides, expressed as Aminoacids (DH, %) or DH(aa). The contour map includes design factorial points (in red). Numbers in boxes with a white background label the contour lines (isolines), representing the same predicted value for a response variable. In both plots, a continuous color gradient visually represents the predicted response. The color scale indicates the range: red for the highest predicted response values, blue for the lowest, and intermediate colors (orange, yellow, green) for progressively increasing or decreasing values within the independent variable levels (factors).
Figure 1. Contour map (left) and response surface (right) for the degree of hydrolysis of proteins based on amino acids and small peptides, expressed as Aminoacids (DH, %) or DH(aa). The contour map includes design factorial points (in red). Numbers in boxes with a white background label the contour lines (isolines), representing the same predicted value for a response variable. In both plots, a continuous color gradient visually represents the predicted response. The color scale indicates the range: red for the highest predicted response values, blue for the lowest, and intermediate colors (orange, yellow, green) for progressively increasing or decreasing values within the independent variable levels (factors).
Fishes 10 00320 g001
Figure 2. Contour map (left) and response surface (right) for the degree of hydrolysis of NSPs based on reducing sugars, expressed as DH(rs)(DH, %). The contour map includes design factorial points (in red).
Figure 2. Contour map (left) and response surface (right) for the degree of hydrolysis of NSPs based on reducing sugars, expressed as DH(rs)(DH, %). The contour map includes design factorial points (in red).
Fishes 10 00320 g002
Figure 3. Contour map (left) and response surface (right) for the degree of hydrolysis of NSPs based on pentoses, expressed as DH(pen)(Pento… (DH, %)). The contour map includes design factorial points (in red).
Figure 3. Contour map (left) and response surface (right) for the degree of hydrolysis of NSPs based on pentoses, expressed as DH(pen)(Pento… (DH, %)). The contour map includes design factorial points (in red).
Fishes 10 00320 g003
Figure 4. Contour map (left) and response surface (right) for the percentage of soluble proteins, expressed as soluble proteins (SProt)(%). The contour map includes design factorial points (in red).
Figure 4. Contour map (left) and response surface (right) for the percentage of soluble proteins, expressed as soluble proteins (SProt)(%). The contour map includes design factorial points (in red).
Fishes 10 00320 g004
Figure 5. Contour map (left) and response surface (right) for soluble peptides produced, expressed as soluble peptides (SPep)(mg). The contour map includes design factorial points (in red).
Figure 5. Contour map (left) and response surface (right) for soluble peptides produced, expressed as soluble peptides (SPep)(mg). The contour map includes design factorial points (in red).
Fishes 10 00320 g005
Figure 6. Contour map (left) and response surface (right) for the percentage of solubilized and/or hydrolyzed dry matter (SHDM), expressed as Gravimetric DH (%)(Gravimet… (%)). The contour map includes design factorial points (in red).
Figure 6. Contour map (left) and response surface (right) for the percentage of solubilized and/or hydrolyzed dry matter (SHDM), expressed as Gravimetric DH (%)(Gravimet… (%)). The contour map includes design factorial points (in red).
Fishes 10 00320 g006
Table 1. Pearson correlation coefficients among dependent variables measured in the filtrate, DH(aa), DH(rs), DH(pen), in the supernatant, SProt and SPep, and in the remaining pellet, SHDM, after hydrolysis of defatted soybean meal flour. Numbers in parentheses indicate p-values.
Table 1. Pearson correlation coefficients among dependent variables measured in the filtrate, DH(aa), DH(rs), DH(pen), in the supernatant, SProt and SPep, and in the remaining pellet, SHDM, after hydrolysis of defatted soybean meal flour. Numbers in parentheses indicate p-values.
VariablesDH(aa)DH(rs)DH(pen)SprotSPepSHDM
DH(aa)-
DH(rs)0.445
(0.018)
-
DH(pen)0.394
(0.038)
0.560
(0.002)
-
SProt−0.510
(0.006)
−0.722
(<0.001)
−0.152
(0.441)
-
SPep0.747
(<0.001)
0.151
(0.444)
-0.126
(0.524)
−0.411
(0.030)
-
SHDM0.406
(0.032)
0.657
(<0.001)
0.313
(0.105)
−0.686
(<0.001)
0.265
(0.173)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Martínez, Ó.; Márquez, L.; Moyano, F.J.; Díaz, M. Modeling the Hydrolysis of Soybean Flour Proteins Digested with Gastric Proteases of the Marine Fish Sparus aurata and Commercial Non-Starch Polysaccharidases. Fishes 2025, 10, 320. https://doi.org/10.3390/fishes10070320

AMA Style

Martínez Ó, Márquez L, Moyano FJ, Díaz M. Modeling the Hydrolysis of Soybean Flour Proteins Digested with Gastric Proteases of the Marine Fish Sparus aurata and Commercial Non-Starch Polysaccharidases. Fishes. 2025; 10(7):320. https://doi.org/10.3390/fishes10070320

Chicago/Turabian Style

Martínez, Óscar, Lorenzo Márquez, Francisco J. Moyano, and Manuel Díaz. 2025. "Modeling the Hydrolysis of Soybean Flour Proteins Digested with Gastric Proteases of the Marine Fish Sparus aurata and Commercial Non-Starch Polysaccharidases" Fishes 10, no. 7: 320. https://doi.org/10.3390/fishes10070320

APA Style

Martínez, Ó., Márquez, L., Moyano, F. J., & Díaz, M. (2025). Modeling the Hydrolysis of Soybean Flour Proteins Digested with Gastric Proteases of the Marine Fish Sparus aurata and Commercial Non-Starch Polysaccharidases. Fishes, 10(7), 320. https://doi.org/10.3390/fishes10070320

Article Metrics

Back to TopTop