Ultrasound-assisted extraction conditions optimisation using response surface methodology from Mitragyna speciosa (Korth.) Havil leaves.

The optimisation of the Ultrasound-Assisted Extraction (UAE) method was investigated by employing the Central Composite Rotatable Design (CCRD) of Response Surface Methodology (RSM). The UAE method was based on a simple ultrasound treatment using methanol as the extraction medium to facilitate the cell disruption of Mitragyna speciosa leaves for optimum extraction yield and Total Phenolic Content (TPC). Three different parameters comprising extraction temperature (X1: 25-50 °C), sonication time (X2: 15-50 min), and solvent to solid ratio (X3: 10-30 mL/g), and were selected as the independent variables, while two response variables were selected, namely extraction yield (Y1) and TPC (Y2). Based on the results, the developed quadratic polynomial model correlated with the experimental data is based on the coefficient of determination (R2) of extraction yield (0.9972, p < 0.0001) and TPC (0.9553, p < 0.0001). At 25 °C, 15 min sonication time, and 10 mL/g of solvent to solid ratio, the optimal conditions recorded an extraction yield and TPC of 22.69% and 143.51 mg gallic acid equivalent (GAE)/g, respectively. Furthermore, the actual response and the predicted values of the developed models correlated with each other as the Residual Standard Error (RSE) values were <5%. Meanwhile, the Liquid Chromatography- tandem Mass Spectrometry (LC-MS/MS) was employed to characterise the optimised M. speciosa extract and revealed the presence of major phytochemicals, including catechin, rutin, kaempferol, coumarin, gallic acid, chlorogenic acid, and caffeic acid. These compounds could exhibit certain therapeutic effects, such as anti-inflammatory, antibacterial, and antioxidant. Therefore, the findings in this study supported the suggestion that the various available bioactive compounds besides alkaloids contributed to the bioactive properties in M. speciosa, making it an effective traditional herbal medicine to treat various illnesses.

Introduction

One of the secondary metabolite products in plants are phenolic compounds that are synthesised from shikimic acid and pentose phosphate through phenylpropanoid metabolisation. Ranging from simple phenolic molecules to highly polymerised compounds, these phenolic compounds are characterised by their benzene rings with one or more hydroxyl substituents [1]. Phenolic compounds exhibit several crucial defence responses in the human body, including anti-inflammatory, anti-ageing, antioxidant, and antiproliferative activities [2]. Realising the versatile antioxidant properties of the phenolic compounds [3], new ideas have been proposed to exploit the natural antioxidant features of phenolic contents from the plant for food, pharmaceutical, and cosmetic applications [4], [5] instead of relying on synthetic antioxidants, such as butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT) [6]. Moreover, the safety issue over the use of synthetic antioxidants remains unclear [7], forcing all parties to search for an alternative source of antioxidants.

The extraction process is a key stage for the identification of bioactive components during the production of phenolic compounds from plants [8]. To date, a variety of factors is found to influence the efficiency of the extraction process. Besides the type of solvent used in the extraction process, the type of extraction method also plays a substantial effect on the overall yield, the chemical compositions, and the tested biological activities. Since phenolic compounds are naturally unstable, each phenolic compound of interest requires a specific approach for extraction and optimisation [9], [10]. Over the years, the ultrasound treatment method has been widely employed for the extraction of plant materials, in particular phenolic compounds. The Ultrasound-Assisted Extraction (UAE) method deploys a unique form of ultrasound wave across the solvent that affects the acoustic cavitation (mechanical and chemical effect) of the plant cell [11].

Numerous studies have successfully employed UAE to extract bioactive compounds from various plants [12], [13], [14]. The ability to achieve high extraction efficiency at a relatively low-temperature condition makes UAE a frequently used extraction method. In addition, UAE is a cost-effective method that offers an easily accessible approach over other conventional extraction techniques. Studies on the improvement of the solvent penetration and enhanced mass transfer across the cell membrane recorded higher extract yields [15], therefore, UAE is regarded as a promising extraction method.

