Optimization of extraction conditions of gelatin from buffalo (Bubalus bubalis) skins using response surface methodology

Abstract

The present study was to determine optimum conditions for gelatin extraction from the skin of buffalo (Bubalus bubalis) using response surface methodology. A central composite design (CCD) was performed to evaluate the effects of NaOH concentration ($X_1$), pre-treatment time ($X_2$), extraction temperature ($X_3$), and extraction time ($X_4$) on the yield (Y₁), gel strength (Y₂), and hydroxyproline content (Y₃) of the extracted gelatin. The optimal combination of the independent variables for a good gelatin yield with high gel strength and hydroxyproline content was found at $X_1$ (0.77 M), $X_2$ (5.08 h), $X_3$ (62.93 °C) and $X_4$ (11.62 h). The experimental values for Y₁ (16.91%), Y₂ (236.5 g), and Y₃ (41.4 g/100 g) were in good agreement with the predicted values of 17.87% yield, 237.80 g gel strength and 41.90 g/100 g of hydroxyproline content. Extraction temperature and extraction time were observed to be the most important factors that influenced the yield, gel strength, and hydroxyproline content, meanwhile pre-treatment time showed negative correlations with the yield and hydroxyproline content of the extracted gelatin. This study demonstrated that manipulation of specific parameters could improve extraction efficiency without compromising the quality of buffalo gelatin, thereby promoting it as an alternative source for gelatin production.

1. Introduction

Gelatin is a partially hydrolyzed form of collagen and a water-soluble, thermos-reversible, and multifunctional hydrocolloid. The raw materials used for gelatin production are generally sourced from bones, skins, and tendons of bovine and pigs. Gelatin has a wide range of applications in the food and non-food industries owing to its considerable techno-functional properties such as water binding, foaming, emulsifier, and fining agent [1,2]. Gelatin is extensively investigated because it preserves the structural characteristics of collagen while eliminating the immunogenic response that collagen may preserve [3]. The most commonly used production process for gelatin manufacture involves the subjection of raw materials to washing, partial hydrolysis with acids or alkalis, neutralization, extraction, filtration, demineralization, concentration, and drying [4].

Over the last decade, the global demand for gelatin has increased considerably. The worldwide gelatin market stood at 1.83 billion USD in 2021 and was projected to increase by about 3.30% to reach 2.15 billion USD in 2026 [5]. According to Commersonnianus (2020) [6], the world production of gelatin is nearly 326,000 tonnes annually, with pigskin gelatin accounting for the highest production (44%), followed by bovine hides (28%), bones (27%) and one percent from other sources. Gelatin from land sources is generally preferred over fish gelatin due to their greater functional properties, however, these mammalian sources raise a number of issues that are up for debate. For instance, Jews and Muslims do not consume any pig-related products in their diets, and consumers are becoming increasingly concerned about the occurrence of health diseases such as bovine sponge encephalopathy (BSE), transmissible spongiform encephalopathy (TSE),] and foot-and-mouth disease (FMD) [7]. Hence, all of these issues point to the need for investigating alternative sources of gelatin.

During the past decades, there has been an intensive trend in gelatin derived from non-mammalian sources such as marine and poultry gelatin as an alternative gelatin [[7], [8], [9], [10], [11], [12]]. However, the gelatin’s yield, functional and physicochemical properties from poultry were low compared to commercial gelatin available in the market [13,14]. Also, Rafieian et al. (2013) [8] reported that chicken deboner gelatin showed poor gelatin yield and rheological properties with only 10% of yield and a viscosity of 5.85 cP. Furthermore, gelatin from chicken leg skin possessed relatively poor gel strength; 79.03 g Bloom [10] which was classified as low; <150 g according to Gelatin Manufacturers Institute of America (GMIA) standards [15]. Although marine gelatin could produce gelatin with higher yields compared to poultry, yet their hydroxyproline content was inferior compared to bovine gelatin, thus resulting in poor physicochemical and functional properties that eventually affect the quality of end products [7,16,17]. Therefore, more studies on finding a prospective gelatin alternative from other sources need to be pursued.

