Microwave assisted adsorption based elution: a benign green process optimized by Box-Behnken modeling yields pure vasicine from Adhatoda vasica

2.1 Plant material and isolation of vasicine

Leaves of A. vasica were obtained from the medicinal plant farms in Thanjavur, India. The plant was authenticated by Dr. Jayendran, Department of Botany, Government Arts College, Ootacamund, India. A voucher specimen (JDB1512) was deposited in the Government Arts College. The leaves were shade dried, ground to a fine powder and used for extraction. Extracts were prepared by soaking 10 g of plant material in 100 ml of suitable solvents at room temperature for 24 h and repeated thrice with the residue. The extract was filtered through Whatman No. 1 filter paper, and then all the filtrates were pooled up successively and concentrated under vacuum by a Rotary evaporator (Buchi Rotavap R-210, Swiss). Based on high performance thin layer chromatography (HPTLC) profiling, the extract (methanol, Merck®, India) (12 g) which had the highest extractive value from leaves (100 g) was extracted through acid-base extraction. The extract was suspended in 5% HCl/water under reflux at 90°C for 1 h. Then, the extract was filtered through Whatman No. 1 filter paper and the filtrate was neutralized using liquor ammonia till pH 8. The precipitate formed was subjected to column chromatography with basic alumina (Merck®, India), and out of the subsequent fractions collected (1–13), fraction 4, 5 precipitated to afford vasicine. The precipitate was dissolved in absolute ethanol (Merck®, India), which gradually crystallized to give pure vasicine (420 mg).

2.2 Selection of extraction solvent

Some 10 g of dried plant material was soaked in 100 ml of respective solvents and the extract obtained was analyzed for target metabolites through HPTLC. The solvent which yielded the maximum amount of desired metabolites was taken for further modeling and RSM development. Water and 5% HCl in water were one of the solvents used in the extraction. Since, the motive of the current study is to extract maximum vasicine through environmentally friendly methodology, HCl in water was used as solvent for all further studies.

2.3 Selection of process parameters and experimental ranges

Before adopting a standardized experimental design protocol and development of the study by RSM based modeling, preliminary sets of tests were performed by following the classical “one variable at a time” approach to roughly select the applicable factors and the range of these factors in microwave assisted extraction. Firstly, the effect of microwave power using a multimode microwave device (Synthos 3000, Anton Paar, Austria) and microwave time on extraction were investigated, where five sets of plant material (1 g) containing 50 ml of 5% HCl/water kept in different microwave powers (300–900 W) each for times of 2–10 min. Secondly, we investigated the effect of solid/solvent ratio or solid loading where five sets of plant material (5–25%) containing 50 ml of 5% HCl/water were extracted in microwave (300 W) for 6 min. Next, we investigated the influence of %HCl in water ratio in the extraction process by considering 10 sets (2–20% of HCl in water) added to plant material (10%) extracted in microwave (300 W) for 6 min. Next, the influence of extraction time was studied where plant material (10%) in 6% HCl in water extracted in microwave (300 W) for 6 min was stirred using a magnetic stirrer at 200 rpm for different extraction times (1–24 h). The influence of agitation was then studied, where different sets containing the plant material were stirred at different agitation speeds (200–1000 rpm). The influential experimental parameters which depicted higher yield of vasicine obtained were analyzed by HPTLC and were further considered for systematic experimental design to find the optimum parameter, set through the RSM procedure.

2.4 Experimental design for RSM

Most influential parameters identified based on the preliminary experiments namely, solid loading (%), HCl/water (%), extraction time (3 factors) and three levels (-1, 0, +1) from their observed range were considered for the Box-Behnken method based experimental design to obtain the standard set of experiments for RSM based modeling and optimization. Three factors and three levels of Box-Behnken design generated 15 sets of experiments/runs which were carried out with three replicates, and the average is depicted in Table 1. To minimize the effects of inexplicable variability in the observed response due to inessential factors, the order of experiments was randomized. Experimental data thus obtained were fitted in a second-order polynomial model and regression coefficients were determined as in Eq. (1):

where, Y is the predicted response factor of the amount of vasicine (mg/g dry material [DM]), β₀ is the intercept and β_i, β_ii, β_ij and X_i are regression coefficients for linear effects, regression coefficients for squared effects, regression coefficients for interaction effects and the coded experimental levels of the parameters, respectively.