Currently, there is a need to develop a reliable model to optimise the independent variables, including the sonication time, the concentration and volume of solvent, extraction temperature, and solvent to solid ratio, to achieve a sufficient amount of phenolic recovery and extract yield from plant samples [16]. Both the empirical and statistical approaches can be used to study the optimisation process [17], [18]. In view of this, the Response Surface Methodology (RSM) is a useful technique that involves mathematical and statistical analysis, which is acquired from the fit of empirical models to the obtained data from experiments. Generally, the linear or square polynomial functions are utilised to describe the studied system. Therefore, an optimisation study can be performed to investigate the experimental conditions [19]. Since the RSM reduces the total amount of experimental trials, operational cost, and time compared to other techniques, thus, it is widely employed in the optimisation of extraction of several compounds, such as polysaccharides, phenolic compounds, and carotenoids from different plant materials [20], [21].

Mitragyna speciosa (Korth.) Havil is a tropical plant from the Rubiaceae family that is native to Southeast Asia countries. The locals in Malaysia call them ‘biak-biak’ or ‘ketum’ and ‘kratom’ in Thailand. M. speciosa has been applied as a traditional medicine to heal muscle fatigue and tiredness, as well as a herbal cure for a number of common sicknesses, such as diarrhoea, diabetes, coughing, and hypertension. Besides its similar function as an alternate substance for morphine or opium to cure drug addiction [22], [23], [24], various studies have discovered that M. speciosa possesses various biological activities, such as anti-inflammation [23], antinociceptive [23], antioxidant [25], [26], and antimicrobial [26]. Apart from the well-known presence of alkaloids that exhibit significant bioactive functionalities, other phytochemicals, such as phenolic compounds from the crude extract, are also believed to contribute to the synergistic effect of the biological activities in M. speciosa.

Recent studies have reported that phenolic compounds are produced in large amounts and exhibit antioxidant, antibacterial, anti-inflammatory, and anticancer properties, which can potentially be exploited for healthcare and pharmaceutical purposes [27], [28]. Previously, methanol was used as the extraction solvent and recorded the highest extraction yield and phenolic content [29]. Therefore, this study aimed to optimise the UAE conditions using methanol to obtain the maximum extraction yield and phenolic compounds of M. speciosa leaves. The Central Composite Rotatable Design (CCRD) of RSM was utilised to optimise the extraction temperature, sonication time, and solvent to solid ratio. Following the statistical analyses, including the regression analysis and the Analysis of Variance (ANOVA), the constructed model was verified to evaluate the reliability of the model. Additionally, the Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) was carried out to detect the presence of major phenolic compounds in the plant extract.

Materials and methods

Plant material

Fresh samples of M. speciosa leaves were obtained from Perak, Malaysia. Following the identification process by Dr Mohd Firdaus Ismail, the samples were submitted at the herbarium of the Institute of Bioscience, Universiti Putra Malaysia (specimen voucher -MFI 0121/19).

Chemicals and reagents

In this study, all reagents and chemicals used were of analytical grade, including methanol (CH3OH), standard gallic acid, Folin-Ciocalteu phenol reagents (Sigma-Aldrich, Darmstadt, Germany), and sodium carbonate (Na2CO3) (Merck, Darmstadt, Germany).

Sample preparation

The collected M. speciosa leaves were thoroughly cleaned using tap water and cut into smaller pieces. The leaves were kept overnight at −80 °C, followed by lyophilisation using a FreeZone freeze drier system (Labconco, Missouri, USA). A laboratory grinder was then used to ground the lyophilised leaves into a fine powder form (1.0 mm) and stored in a sealed jar.

UAE of M. speciosa

Methanol was used as the solvent for the UAE method. Approximately 1.0 g of dried M. speciosa powder was transferred into a conical flask with the desired solvent ratio. The flask was placed in a Power Sonic 405 ultrasonic bath (Hwashin Technology Co., Seoul, Korea) and the extraction temperature and sonication time were fixed according to the experimental design.