Buffalo or its scientific name (Bubalus bubalis) has been used as a main source of red meat in Borneo, primarily for frozen meat exportation and the skin normally would be discarded. The public’s perception of waste has changed from that of an inevitable byproduct of industrialized economies to that of a valuable, reusable resource as urbanization and consumption rates rise and natural resources deplete. Waste services are heavily featured in the aims and indicators of both Sustainable Development Goals 11 and 12, particularly with assurance to prevent, reduce, recycle, and reuse waste. The goal is to appropriately collect and dispose of municipal solid waste as well as reduce food waste worldwide by 2030 [18]. Buffalo skin is regarded as an underutilized source of gelatin. To date, very limited works have been done on gelatin derived from buffalo skin. Interestingly, the skin of buffalo is generally twice as thick as cowhide between 6 and 8 mm, indicating the presence of a high density of collagen bundles and therefore might result in a higher yield of gelatin [19].

Extraction processes are among the vital parameters that will affect the quality and production scale of gelatin. Arsyanti et al. (2018) [20] used different concentrations of sodium hydroxide and a combination of sodium hydroxide and citrate acid to produce gelatin from buffalo skin. It was found that the gelatin yield was increased as the concentration of NaOH increased yet produced poor physicochemical properties of gelatin. While studied by Rabiatul Amirah et al. (2020) [21] comparing different concentrations of sodium hydroxide (NaOH) and calcium hydroxide (CaOH) during the pre-treatment step of gelatin extraction from buffalo skin and it was found that NaOH showed better gelatin yield compared to CaOH. Moreover, an attempt of using enzyme treatment in buffalo skin gelatin was done by Aprizal et al. (2019) [22] and they reported that the gelatin yield was only in the range of 5.99–7.33% and the viscosity property was less than GMIA standard. These findings showed that by varying treatment conditions, different characteristics of gelatin such as yield, physicochemical and functional properties can be obtained. However, given the poor quality of gelatin produced in previous studies, optimizing the extraction process to obtain a high yield of buffalo gelatin without jeopardizing gelatin quality is critical. The most important variables influencing extraction yield and functional properties of extracted gelatin are NaOH concentration, alkali-treatment time, extraction temperature, and extraction time.

Response Surface Methodology (RSM) is a statistical and mathematical technique for developing, improving, and optimizing processes. To obtain the best yield and functional properties of gelatin, factors such as concentration of alkaline, pre-treatment time, extraction time, and extraction temperature, need to be highlighted. Hence optimization is a suitable technique for developing methods of extraction as the quality of extracted material solely depends on the processing parameters. The relevant interactions between factors can be found and quantified using RSM [23], which can lead to the production of high-quality gelatin. Therefore, the present work aimed to optimize the extraction conditions for gelatin from buffalo skin using RSM. The impacts of notable processing variables (NaOH concentration, pre-treatment time, extraction temperature, and extraction time) on the yield, gel strength, and hydroxyproline content of buffalo gelatin were examined and the optimum conditions for buffalo gelatin extraction were determined in this study.

2. Materials and methods

2.1. Materials

Buffalo skin (Bubalus bubalis) (1 kg) was provided by Huswani enterprise in Malaysia. The skin was washed thoroughly and cut at 1–4 cm size, kept frozen in polyethylene bags at −40 °C until it was used. All chemicals used were analytical grade.

2.2. Extraction of gelatin

Extraction of buffalo skin gelatin were performed according to the method carried out by Mulyani et al. (2019) [19] (Fig. 1), with slight modifications.

Fig. 1.

Extraction process of gelatin.

2.3. Experimental design

In order to optimize the extraction conditions for buffalo skin gelatin, RSM with 5 levels central composite design (CCD), $2^4$ factorial points, eight axial points (α = 2), and six replicates were adopted. The concentration of NaOH (%, X₁), pre-treatment time of alkali solution (h, X₂), extraction temperature (°C, X₃), and extraction time (h, X₄) were chosen as the independent variables. Results from a preliminary study are used to determine the range and centre point values for four independent variables (Table 1). The dependent variables, Yield (%, Y₁), gel strength (Bloom, Y₂), and hydroxyproline content (g/100 g, Y₃) were chosen as the responses measured. The CCD design obtained via RSM approach had a total of 30 experimental runs (Table 2). The experiments were unblocked and carried out in a random order in order to reduce the effects of unexpected variability in the measured responses.

