INTRODUCTION:

Medicinal plants have been used for thousands of years before the advent of current medicine and around 40 % of the new medicine is obtained from medicinal plants. A wide variety of medicinal and nutritional values has been attributed to the bark, roots, seeds, flowers and leaves of Moringa oleifera ¹. Moringa oleifera is an angiosperm plant, which includes several other species. It is a native of the Himalayas region that is widely grown in the tropical and sub-tropical world such as India, China, Afghanistan and Pakistan, And other countries like Saudi Arabia ².

Recently, researchers have shown a growing interest in Moringa oleifera leaves due to their potential for treating cardiovascular diseases, diabetes, cancer ³ and several biological activities like anti-bacterial ⁴, anti-inflammatory ⁵, anti-atherosclerotic ¹, anti-microbial ⁶, anti-obesity ⁷, anti-anthelmintic ⁸, and anti-oxidant⁹. Such useful properties have been related essentially to leaf composition in phenolic compounds such as flavonoids. Furthermore, these compounds have applications in agricultural, nutritional, industrial and pharmacological fields ¹⁰. This makes it a natural alternative to chemical drugs, which have many negatives and complications for human health and the environment ¹¹.

Therefore, there are scientific studies based on environmentally friendly extraction methods for active substances by using technologies that are effective, safe and economical while providing greater yields and improve the quality of the final product ¹⁰. Several methods have been used to extract the active substances like microwave-assisted extraction ¹², pressurized hot water extraction ¹³, solid–liquid extraction ¹⁴, soxhlet extraction ¹⁵, maceration extraction ¹⁶ and among these techniques, the ultrasonic-assisted extraction (UAE) is a modern method that used to extract compounds from plants while preserving their structural and molecular properties. This technique UAE also reduces solvent and energy consumption, saves time and simplifies processing ¹⁷.

In order to overcome problems of processing, optimization can be carried out applying multivariate statistic methods such as response surface methodology (RSM)¹⁸, which is a collection of statistical and mathematical techniques based on polynomial equations fitted to experimental data and allows the evaluation of multiple parameters and their interactions. The most advantageous feature of RSM is that it decreases the number of experimental points, saving time, energy and raw materials ¹⁹.

This study aimed to determine the optimal conditions to increase the flavonoids yield from Moringa oleifera leaves by optimizing the experimental variables namely: extraction time (min); extraction temperature (°C); solvent-solid ratio (ml/g). We applied the statistical technique (RSM) based on a Box–Behnken design (BBD) and using ultrasonic-assisted as effective extraction technology.

EXPERIMENHTAL SECTION:

Materials:

Moringa oleifera Leaves samples used in this study during the harvesting period in April 2019 were obtained from the Technical Institute for the Development of Saharan Agronomy (TIDSA) located at Ouargla-Algeria. The leaves were dried in an oven at 35 (ºC) for 12 hours. The samples were ground to their particle size through 0.5-1 (mm) sieve. For the products: Ethanol, aluminum trichloride and Rutin were supplied from Biochem Chemopharma (France).

Ultrasound-assisted extraction:

The extraction was carried out in an ultrasonic bath (JP Selecta. Spain) with a frequency of 40 (kHz), at three temperatures (15, 30 and 45 °C), for (20, 40 and 60 minutes), in a solvent-solid ratio of (5, 6 and 7 ml/g). Through mixing 10 g of leaves with a 70 % aqueous ethanol solution. Samples were put into bottles and stored at -4 (ºC) before starting the experiments.

Determination of the flavonoids content:

The total flavonoid content was determined according to the method of aluminum trichloride ²⁰. Briefly, 1 ml of 2% aluminum trichloride (AlCl₃) ethanol solution was mixed with 1 ml of extract solution. After incubation in the dark for 30 minutes at ambient temperature. The absorbance was measured at 415 nm on a spectrophotometer (Shimadzu UV-1800, Japan). The total flavonoid content is expressed as mg ER (equivalent of Rutin) per g of extract. Each experiment was done in triplicate.

Experimental design and statistical analysis:

The experimental design was used to maximize the extraction of flavonoids. The BBD as shown in equation (1), a method was chosen for the investigation of factors effects on the extraction process: extraction time (X₁), extraction temperature (X₂) and solvent-solid ratio (X₃) as shown in Table 1. The design requires three levels of each factor. Each factor was studied at three different levels (-1, 0, +1). The inclusion of three center points offered a more precise estimate for the adequacy of the model and experimental error. It also enabled the determination of the significance of the interactions between factors.