Determination of extraction yield

Once the ultrasound process was completed, the solution in the flask was filtered. Then, a Rotavapor R-21 rotary evaporator (Buchi, St. Gallen, Switzerland) was used to concentrate the extract at a fixed temperature of 40 °C. The concentrated product was freeze-dried to completely remove the excess solvent. The percentage of extraction yield was calculated based on the total dry weight for each run using the Equation (1):

Total Phenolic Content (TPC)

The TPC of the extract was analysed through the Folin-Ciocalteu assay according to a previous method [30] with few adjustments. For each extract, 0.5 mL of Folin-Ciocalteu reagent was added to 100.0 μL of sample with 1.0 mg/mL of standard gallic acid solution and left at room temperature for 3 min. Next, 1.0 mL of 7.5% Na2CO3 was added to the mixture and heated at 95 °C for 1 min. The formation of a blue complex should be visible after the mixture was left to cool down to room temperature. A UV-1611 Ultraviolet (UV)-visible spectrometer (Shimadzu Co., Kyoto, Japan) was employed to determine the absorbance of the sample at 760 nm. The TPC concentration was referred to as mg of Gallic Acid Equivalent (GAE) per gram of dry weight extract (mg GAE/g).

Experimental design using the RSM

The CCRD with a three-factor-five level was utilised to evaluate the optimal conditions of the M. speciosa leaves extraction. Three independent variables were selected comprising extraction temperature (X1: 25–50 °C), sonication time (X2: 15–50 min), and solvent to solid ratio (X3: 10 to 30 mL/g), while the two response variables were analysed, which were the extraction yield (Y1: weight %) and the TPC (Y2: mg GAE/g). The Design Expert® version 7.0 software (Stat. Ease Inc., Minneapolis, USA) was employed to construct a total of 20 experiments. The experiments were carried out using three independent variables that include eight factorial points, six axial, and six centre points. The independent variables and their corresponding coded levels are listed in Table 1.

Table 1

The three independent variables and their coded levels for the CCRD setup.

Symbol	Independent variable	Coded level
		−1.68	−1	0	+1	+1.68
X1	Extraction temperature (°C)	16.48	25.00	37.50	50.00	58.52
X2	Sonication time (min)	3.07	15.00	32.50	50.00	61.93
X3	Solvent to solid ratio (mL/g)	3.18	10.00	20.00	30.00	36.82

Statistical analysis

Two statistical analyses, namely response surface analysis and ANOVA, were performed to investigate the statistical significance of the model terms, the coefficients of regression, and correlate the experimental data through mathematical models to optimise the overall region of the response variable. The response variables were predicted via the development of a quadratic polynomial model, as shown in the Equation 2:where Y represents the predicted dependent variable; b is the constant that set the response to the central point of the experiment; b, b, and b are the linear effect terms for the regression coefficients; b, b, and b are the terms for the quadratic effect; and b, b, and b are the terms for the interaction effect, respectively.

The regression analysis is a practical technique to achieve a reliable model in which the determination coefficient (R2) and t-test (p < 0.05) were used to predict the adequacy of the model. In addition, the regression coefficients significance was determined using the t-test, while non-significant coefficients were dismissed to gain a reduced model. The response surface plots were established to determine the relationship between the independent variables (X1, X2, and X3) and the response variables (Y1 and Y2). The desirability approach in the design expert software was utilised to select the optimal conditions for the extraction process based on the condition that achieved the maximum TPC from M. speciosa leaves.

Verification of the constructed model

The fitness of the constructed model was assessed by comparing the actual values of the extraction yield and TPC at optimum conditions with the predicted values. The Residual Standard Error (RSE) percentage was calculated for each response according to the Equation (3):

Liquid chromatography

Liquid Chromatography (LC) was conducted using the Dionex UltiMate 3000 Ultra High-performance Liquid Chromatography system (Thermo Fisher Scientific, Massachusetts, USA) equipped with a Thermo Syncronis C18 column (2.1 mm × 100 mm × 1.7 µm) (Thermo Fisher Scientific). During the analysis, the temperature was retained at 55 °C at a flow rate of 450 µL/min. Solvent A (water mixed with 0.1% formic acid) and solvent B (acetonitrile mixed with 0.1% of formic acid) were used as the mobile phases. Beginning at 0.5% of solvent B, the gradient elution program was set for 1 min, followed by 15 min from 0.5% to 99.5% of solvent B before being maintained for 4 min, and returned to 0.5% of solvent B in 2 min. The injection volume was set at 2 µL.