Table 1.

Experimental range and values of the independent variables in the central composite design for gelatin extraction from buffalo (Bubalus bubalis) skin.

Independent variables	Symbols	Range and Levels
		−2	−1	0	1	2
Concentration of NaOH	X1	0.25	0.5	0.75	1.0	1.25
Pre-treatment time (h)	X2	0.2	2.8	5.4	8	10.6
Extraction Temperature (°C)	X3	40	50	60	70	80
Extraction Time (h)	X4	3	10	17	24	31

Table 2.

Central composite design and responses of the dependent variables for gelatin (Bubalus bubalis) skin to the independent variables.

Run No.	Coded levels of variable				Responses
	$X_1$	$X_2$	$X_3$	$X_4$	$Y_1$	$Y_2$	$Y_3$
1	0.5	2.8	50	10	2.51	103.6	21.31
2	0.75	5.4	60	31	19.45	93.6	44.1
3	0.75	10.6	60	17	15.4	86.49	42.1
4	0.75	5.4	60	17	16.9	230.8	41.7
5	0.5	2.8	50	24	10.2	89.1	37.34
6	0.5	8	50	24	11.84	98.2	38.57
7	1	2.8	70	24	21.07	7.6	35.74
8	0.25	5.4	60	17	16.98	158.5	26.96
9	0.75	5.4	60	3	4.1	168.8	39.98
10	1	8	50	24	14.49	178.8	34.68
11	0.75	5.4	60	17	19.1	250.4	44.74
12	1	8	50	10	3.88	100.1	20.49
13	1	2.8	50	24	9.8	161.9	35.94
14	1.25	5.4	60	17	11.09	139.7	22.51
15	0.75	0.2	60	17	20.52	8.4	39.6
16	1	8	70	10	19.58	54.2	34.13
17	1	2.8	70	10	16.06	99.9	39.52
18	0.75	5.4	60	17	22.16	220.7	40.7
19	0.5	2.8	70	24	21.38	9.1	40.4
20	0.5	8	70	24	23.67	30.58	36.74
21	0.5	8	70	10	18.51	130.6	39.11
22	1	2.8	50	10	5.1	146.5	24.7
23	1	8	70	24	20.78	64.1	36.59
24	0.5	2.8	70	10	20.23	78.2	42.24
25	0.75	5.4	80	17	24.76	1	40.47
26	0.5	8	50	10	1.69	116.2	30.82
27	0.75	5.4	60	17	21.96	270.32	45.03
28	0.75	5.4	60	17	18.2	265.2	40.24
29	0.75	5.4	60	17	23.43	269.8	43.34
30	0.75	5.4	40	17	0.69	70.7	19.7

X₁ (concentration of NaOH, %), X₂ (pre-treatment time of alkali solution, h), X₃ (extraction temperature, °C), and X₄ (extraction time, h).

The experimental data were statistically analyzed by Design-Expert 6.0.4. (State-Ease, Inc., Minneapolis MN, USA). According to the experimental design and the response value, a second-order polynomial equation was chosen to represent the experimental data. In this study’s model, the effect of four factors; concentration of NaOH (X₁), pre-treatment time (X₂), extraction temperature (X₃), and extraction time (X₄)] were investigated and six replicates were performed. The variance for each factor was divided into linear, quadratic, and interaction components. The following quadratic regression model was used for the analysis of data (Eq. (1)):

$$\mathrm{Y}={\mathrm{\beta }}_0+\sum _{\mathrm{i}=1}^4{\mathrm{\beta }}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{i}}+\sum _{\mathrm{i}=1}^4{\mathrm{\beta }}_{\mathrm{i}\mathrm{i}}{\mathrm{X}}_{\mathrm{I}}^2+\sum _{\mathrm{i}=1}^3\sum _{\mathrm{j}=\mathrm{i}+1}^4{\mathrm{\beta }}_{\mathrm{i}\mathrm{j}}{\mathrm{X}}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{j}}$$ (1)

where Y is the dependent variable (yield, gel strength, and hydroxyproline), ${\beta }_0$ is constant, ${\beta }_i$, ${\beta }_{ii}$, ${\beta }_{ij}$ are regression coefficients and $X_i$, $X_j$ are levels of the independent variables. After the multifactor analysis of variance has been determined, the optimal extraction conditions were obtained by the desirability function approach using Design Expert software. The response surface plots were developed using the Statistica program (Statistica version 5.5, Statsoft Inc. Tulsa, OK, USA) and represented as a function of two independent variables while keeping the other two independent variables at optimal values.