Where Y is the predicted response (total flavonoid content), is a constant., and are the linear, quadratic and interactive coefficients of the model, respectively. Accordingly, and represent the levels of the independent variables, respectively.

Table.1: Experiment design levels for various parameters.

Symbols	Independent Variable	Levels
Symbols	Independent Variable	-1	0	+1
X₁	Extraction time (min)	20	40	60
X₂	Extraction temperature (°C)	15	30	45
X₃	Solvent-solid ratio (ml /g)	5	6	7

Statistical analysis:

All the data were shown as the mean of three replicate. The Design-Expert software, version 12 (Stat-Ease Inc., Minneapolis, MN, USA) was applied for data analysis and optimization procedure. Statistical analysis of the model was performed to assess the analysis of variance (ANOVA), the quality of the polynomial model equation was evaluated, F-value and the determination of coefficient R².

RESULTS AND DISCUSSION:

Optimization of the flavonoids yield:

Table 2 shows the process variables, experimental and predicted values data of total flavonoid content. The percentage yield ranged from 40.83 (mg) to 72.25 (mg RE/g). The maximum yield of flavonoid 27.83 (mg) was recorded under the experimental conditions of extraction time 40 (min), extraction temperature 45 (°C) and solvent-solid ratio 5 (ml/g).

Table.2: Box–Behnken design and the response values for yields of Flavonoids.

Run	X₁ (min)	X₂ (°C)	X₃ (ml/g)	Flavonoids (mg RE/g)
Run	X₁ (min)	X₂ (°C)	X₃ (ml/g)	Experimental	Predicted
1	20	15	6	45.62	46.43
2	20	30	7	54.98	56.06
3	60	15	6	41.67	40.80
4	60	30	5	62.31	61.23
5	40	15	7	40.83	38.94
6	40	30	6	61.16	61.16
7	40	45	5	72.1	73.99
8	20	45	6	69.81	70.68
9	40	45	7	72.25	70.30
10	40	15	5	54.28	56.23
11	20	30	5	70.98	68.22
12	60	45	6	66.5	65.69
13	40	30	6	61.16	61.16
14	60	30	7	49.66	52.42
15	40	30	6	61.16	61.16

Model fitting:

The application of RSM offers, based on parameter estimates, an empirical relationship between the test variables and the response variable (extraction yield of flavonoids). By applying multiple regression analysis on the experimental data, the test variables and the response variable are related by the second-order polynomial as shown in equation (2):

Where Y is flavonoids yield and X₁, X₂, and X₃ are the values for the extraction time, extraction temperature and solvent-solid ratio respectively. Usually, there may be Insufficient or poor result in the exploration and optimization of the response surface, unless the model exhibits good adequacy, which makes the model fitness investigation essential. The p-value and F-value of the model were highly significant (p = 0.0012) and (25.13) respectively. The value of R² (97.84) indicated a good agreement between the veritable and predicted values of flavonoids yield. The value of the Adj-R² (93.94) also suggested that the model was highly significant. There was only about 6% of the total variation could not be described by the model. All these values showed that the model exhibited excellent fitness to the true behavior of the flavonoids extract process. As shown in (Table 3). That all the independent variables tested are significant (p < 0.05) according to (Table 3). Results also show that the extraction temperature is the most significant impact factor on the yields of total flavonoids content due to it having the greatest p-value (p < 0.0001), followed by the solvent-solid ratio it having the p-value (p = 0.0025), while the extraction time is p-value (p = 0.0365).

Table.3: Analysis of variance for the response surface quadratic model.

Source	SS	DF	MS	F-value	p-value
Model	1588.79	9	176.53	25.13	0.0012
X₁-Time	56.45	1	56.45	8.04	0.0365
X₂-Temperature	1206.88	1	1206.88	171.82	< 0.0001
X₃-Solvent-Solid Ratio	219.98	1	219.98	31.32	0.0025
X₁X₂	0.1024	1	0.1024	0.0146	0.9086
X₁X₃	2.81	1	2.81	0.3994	0.5552
X₂X₃	46.24	1	46.24	6.58	0.0503
	29.39	1	29.39	4.18	0.0962
	21.96	1	21.96	3.13	0.1373
	4.83	1	4.83	0.6877	0.4447
Residual	35.12	5	7.02	-	-
Lack of Fit	35.12	3	11.71	-	-
Cor Total	1623.91	14	-	-	-
R²	97.84	-	-	-	-
Adj-R²	93.94	-	-	-	-

Response surface analysis of flavonoids:

The 3D response surface is the schematic representation of the regression equation ²¹. They provide a method to visualize the relationship between experimental levels and responses of each variable and the nature of interactions between two examination variables, the relationship between independent and dependent variables as illustrated in the 3D representation of the response surfaces plots presented by the model for the yield of flavonoids (Figs. 1–3).