Data acquisition

Tandem Mass Spectrometry (MS/MS) were performed using the QExactive HF Orbitrap Mass Spectrometry System (Thermo Fisher Scientific, Massachusetts, USA) coupled with a Heated Electrospray Ionisation (HESI) probe. The data acquisition was programmed between an m/z of 100–1000 for a full MS scan. The resolutions of the full MS and MS2 scan were fixed at 60 k and 15 k, respectively, while the spray voltage of positive and negative mode were deployed at 3.9 kV and 3.3 kV, respectively. The ion source conditions were programmed as follows: capillary temperature of 320 °C, sheath gas flow rate, aux gas flow rate, sweep gas flow rate, and S-lens RF level of 28, 8, and 0, respectively, and aux gas heater temperature of 300 °C. Before the samples were analysed, the instrument was calibrated using positive and negative Pierce LTQ ESI Calibrations solutions (Thermo Fisher Scientific, Massachusetts, USA).

Data analysis

The data was processed using the Compound Discoverer version 2.0 software (Thermo Fisher Scientific, Massachusetts, USA). The analysis was set using the default workflow setting comprising background subtraction with blank data, alignment of the retention time, detection feature, determination of elemental composition, and library matching. The compound identification was performed by matching the MS/MS data against the mzCloud database integrated in the software with a cutoff score of ≥ 80% similarity.

Results and discussion

Multiple regression results and the analysis of the adequacy of the fitted model

The application of RSM with CCRD for the optimisation of extraction temperature, sonication time, and solvent to solid ratio on the extraction yield and TPC of the M. speciosa leaves was investigated. Several runs in each response, specifically the extraction yield (14, 15, 17, 19, and 20) and TPC (5, 6, and 8) were marked as missing independent variables (outliers). Hence, they were not included in the construction of the model. Based on the ANOVA result, it was revealed that the F-values of 200.17 and 38.96 for the extraction yield and TPC, respectively, implied the significance of all the models. The result was supported by the findings that the probability of the values attributed to noise was only 0.01%. The probability was relatively low for each response (p < 0.05), demonstrating that the models were significant. According to the actual values from the design matrices and the response variables of the predicted data using CCRD in Table 2, the extraction yield (Y1) and TPC (Y2) of the actual values varied from 10.41 to 31.64% and 126.51–154.25 mg GAE/g, respectively.

Table 2

The actual values for the design matrices and predicted data of the extraction temperature (X1), sonication time (X2), and solvent to solid ratio (X3) for the optimisation of UAE of M. speciosa leaves using the CCRD.

Run	Type	Independent variables			Code level			Response variable
		X1(°C)	X2(min)	X3(mL/g)	X1	X2	X3	Extraction yield, Y1(%)		TPC, Y2(mg GAE/g)
								Actual	Predicted	Actual	Predicted
1	Factorial	25.00	15.00	10.00	−1	−1	−1	18.24	17.83	142.88	147.33
2	Factorial	50.00	15.00	10.00	1	−1	−1	17.26	17.41	145.16	143.18
3	Factorial	25.00	50.00	10.00	−1	1	−1	20.57	20.58	133.16	127.20
4	Factorial	50.00	50.00	10.00	1	1	−1	21.65	20.90	147.24	154.05
5	Factorial	25.00	15.00	30.00	−1	−1	1	28.55	29.54	153.06	146.03
6	Factorial	50.00	15.00	30.00	1	−1	1	30.48	30.71	129.79	135.53
7	Factorial	25.00	50.00	30.00	−1	1	1	30.26	30.35	126.51	128.27
8	Factorial	50.00	50.00	30.00	1	1	1	31.61	32.27	153.43	148.76
9	Axial	16.48	32.50	20.00	−1.68	0	0	27.25	26.96	132.97	136.89
10	Axial	58.52	32.50	20.00	1.68	0	0	28.27	28.21	154.24	150.63
11	Axial	37.50	3.07	20.00	0	−1.68	0	25.30	24.84	140.97	140.16
12	Axial	37.50	61.93	20.00	0	1.68	0	28.35	28.46	133.24	134.37
13	Axial	37.50	32.50	3.18	0	0	−1.68	10.47	11.18	146.51	144.44
14	Axial	37.50	32.50	36.82	0	0	1.68	31.64	30.58	136.51	138.90
15	Center	37.50	32.50	20.00	0	0	0	27.91	26.48	133.79	134.40
16	Center	37.50	32.50	20.00	0	0	0	27.36	26.48	135.97	134.40
17	Center	37.50	32.50	20.00	0	0	0	24.77	26.48	135.70	134.40
18	Center	37.50	32.50	20.00	0	0	0	27.22	26.48	134.51	134.40
19	Center	37.50	32.50	20.00	0	0	0	28.48	26.48	133.24	134.40
20	Center	37.50	32.50	20.00	0	0	0	23.06	26.48	133.24	134.40