2.4. Yield

The amount of gelatin produced was determined using the following mathematical procedure below [24] (Eq. (2)).

$$Yield\left(\frac{g}{100}\right)=\frac{Dryweightofgelatin\left(g\right)}{Wetweightofskin\left(g\right)}\times 100$$ (2)

2.5. Gel (Bloom) strength

The British Standard 757: 1957 technique was used to determine the Bloom gel strength [25]. Gelatin samples were produced by dissolving 6.67% (w/v) dry gelatin in distilled water and heated for 30 min in a water bath at 60 °C until completely dissolved. The gelatin solutions were refrigerated in a refrigerator at a maturation temperature of 9–10 °C for 16–18 h. The gels were tested on a texture analyzer (TA.XT Stable Micro System, UK) with a load cell of 5 kg, by penetration with 0.5 cm diameter and 12.7 cm length of a standard flat bloomed plunger (P/0.5R). The penetration test was performed using a regular glass Bloom jar that was put in the centre of the plunger. To determine the maximal mass in the sample, the probe was forced to penetrate 4 mm into the sample at a speed of 0.5 mm/s.

2.6. Hydroxyproline content

Hydroxyproline content was measured by the method described by Food Standard Agency (2012) [26], using a conversion factor of 8. Preparation of standard stock solution started with dissolving 60 mg of hydroxyproline with distilled water in a 100 mL volumetric flask. An intermediate solution (6 μg/ml) of hydroxyproline was prepared by mixing 1 mL of stock solution with 99 mL distilled water in a 100 mL volumetric flask. A 100 μL aliquot of gelatine extract was placed into a test tube and 0.75 mL of 3.5 M sulphuric acid was added to the test tubes. The sample solutions were hydrolyzed for 16 h at 105 °C using a dry bath and diluted with 11.5 mL water. After that, 224.4 μL of the sample was diluted with 5.8 mL of tap water. Then sample was added with 1.0 mL of Chloramine-T solution, and incubated for 20 min at room temperature. 1.0 mL of 4-dimethylaminobenzaldehyde solution was later added and incubated at 60 °C for 15 min. The reaction was stopped by incubating the solution in iced water for 5 min. Gelatin extract was measured against the standards curve at 558 nm.

2.7. Electrophoretic analyses

The SDS-PAGE pattern of gelatin was determined according to the method described by Laemmli et al. (1970) [27] using 10% resolving gel and 4% stacking gel. After electrophoresis, the gel was stained with 1 g/L Coomassie brilliant blue R-250, dissolved in water, methanol, and trichloroacetic acid (5:4:1), and de-stained using a solution containing methanol, distilled water, and acetic acid at a ratio of 5:4:1. Bovine gelatin (Sigma, St. Louis, MO, USA) was used as standard and the protein molecular weight markers indicator (Sigma, St. Louis, MO, USA) was used as a marker for α-chain and β-component mobilities and obtained molecular weight of the protein bands.

3. Results and discussion

3.1. Fitting of RSM model

Response surface methodology (RSM) was used to obtain optimized experimental conditions for the buffalo skin gelatin and the experimental results of the 4-factors, 3-level central composite design are presented in Table 2. The coefficients and P-values on all of the variables of linear ($X_1$, $X_2$, $X_3$, $X_4$), quadratic ($X_1^2$, $X_2^2$, $X_3^2$, $X_4^2$) and interactions ($X_1{X}_3$, $X_1{X}_4$, $X_2{X}_3$, $X_2{X}_4$, $X_3{X}_4$) terms were determined and are shown in Table 3. The p-value of <0.0001, <0.0001, and <0.0001 implies that the total regressions model of yield, gel strength, and hydroxyproline content were statistically significant (p < 0.05) at 95% probability level, as a consequence ANOVA demonstrated that the anticipated 2nd order model was statistically valid. While the lack of fit values of Y₁, Y_2, and Y₃ did not show a significant p-value; p = 0.6817, p = 0.3501, and p = 0.2347. The lack of fit value reflects the model’s fitness, where the non-significance lack of fit showed that the model was valid for the present work [28]. As a result, the quadratic polynomial equation-based response surface model was statistically significant. Moreover, in agreement with R² values, the 2nd-order surface response models were acceptable to represent the relationships between independent variables and responses measured in the real system [29]. On top of that, relatively low values of coefficient variation (C.V. %) for Y₁; 15.47%, Y₂; 19.73% and Y₃; 7.32% showed that the models' predicted values are close to the actual values [30].