2.5 Analysis of response variables

Commercial grade vasicine (Sigma-Aldrich) was used as the standard for the experiments. The amount of vasicine obtained in each experimental run was the response variable taken. The amount was quantified by HPTLC densitometric analysis, which was performed on aluminum-backed plates (20×20 cm) coated with 0.2 mm layer of silica gel 60 F₂₅₄ (E-Merck, Germany). Sample application was done as 6 mm bands using a CAMAG automatic TLC sampler 4 (ATS4) applicator (Switzerland) fitted with a CAMAG microliter syringe. A constant application rate of 150 nl/s was maintained. The linear ascending technique was used to develop the plates to a distance of 80 mm with solvent system (ethyl acetate:methanol, 8:2 (%, v/v) with a few drops of ammonia) as the mobile phase in the CAMAG automatic developing chamber 2, which was previously saturated with mobile phase vapor for 30 min at 25°C. Developed plates were scanned using a CAMAG TLC scanner 4, and visualized under UV light at 254 nm and 365 nm. The scanned images were later processed for densitometric analysis using VisionCATS v1.4.0. Validation of the proposed HPTLC method was done based on guidelines of the International Conference on Harmonization [19]. A concentration of 100–800 ng/spot was checked for linearity of both the compounds, the concentration was plotted against peak area and a concentration curve was obtained. Specificity of this method was confirmed by analyzing the R_f values of spots of extracted vasicine and comparing those with standards. The HPTLC analysis for samples obtained from different RSM runs and the final optimized run were done using similar conditions used for standards. 1H Nuclear magnetic resonance (NMR) and 13C NMR spectra were determined on a Bruker-300 NMR spectrometer and chemical shifts were expressed as parts per million against Trimethylsilane (TMS) as internal reference. For Fourier transform infrared (FTIR) analysis, 1 mg of compounds was ground along with 100 mg of potassium bromide (KBr – pre-ground and desiccated at 500°C for 12 h) and the disc was prepared by the hydraulic pressure method. FTIR (Perkin Elmer) analysis was performed at a wave number range of 400–4000 cm^-1. The final purity of extracted compounds was analyses using an High Performance Liquid Chromatography–Diode Array Detection (HPLC-DAD) system. An Agilent 1200 Series HPLC system (Waldbronn, Germany) with a G1315D diode-array detector was used. Samples (20 μl) were injected into a Phenomenex Kinetex XB-C₁₈ column (75 mm×3 mm, 2.6 μm). The mobile phase consisted of 10 mm ammonium formate (NH₄COOH) in water:acetonitrile (98:2) (A) and 10 mm ammonium formate (NH₄COOH) in water:acetonitrile (2:98) (B). The flow rate was maintained at 1.0 ml/min.

2.6 Adsorption based elution studies

2.7 Statistical analysis

Results obtained were expressed as the mean±standard deviation of the replications. Results obtained in the experimental run generated by Box-Behnken design were expressed as mean of replicates. RSM based model fitting and statistical analysis was performed using Design Expert (release 9.0.3.1; State-Ease, Inc., Minneapolis, MN, USA). An analysis of variance (ANOVA) was performed to determine the significant levels defined at p<0.05, p<0.01 and p<0.001.

Extractions were performed in replicates at all points in the study design. The corresponding extracts were analyzed for the dependent variables (responses): amount of vasicine (Y₁). Mean values were analyzed using least-square regression and fitted to the generalized second-order polynomial model [(Eq. 1)] to all of the dependent Y response variables. Response surface plots were developed using reduced fitted polynomial models which allow the relationship between the experimental levels and the response of each factor to be examined and the optimum conditions to be recognized.

3.1 Selection of extraction solvent

Extraction was initially performed using different solvents based on polarity. Pure solvents in the increasing order of their polarity were selected for study, as solubility of metabolites varies with polarity. Since vasicine (Figure 1) is an alkaloid with an alkaline nature, dilute acidic solvents such as hydrochloric acid (5–10%) in water were used. As pure water extract had no appreciable yield of vasicine it was excluded from further studies. The maximum amount of extract and metabolites were obtained in methanol, therefore it was selected for conventional isolation of metabolites. Exhaustive extraction using methanol from leaves yielded methanolic extract which was analyzed for amount of vasicine through HPTLC. The amount was found to be 1.2% (w/w) in leaves of A. vasica, which corresponds to previous reports [20]. HCl (5%) in water used in extraction yielded a significant amount of vasicine (0.32%), but this was lower than methanolic extracts. The purity was also less (65–70%). Since water based extraction is “green” and safe, studies were advanced to optimize the yield and purity of vasicine through use of this process.

Figure 1:

Vasicine.

3.2 Isolation of molecules

To find the yield obtained through conventional isolation methods, column chromatography was performed and the vasicine crystallized was analyzed for its yield and purity. The fractions collected led to the isolation of pure vasicine as characterized by NMR spectroscopy. The spectral data (Figure 2) were compared and confirmed with previous studies [21]. As the opted extraction process produced a low yield of vasicine, further studies were done to optimize the extraction efficiency and recovery of these metabolites through microwaves and RSM.

Figure 2:

Structural elucidation of vasicine. (A) Fourier transforms infrared (FTIR) spectra of vasicine.

Figure 2:

Structural elucidation of vasicine. (B) Proton and carbon nuclear magnetic resonance (NMR) of vasicine.