The quadratic polynomial models are shown in Table 3. The generated Equation. 4 and 5 described the empirical correlation between the independent and dependent variables for each response. The high values of R2 for the extraction yield (0.9972) and TPC (0.9804) from the ANOVA results demonstrated the significant correlation between the CCRD design and the developed quadratic polynomial models. Basically, for each addition of a variable to a model, the R2 value would increase, irrespective of the significance of the statistic. Furthermore, the adjusted R-square (Adj. R2) was considered an essential parameter that determines the adequacy of the model. The Adj. R2 also signifies the descriptive power of the regression models and simultaneously includes the multiple variables. The tally value increases only when a particular variable improves the model significantly than that could be achieved normally by probability. As the Adj. R2 values were 0.9923 and 0.9553 for the extraction yield and TPC of M. speciosa, respectively, the findings verified the predictability of the models for the determination of the optimal conditions required to achieve the maximum extraction yield and TPC value of the M. speciosa leaves extracts (Fig. 1).

Table 3

The quadratic polynomial equations (Equation 4 and 5) for the two responses based on the coded factors.

Responses	Equations	R2	R2(adjusted)	Regression (p-value)	Lack-of -fit
Y1	27.27 + 0.37X1 + 1.08X2 + 5.45X3 + 0.21X12 – 0.12X22 – 2.62X32 + 0.19X1X2 + 0.40 X1X3 − 0.48X2X3 (4)	0.9972	0.9923	< 0.0001	0.1261
Y2	134.44 + 6.06X1 – 2.04X2 – 2.27X3 + 3.09X12 + 0.79X22 + 2.35X32 + 2.27X1X2 + 1.79X1X3 + 0.053X2X3 (5)	0.9804	0.9553	< 0.0001	0.1472

Note: Y1 and Y2 represent the predicted responses comprising the extraction yield and TPC, respectively, while X1, X2, and X3 are the independent variable values, namely extraction temperature (°C), sonication time (min), and solid to solvent ratio, respectively.

Fig. 1

The predicted data versus actual values of the a) extraction yield and b) TPC of M. speciosa leaves.

In terms of the Coefficients of Variation (CV), the models recorded the CV for the extraction yield and TPC of 2.11% and 1.08%, respectively. Generally, the acceptable CV value should be lower than 20%. Therefore, the results satisfied the reproducibility of the models. Besides the CV, the lack-of- fit analysis determines the validity of the models in which an insignificant p-value of more than 0.05 (p greater than 0.05) indicates that the model fit accurately with the actual data. Since the lack- of-fit for the extraction yield and TPC were 0.1261 and 0.1472, respectively, hence, all the quadratic polynomial models in this study were accurate and reliable to predict the corresponding responses.