Table 3.

The significance probability (p-value) of regression coefficients in the final reduced models.

Variables	Yield (%)	Gel Strength (g)	Hydroxyproline content (g/100 g)
Model	<0.0001	<0.0001	<0.0001
$X_1$	0.3521	0.3309	0.0198
$X_2$	0.8543	0.00697	0.9356
$X_3$	<0.001	<0.0001	<0.0001
$X_4$	<0.001	0.0121	0.011
$X_1^2$	0.0026	<0.0001	<0.0001
$X_2^2$	0.1759	<0.0001	0.3646
$X_3^2$	0.0006	<0.0001	<0.0001
$X_4^2$	0.0006	<0.0001	0.7362
$X_1{X}_2$	0.5803	0.2592	0.2048
$X_1{X}_3$	0.1764	0.0549	0.9791
$X_1{X}_4$	0.7833	0.0448	0.6725
$X_2{X}_3$	0.9591	0.3576	0.1358
$X_2{X}_4$	0.3761	0.1985	0.9717
$X_3{X}_4$	0.0442	0.0058	0.0001
Lack of fit	0.6817	0.3501	0.2347
R2	0.9467	0.9534	0.9378
Adjusted R2	0.8969	0.9099	0.8797
Predicted R2	0.7860	0.7825	0.6960

X₁ (concentration of NaOH, %), X₂ (pre-treatment time of alkali solution, h), X₃ (extraction temperature, °C), and X₄ (extraction time, h).

The optimal conditions were determined by the highest level of desirability of 1.000 where NaOH concentration ($X_1$) = 0.77 M, pre-treatment time ($X_2$) = 5.08 h, extraction temperature ($X_3$) = 62.93 °C and extraction time ($X_4$) = 11.62 h with predicted response values of yield ($Y_1$) = 17.8%, gel strength ($Y_2$) = 237.8 g and hydroxyproline content ($Y_3$) = 41.9 g/100 g. A maximum yield of 16.91 ± 0.54% was achieved along with 236.50 ± 8.61 g of gel strength and 41.4 ± 2.11 g/100 g of hydroxyproline content respectively. By cooperating with the optimal response value, the correlation coefficients of each measured value were on par with the range that had been predicted. This demonstrated that the measured values matched those predicted values obtained by the model system. The following fitted regression equations were produced after analysing and eliminating any insignificant factors (p > 0.05) (Table 4):

Table 4.

Response surface models of gelatin from Buffalo skin.

Responses	Regression Equation	R2	p-value
Yield (%)	Y1 = 20.29 + 0.6.25 $X_3$ + 3.18 $X_4$ − 1.29 $X_3{X}_4$ - 1.62 $X_1^2$ - 1.95 $X_3^2$ - 2.18 $X_4^2$	0.9467	<0.0001
Gel strength (g)	Y2 = 251.20 + 27.48 $X_3$ − 14.18 $X_4$ + 13.33 $X_1{X}_3$ − 19.57 $X_3{X}_4$ − 25.38 $X_1^2$ − 0.50.79 $X_2^2$ - 53.69 $X_3^2$ - 29.85 $X_4^2$	0.9534	<0.0001
Hydroxyproline content (g/100 g)	Y3 = 42.63 − 1.40 $X_1$ + 4.26 $X_3$ + 2.16 $X_4$ − 3.42 $X_3{X}_4$ − 4.50 − 3.16 $X_3^2$	0.9378	<0.0001

Y₁ (yield, %), Y₂ (gel strength, g), Y₃ (hydroxyproline content, g/100 g), $x_1$ (Concentration of NaOH, M), $x_2$ (pre-treatment time, h), $x_3$ (extraction temperature, °C), $x_4$ (extraction time, h).