Figure.1: Response surface plot showing the effect of extraction temperature (°C) and extraction time (min) on the total flavonoids content from Moringa oleifera leaves.

Figure.2: Response surface plot showing the effect of solvent-solid ratio (ml/g) and extraction time (min) on the total flavonoids content from Moringa oleifera leaves.

Figure:3: Response surface plot showing the effect of extraction temperature (°C) and solvent-solid ratio (ml/g) on the total flavonoids content from Moringa Oleifera leaves.

These types of plots show the effects of two factors on the response each time. In the experimental range, the other factor was kept at level zero. As expected, a greater increase in flavonoids yield resulted when the extraction temperature (X₂) was increased in the range from 15 to 45 (°C), which may indicate that an extraction temperature 45 (°C) is required to achieve maximum increase (Fig. 1), It can be stated that this action makes sense the higher temperature increase diffusion rate and flavonoids solubility as well as reduce solvent viscosity. Leading to better extraction of flavonoids. Similar results reported while extracting phenolic compounds from Himanthalia elongate ²² and polysaccharides from Tremella mesenterica ²³. Likewise, a decrease in flavonoids yield resulted when the solvent-solid ratio (X₃) was increased in the range from 5 to 7 (ml/g). which may indicate that a solvent-solid ratio 5 (ml/g) is the peak, since then any higher amount of solvent will not change the driving power of the flavonoids in the solvent afterward, similar results reported while extracting phenolic compounds from and purple rice ²⁴ and pomegranate peel ²⁵. On the contrary, longer extraction time (X₁) had negative effects on the flavonoids extraction in the range from 20 to 60 (min), implying that the further increase in extraction time was not advantageous for the extraction because of the degradation of the produced flavonoids, but the result is satisfactory because this makes the process more economical the same result was gained while extracting phenolic compounds from perilla leaves ²⁶. Based on the results of linear and quadratic coefficients (Table 3) and RSM results, we concluded that the order of factors affecting the extraction of flavonoids was extraction temperature then solvent/solid ratio then extraction time.

Optimum conditions:

Based on the result of the response surface, the optimal conditions were extraction time 23 (min); extraction temperature 44 (°C); solvent-solid ratio 5.05 (ml/g). In order to warrant the fitness of the model equation, a confirmation experiment was carried out under the optimal conditions. The model predicted a maximum response of 74.34 (mg ER/g). In the verification experiment, the yield of flavonoids was 72.65 (mg ER/g). There was no significant difference between the predicted and practical value. This good correlation confirmed that the response model was adequate for reflecting the expected optimization. The results also indicated that the model was adequate for the extraction process.

Table.4: Optimum conditions, predicted and experimental value of response under these conditions.

Optimum conditions			Flavonoids (mg ER/g)
Extraction time (min)	Extraction temperature (°C)	Solvent-solid ratio (ml /g)	Experimental	Predicted
23	44	5.05	72.65	74.34

CONCLUSION:

The performance of the ultrasonic-assisted extraction of flavonoids from Moringa oleifera Leaves studied with a statistical method based on the response surface methodology in order to quantify and identify the variables which may maximize the yield of flavonoids. The three variables are chosen, namely extraction time (min), extraction temperature (°C) and solvent-solid ratio (ml/g) all have an influence on the yield of flavonoids during the extraction process. The optimal conditions obtained by RSM for the extraction of flavonoids include the following parameters: extraction time 23 (min), extraction temperature 44 (°C) and solvent/solid ratio 5.055 (ml/g). Under these conditions, the flavonoids yield was 72.65 (mg ER/g) which matched with the predicted value 74.34 (mg ER/g). This study indicates that Moringa oleifera is a very useful tree, which contains a high rate of flavonoids.

NOMENCLATURES:

RSM: Response surface methodology

BBD: Box–Behnken design

UAE: Ultrasonic-assisted extraction

ANOVA: Analysis of variance

SS: Sum of square

DF: Degree of freedom

MS: Mean square

ACKNOWLEDGMENTS:

All thanks and gratitude to the ‎Process Engineering Department of El-Oued University-Algeria for the efforts and contribution to achieving this study.

CONFLICT OF INTEREST:

The authors declare that there is no inconsistency for interests regarding the publication from this paper

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Received on 24.05.2021 Modified on 21.06.2021

Asian J. Research Chem. 2021; 14(5):363-367.

DOI: 10.52711/0974-4150.2021.00062