Effects of UAE parameters on the extraction yield

Optimising the extraction yield is vital, particularly in the development of natural product and drug synthesis, since higher extract yield could reduce the overall production cost [31]. The effects of three independent variables on the extraction yield were evaluated according to the significant coefficient (p < 0.05) of the quadratic polynomial equation. Based on the results, the extraction yield (Y1) was significantly influenced by the extraction temperature, sonication time, and solvent to solid ratio (p < 0.05) in the three first-order linear effects (X1, X2, and X3), one second-order quadratic effect (X32), and one interactive effect (X2X3). According to the linear coefficients and the corresponding predicted model obtained for Y1 in Table 3, the X1, X2, and X3 positive coefficients demonstrated that the extraction yield was enhanced with the increment of the extraction temperature, sonication time, and solvent to solid ratio.

The relationships between the three extraction parameters and the extraction yield were illustrated through the three-dimensional (3D) response surface plots (Fig. 2a–c). The response surface plot in Fig. 2a displays the function of the extraction temperature versus sonication time on the extraction yield percentage at a constant solvent to solid ratio of 20 mL/g. According to the results, the increase in sonication time to 50 min from 15 min recorded a higher extraction yield (27.88%). In addition, the interaction effect suggested that a longer sonication time was more effective in obtaining a higher extraction yield (28.67%) at a maximum extraction temperature of 50 °C. The improved efficiency was due to the simultaneous action of the sonication that promoted the hydration and fragmentation reaction while expediting the rate of mass transfer of solutes to the extraction solvent and avoiding substantial solvent degradation [32], [33].

Fig. 2

Response surface 3D plots of; the interaction effect of extraction yield as a function of extraction temperature, sonication time, and solvent to solid ratio (a–c); the interaction effect of TPC as a function of extraction temperature, sonication time, and solvent to solid ratio (d–f).

Fig. 2b shows the effect of extraction temperature versus solvent to solid ratio at a constant sonication time of 32.50 min. The extraction temperature contributed an insignificant influence on the extraction yield, as shown by the slow linear increase of the yield extract from 26.11% to 26.50% as the extraction temperature rose from 25 °C to 50 °C. The increment of the extraction temperature would result in either a negative or positive effect on the extraction yield. On one hand, the increasing extraction temperature reduces the solvent density, thus, decrease the extraction yield. In fact, an excessive extraction temperature degrades certain phytochemical compounds and should be avoided. On the other hand, higher extraction temperature also increases the mass transfer rate by promoting solubility of the solute, therefore, enhances the extraction yield [31]. It should be noted that the increase in extraction yield only took place when the solvent to solid ratio also increased.

The response surface 3D plot in Fig. 2c shows the function of sonication time versus solvent to solid ratio at a fixed extraction temperature of 37.50 °C. The surface plot revealed that the extraction yield improved with the increase in solvent to solid ratio. The extraction yield was 30.57% when a higher solvent to solid ratio was applied (30 mL/g). It was believed that higher solvent to solid ratio conditions allow more solvent to enter the cells as well as more compounds to permeate into the solvent. However, the solvent reached a saturation level at a faster rate during the extraction process when the solvent to solid ratio was low [31], hence affecting the overall extraction yield performance.

Effects of extraction parameters on TPC

The TPC (Y2) was significantly influenced by the extraction temperature, sonication time, and solvent to solid ratio (p < 0.05) in the three linear effects (X1, X2, and X3), two quadratic effects (X12 and X32), and one interactive effect (X1X2). According to the linear coefficients and the corresponding predicted model for Y2 in Table 3, the positive coefficient for X1 indicated that the TPC increased with the increment in extraction temperature. However, the negative coefficient for X2 and X3 showed that the TPC reduced with the increased sonication time and solvent to solid ratio.

The response surface 3D plots describe the relationship between TPC and the three extraction parameters, as shown in Fig. 2d–f. The interaction between the extraction temperature and sonication time on TPC in Fig. 2d showed that a low TPC was achieved (127.51 mg GAE/g) when a lower extraction temperature (25 °C) was used, while the solvent to solid ratio was constant at 20 mL/g. However, the TPC value increased when the extraction temperature rose to 50 °C (maximum extraction temperature). The interaction effect suggested that the longer sonication time was effective in achieving a higher TPC value of 45.03 mg GAE/g. The finding was similar to the results in Fig. 2a, where a longer sonication time at high extraction temperatures produced a higher extraction yield. The increase of extraction temperature could have contributed to the enhanced diffusivity of the solvent into cells, a higher rate of cavitation bubble formation, and increased the solubility and desorption of the phenolic compounds from the cells [34], [35], consequently enhanced the yield of phenolic compounds.