3.2. Effect of extraction variables on gelatin yield

Response surface methodology was used to illustrate the effects of NaOH concentration, pre-treatment time, extraction temperature, and extraction time on the gelatin yield. The statistical significance of the quadratic polynomial model equation was evaluated by the analysis of variance. The analysis variance of ANOVA showed that the total regression model of yield possessed a high significant (p < 0.0001) value with the experimental data (Table 3). To develop the fitted response surface model equation, all insignificant terms (p > 0.05) were eliminated, and the fitted model obtained by RSM was shown in Eq. (3). It was noticeable that linear of extraction temperature ($X_3$) showed a major positive effect on the yield, followed by extraction time ($X_4$). While for interaction coefficient ($X_3{X}_4$) only extraction temperature and time were significant. On the other hand, for quadratic terms, extraction temperature ($X_3^2$) and extraction time ($X_4^2$) was found to be significant (p < 0.05) for the yield model.

$${\mathrm{Y}}_1=20.29+\mathrm{0.6.25}{X}_3+3.18X_4-1.29X_3{X}_4-1.62X_1^2-1.95X_3^2-2.18X_4^2$$ (3)

The gelatin yield was observed to have a slight positive correlation with NaOH concentration; 0.25–1.25 M but inversely with pre-treatment time; 1–11 h of the extraction process. Based on Fig. 2(a), gelatin yield increased with the NaOH concentration increase in the range of 0.25–0.80 M. However, at a concentration of 0.80–1.25 M, the uses of NaOH solution were less affecting to the buffalo skin, which can be observed as the graph of the gelatin yield curved downward (Fig. 2(a)). This might be due to the presence of a high concentration of hydroxide (-OH) group that lead to the degradation of extracted proteins in pre-treatment solutions [31]. NaOH solution is the most widely used in gelatin extraction [11,20,32,33] as it provides a more open collagen structure to the skin which facilitates the transfer rate of the solvent into the intercellular substance [34,35]. A similar study by Arsyanti et al. (2018) [20] that evaluated the effect of alkaline pre-treatment on the extraction of gelatin from buffalo skin (Bubalus bubalis), found that as the concentration of NaOH and NaOH-citrate acid at 0.25, 0.50 and 0.75 M increased, the gelatin yield was also increased. In addition, from our previous work, we found the increasing concentration of NaOH (0.3, 0.5, and 0.7 M) for pre-treatment subsequently increase the yield to 4.40, 7.52, and 9.52% respectively [21]. Whereas, gelatin extracted from chicken feet and skin using different NaOH concentrations of 0.025, 0.050, and 0.075 M for 80 min showed no significant differences in gelatin yield [14]. The various scenarios of NaOH’s effects on the physical and chemical properties of gelatin can be due to the level of differences in NaOH concentration and type of raw material used in each study.

Fig. 2.

Three-dimensional response surface plots of interactive effects of NaOH concentration, pre-treatment time, extraction temperature, and extraction time on yield, gel strength, and hydroxyproline content.

Besides NaOH concentration, Fig. 2(b) shows that the gelatin’s yield was significantly increased with the increase of extraction temperature from 40 to 80 °C and extraction times from 3 to 31 h. This was in accordance with Aydin & Omer (2018) [36] as they reported that the yield of gelatin extracted from chicken deboned increased approximately from 7 to 12 g/100 g at temperatures 70–85 °C. At high temperatures above 75 °C, the covalent bonds including intermolecular crosslinks and peptide bonds break down, resulting in smaller α-chain fractions thus increased the collagen solubility [37,38]. Based on the experimental data, it was observed that the hydrolysis temperatures from 40 to 80 °C and extraction time from 3 to 31 min affected yield significantly (p < 0.05) while linear and interactions coefficients of concentration of NaOH and pre-treatment times were not significant p > 0.05 (Fig. 2(b)). This can be seen clearly where at 40 °C (run number 30, Table 2), the gelatin yield of 0.69 g was the lowest among other runs, and the extracted gelatin yield of 24.76 g was the highest when extracted at 80 °C (run number 25, Table 2). This suggested that gelatin requires an appropriate temperature in order to disrupt the collagen structure by cleaving cross-links at the terminal of telopeptide regions and cleaving the collagen triple-helical domain, which resulted in the production of gelatin [39]. Although pre-treatment with alkali is crucial in the process of collagen to gelatin, yet collagen must be heated above the transition temperature of its triple helix structure, also known as the ‘super helix,’ which causes the helical structure of the collagen molecule chains to collapse, resulting in collagen solubilization, and increased hydration capacity [40,41].

3.3. Effect of extraction variables on gel strength of gelatin

Based on the developed model by RSM, the 3-D surface plots of the model were generated to show the effects of variables and their interaction which were shown in Fig. 2 (c) and (d), and the response model was represented in Eq. (4) below:

$${\mathrm{Y}}_2=251.20+27.48X_3-14.18X_4+13.33X_1{X}_3-19.57X_3{X}_4-25.38X_1^2-\mathrm{0.50.79}{X}_2^2-53.69X_3^2-29.85X_4^2$$ (4)

Fig. 2 (c) and (d) show the interaction effect of concentration of NaOH, pre-treatment time, extraction temperature, and extraction time, respectively. The gel strength of gelatin was increased along with pre-treatment time and NaOH concentration. However, the value of gel strength decreased with Bloom values of approximately less than 110 g at 8 h–10.6 h of pre-treatment time. This may be due to the over-swelling of collagen structure as time increased, thus causing more low molecular weight peptides of α and β chains to be released into the system. Extraction temperature and extraction time seem to have the same trend in which the temperature and time were proportion to the value of gel strength. However, at 70 °C the value of gel strength started to decrease. This might be due to the low proportion of the high molecular weight of protein produced when protein was exposed to high temperature [9]. Thus, causing unstable interactions between the compound and producing poor triple helical structure. This was in line with previous studies of gelatin extracted from Kuma Kuma skin [42], blackspotted croaker skin [33], chicken deboner skin [8], and grass carp skin [43], in which the value of gel strength increased with extraction temperature and time until it reached a certain limit, and then started to decline.

In general, gelatin with high yield most likely possesses low gel strength [44,45]. This phenomenon is accurate for run number 25 (Table 2) which posed a higher gelatin yield of 24.76 g compared to the other run numbers, yet it produced the lowest gel strength (1 g). According to Sha et al. (2019) [46], high extraction temperature resulted in fast degradation of the α and β chains of the gelatin, hence low internal force is required to form a gel and leads to a low value of gel strength. This was correlated well with the presence of a faint band that was observed in run number 25 during electrophoresis (Fig. 3). It showed the differences in gel strength were governed by molecular weight distribution. The low molecular weight of protein had a shorter chain which resulted in a poor inter-junction zone network thus indicated by a low gel strength value [41]. It was undeniable that lengthening gelatin extraction processes, or adjusting either concentration of acid/alkali, pre-treatment time, extraction temperature, or extraction time, could affect the gelatin yield. However, longer extraction or vigorous extraction method might produce low molecular weight (Mw) peptides, and this is not an advantage as low Mw peptides tend to produce weak gel strength. Based on Table 2, most of the gelatin that had more than 20 g yield showed low gel strength (run numbers 7, 15, 19, 20, 23, 24, and 25). Hence, protein modification such as the addition of salts, glycerol, glutaraldehyde, enzymes, and sugars could be added to enhance the functional properties of gelatin [40].

Fig. 3.

The SDS-PAGE electrophoretic pattern of gelatin. The five panels indicated protein bands from Marker = protein molecular weight marker, STB = standard bovine gelatin, Run 25 = buffalo gelatin extracted at 0.75 M NaOH, 5.4 h pretreatment time, 80 °C extraction temperature and 17 h extraction time, Run 9 = buffalo gelatin extracted at 0.75 M NaOH, 5.4 pretreatment time, 60 °C extraction temperature and 3 h of extraction time, and Optimized Gelatin = buffalo gelatin extracted at 0.77 M NaOH, 5.08 h pretreatment time, 61.93 °C and 11.62 h extraction time.