At a constant sonication time of 32.50 min, the influence of different extraction temperatures and solvent to solid ratio on the TPC under is presented in Fig. 2e. It was observed that the TPC value increased with the decrease in solvent to solid ratio. At 30 mL/g solvent to solid ratio, the TPC value was 129.75 mg GAE/g. In contrast, as the solvent to solid ratio was lowered to 10 mL/g, a considerable increase in the TPC was noticed of up to 137.76 mg GAE/g. The result was associated with the diluting effect of the solvent to solid ratio, which obtained a lower TPC with a higher diluted solvent [36]. Nonetheless, reducing the usage of solvent for the extraction process was extremely necessary and practical to achieve a cost-efficient and economically viable extraction method [35], [37]. Meanwhile, the interaction effect suggested that the higher extraction temperature (50 °C) was effective to attain a higher TPC value at both the minimum and maximum solvent to solid ratio.

Fig. 2f shows the surface plot as a function of sonication time versus solvent to solid ratio at a fixed extraction temperature of 37.50 °C. The surface plots revealed that the TPC value was 137.23 mg GAE/g when a lower sonication time (15 min) was used. However, the TPC was reduced to 133.25 mg GAE/g when the extraction temperature increased to 50 min. In addition, the interaction effect suggested that the highest TPC value of 141.93 mg GAE/g was recorded when the lowest solvent to solid ratio (10 mL/g) and minimum sonication time (15 min) was applied. The result could be explained by the impact of the ultrasonic waves that disrupted the cell wall of the plant during the first 15 min of sonication. Therefore, intracellular material was released at a higher rate into the solvent and increased the TPC level. The UAE mechanism occurs mainly in two stages. The first stage, known as the washing stage, involves the dissolution of soluble components that takes place on the surface of the leaf matrix. This was followed by the second stage, known as the slow extraction stage, where diffusion and osmotic process facilitate the rate of mass transfer of the solute into the solvent from the leaf matrix [38]. Excessive exposure to ultrasound treatment at a longer sonication time resulted in the degradation of phenolic compounds in the extract [39]. It was also obvious that extending the sonication time was economically impractical [37].

Verification of the developed models

The adequacy of the developed final models was verified and evaluated through three randomised validation sets (Table 4). The actual values were compared with the predicted data by calculating the RSE percentages (Eq. (3)) and were only considered to agree with the predicted data if the RSE values were lower than 5%. Since all the calculated RSE values were<5%, as shown in Table 4, therefore, the results indicated the actual values and the predicted data were insignificantly different, therefore, verifying the acceptability of the models.

Table 4

The actual values and predicted data for the model verification.

Set	Independent variable			Response variable
	X1(°C)	X2(min)	X3(mL/g)	Extraction yield, Y1(%)			TPC, Y2(mg GAE/g)
				Actual	Predicted	RSE (%)	Actual	Predicted	RSE (%)
1	33.00	15.00	20.00	25.59	26.03	0.02	139.56	136.47	2.26
2	40.00	30.00	15.00	23.31	23.74	0.02	132.33	137.55	3.79
3	40.00	30.00	20.00	26.81	27.19	0.01	140.77	136.00	3.51

Extraction parameters under optimised conditions

The CCRD analysis was used to evaluate the simultaneous maximum extraction yield and TPC of M. speciosa leaves under optimised conditions. According to the optimised results, the extraction temperature of 25 °C, sonication time of 15 min, and solvent to solid ratio of 10 mL/g recorded the optimum extraction yield and TPC of 22.69% and 143.51 mg GAE/g, respectively. A significantly higher TPC from the optimised conditions was obtained in this study compared to the results in recent studies [26], [40], which were 24.02 mg GAE/g and 105.58 mg GAE/g, respectively. The significant amount of TPC achieved in this study was suggested due to the utilisation of the optimised UAE method and environmental conditions, including temperature, light exposure, soil fertility, soil water, and salinity that could influence the build-up of secondary metabolites in most plants and affected the outcome of the study[41], [42].