3.4. Effect of extraction variables on hydroxyproline content of gelatin

ANOVA and linear regression analysis showed that the extraction temperature and extraction time were the most significant factors that affected the hydroxyproline content of buffalo skin gelatin. The intercept point and coefficients with p-value <0.05 provided a linear equation of R² = 0.9378, to predict the effect of the variables on hydroxyproline content (Eq. (5)):

$${\mathrm{Y}}_3=42.63-1.40X_1+4.26X_3+2.16X_4-3.42X_3{X}_4-4.50-3.16X_3^2$$ (5)

Fig. 2(e) and (f) showed the interaction effect of independent variables on hydroxyproline content of the buffalo skin gelatin. It was observed that there is a synergistic interaction effect between extraction temperature (X₃) and extraction time (X₄) on the hydroxyproline content of buffalo skin gelatin as shown in Fig. 2(f). A high amount of hydroxyproline (hyp) content of 40.4 and 40.47 g/100 g was produced when extracted at high temperatures of 70 and 80 °C and extraction times of 24 and 17 h (run number 19 and 25). The hydroxyproline content for both run number 19 and 25 was approximately 47% higher than gelatin produced in run number 30; 19.7 g/100 g which was extracted at 40 °C of extraction temperature with a long extraction time of 17 h. Moreover, albeit gelatin produced from run number 9 (Table 2) was extracted for a shorter time 3 h at 60 °C, the hydroxyproline content was higher; 39.98 g/100 g, than run number 30. This showed that a temperature above 40 °C is required to degrade three polypeptide chains tropocollagen that forms a triple helical structure into a linear polymer of amino acids, whose polymer chain is generally a repetition of amino acid glycine-proline-proline or glycine-proline-hydroxyproline [2]. A similar effect of temperature was reported for catfish bone gelatin [47], where the hydroxyproline recovery was at 61.56% when extracted at 60 °C compared to the extraction at 30 °C with only 23.12% of hyp recovery. In the present study, interestingly, it was noticeable that samples with low gel strength <10 g (run numbers 7, 15, 19, 25) showed a promising value of hydroxyproline content ranging from 35.74 to 40.47 g/100 g. This suggested that high hydroxyproline content does not necessarily produce high gel strength. Although imino acids (proline and hydroxyproline) were associated with the formation of stable triple helix structure with the formation of hydrogen bonds between free water molecules and the hydroxyl group of hydroxyproline, yet it was reported that high molecular weight is much more crucial to forming more organized triple helical structure by possessing better stabilizing interactions between compounds [48]. Hence, it can be suggested that high hydroxyproline content produces gelatin with low gel strength, but is favorable to the gelatin yield [2,48].

4. Conclusions

Gelatin extracted at the optimum parameter of 0.77 M NaOH concentration, 5.08 h of extraction time, 61.93 °C extraction temperature, and 11.62 h of extraction time has a promising property of yield, gel (Bloom) strength, and hydroxyproline content. It was observed that the buffalo skin gelatin exhibits comparable gel strength and hydroxyproline content to standard bovine gelatin, hence the potential to be an alternative ingredient to mammalian gelatin. Extraction temperature significantly affected all the response variables; yield, gel strength, and hydroxyproline content, which showed that the thermal factor is crucial for the conversion of collagen to gelatin. No significant difference (p > 0.05) was observed in the linear coefficient of alkaline pre-treatment towards yield and hydroxyproline content, however, it may be one of the important factors to assist in the swelling process of collagen during pre-treatment to remove non-collagenous materials. This study found the optimized parameters for the extraction of good quality gelatin from the underutilized source, buffalo skin. Further analyses of the physicochemical and functional properties of the optimized gelatin shall be obtained to characterize the gelatin produced. In addition, more variables could be explored to increase the gelatin process efficiency and improve the physicochemical and functional properties of buffalo gelatin in the future.

Declarations

Author contribution statement

Rabiatul Amirah Ramli: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Umi Hartina Mohamad Razali: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Nor Qhairul Izzreen Mohd Noor: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

Funding statement

Nor Qhairul Izzreen Mohd Noor was supported by the Ministry of Education Malaysia and Universiti Malaysia Sabah for the financial supported provided under Fundamental Research Grant Scheme (FRGS/1/2018/STG05/UMS/03/1).

Data availability statement

Data will be made available on request

Declaration of interest’s statement

The authors declare no conflict of interest.

Appendix A. Supplementary data

The following is the Supplementary data to this article.