The actual responses under optimum conditions demonstrated that all models were consistent with the predicted response values given that the values of RSE were below 5%, as shown in Table 5. The parameter range was chosen based on previous preliminary data (information not provided). In view of the need to minimise the actual cost of production, it was necessary to assess the suitable economic conditions to achieve the desired output through the utilisation of minimum energy and solvent consumption. Thus, this study demonstrated the cheapest and fastest approach to extract bioactive substances from M. speciosa leaves using the UAE conditions by narrowing the extraction temperature range (25–50 °C), sonication time (15–50 min), and solvent to solid ratio (10–30 mL/g).

Table 5

The values of actual and predicted response from the optimised UAE conditions.

Symbol	Response variable	RSE (%)	Actual value	Predicted value
Y1	Extraction yield (%)	3.09	21.99	22.69
Y2	TPC (mg GAE/g)	0.83	144.70	143.51

Analysis of the optimised M. speciosa leaves extract through the mass spectrometry

Table 6 shows the identified phenolic compounds from the MS analysis. Certain compounds, such as kaempferol, kaempferol-3-O-β-glucopyranosyl-7-O-α-rhamnopyranoside, caffeic acid, chlorogenic acid, rutin, and quercetin recorded similar findings with previous study [43]. Based on the compound identification, the detection of various phenolic compounds strengthens the suggestion that other bioactive compounds besides alkaloids were present and contributed to the diverse bioactive characteristics of M. speciosa.

Table 6

Identification of phenolic compounds from the optimised leaves extract of M. speciosa.

No.	Identified Compounds	Retention time	Molecular formula	Molecular weight
1	Quinic acid	0.57	C7 H12 O6	192.06
2	Catechin	4.351	C15 H14 O6	290.07
3	Chlorogenic acid	4.427	C16 H18 O9	354.09
4	Caffeic acid	4.656	C9 H8 O4	180.04
5	Fraxin	4.737	C16 H18 O10	370.09
6	Neochlorogenic acid	4.799	C16 H18 O9	354.09
7	3,4-Dihydroxybenzaldehyde	4.826	C7 H6 O3	138.03
8	Quercetin	5.53	C15 H10 O7	302.04
9	Rutin	5.558	C27 H30 O16	610.15
10	Quercetin-3β-D-glucoside	5.722	C21 H20 O12	464.09
11	Coumarin	5.893	C9 H6 O2	146.03
12	Kaempferol-3-O-β-glucopyranosyl-7-O-α-rhamnopyranoside	5.942	C27 H30 O15	594.15
13	Kaempferol	5.951	C15 H10 O6	286.04
14	Kaempferol-7-O-glucoside	5.975	C21 H20 O11	448.10
15	4,5-Dicaffeoylquinic acid	6.221	C25 H24 O12	516.12

Conclusion

This study successfully applied RSM as a practical approach to optimise the UAE conditions of M. speciosa leaves and provided further insights into the correlation between the independent and response variables. According to the ANOVA results and the R2 values of the extraction yield and TPC at 0.9972 and 0.9804, respectively, the findings suggested that the developed quadratic polynomial models were satisfactory to analyse the interactions between the independent and response variables. The actual values under the optimum conditions (extraction temperature of 25 °C, sonication time of 15 min, and solvent to solid ratio of 10 mL/g) corresponded well with the predicted values as the RSE percentages were below 5%. Furthermore, the characterisation of the phenolic compounds from the crude extract using LC-MS/MS identified the presence of various phenolics compounds, such as kaempferol, kaempferol-3-O-β-glucopyranosyl-7-O-α-rhamnopyranoside, chlorogenic acid, caffeic acid, rutin, and quercetin, which may possess therapeutic properties, such as anti-inflammatory, antibacterial, and antioxidant. Therefore, the results from this study would be utilised in the upscale development of pharmaceutical drugs containing M. speciosa leaves extract considering the economical evaluation that offers a cost-effective and time-efficient approach.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.