3.3 Selection of variables and experimental ranges

Microwaves are electromagnetic waves with frequency ranging from 300 MHz to 300 GHz. The application of microwaves in extraction of compounds is strictly limited to conversion of electromagnetic energy into heat energy [22]. In the current study, 5% HCl in water was taken as a solvent to solubilize vasicine from plant matrix into solvent to be later purified. The problem with this is that vasicine is water insoluble and does not easily diffuse out of plant matrix (leaves). Therefore, HCl was added in water to increase its solubility (due to increased acidity) and microwave energy was applied to pull out vasicine into solvent. The process of extraction was tried initially with regular heating (Soxhlet) and it had a larger time duration (around 24 h) to completely extract the vasicine from the plant matrix. However, microwave assisted extraction depicted similar yields with 6 min of irradiation, the reason being that plant leaves of A. vasica are thick and fleshy and therefore require much time for conventionally (externally) applied heat to weaken the sturdy plant tissues to yield vasicine. The larger time required in this case is due to the time consumed for energy transfer through conduction through external material (plant tissue surface) in the presence of thermal gradients, whereas microwave irradiation delivers energy directly to materials (plant cells inside tissues) through molecular interactions with the electromagnetic field, which gets converted to thermal energy [23]. The energy consumption in the developed process is therefore far less compared to the conductive heating process to isolate vasicine from its plant matrix. The yield of vasicine with respect to microwave power and time of irradiation is shown in Figure 3. Observations depict that extraction yield increases until 6 min of irradiation and then becomes stationary. Microwave irradiation at 300 W and 450 W power yielded the same amount of vasicine. However, with increase in microwave power, the leaves were rapidly degraded due to higher localized heat, which decreased the extraction efficiency. Thus, microwave power and time of irradiation were fixed at 300 W for 6 min. Microwaves have the unique ability to penetrate into the plant matrix directly and cause localized heating. The temperature can be adjusted by adjusting microwave power. This highly localized heating causes the solutes inside the plant matrix to selectively migrate to the surrounding solvents at a higher mass transfer rate. In addition, the severe mechanical stress and localized pressure build up inside the plant cells disrupt them, causing more solute to escape. Thus, microwave power nullifies the mass transfer limitations generally caused during the extraction process [11]. These dynamic features make microwaves more applicable in phyto-molecule isolation of current commercial processes [24, 25].

Figure 3:

Effect of microwave energy on extraction yield of vasicine. Plot shows yield (%) of vasicine recovered from total vasicine available in plant matrix by applying different microwave energy (W) for different irradiation durations (2–10 min).

Initially, the influence of solid loading (%) in extraction process was studied. The maximum yield obtained was 88% yield at 10% solid (Figure 4A). The yield % (88%) was found in comparison with the total amount of vasicine present in plant material (1.2%). It was observed that the extraction yield increases as the solid amount increases, with the highest obtained at 10%, which decreased with further increase in solid. This was due to the fact that the more solid could promote a decrease in concentration gradient because of less available solvent for extraction. Higher solvent availability increases diffusion rate allowing maximum extraction [26–28]. A previous report suggested that extraction yield from dry peel powders of pomegranate was increased while increasing solvent-solid ratios [29]. The extractive ability becomes constant above a certain solvent-solid ratio, which suggests the reach of maximum concentration gradient. Next, the effect of % HCl in water on extraction was investigated. The maximum yield obtained was 88% at 6% of HCl in water (Figure 4B). The extraction of alkaloid compounds from a sample/material is directly related to the compatibility of the compounds with the solvent system according to the principle “like dissolves like” [30]. Change in HCl content in water changes acidity which affects the solubility of alkaline compounds (alkaloids). The focus of the present study was maximum recovery of vasicine, which made 6% HCl/water solvent system ideal for extraction. Finally, impact of extraction time in yield of metabolites from plant material was studied (Figure 4C). The amount of metabolites increased along with time, but became constant over a time limit. This might be due to the fact that the maximum extractive value is reached over the time duration and hence no further mass transfer of solute from plant material is possible. The maximum time required to reach maximum yield of metabolites is 4 h, which is relatively sooner than any conventional extraction process which involves a minimum of 24–48 h. The effect of agitation signified linearity in observations and became stationary from 200 rpm (Figure 4D). These observations signify the fact that the process is rapid in addition to minimizing the costs of operation of process.

Figure 4:

Effect of different influential parameters on yield of vasicine. Plot depicts that except for agitation (following linearity), all other parameters affect the extraction process.

3.4 Extraction optimization by RSM

3.4.2 Statistical analysis

Using the Box-Behnken experimental design, the second-order polynomial quadratic response equation [Eq. (1)] was used to establish a mutual link between the studied parameters. An empirical mutual relationship between the response dependent variables and independent parameters based on the coded factors was established according to the equation below.

(2)Y1=11.07-0.45X1+0.76 X2+0.11 X3+0.05 X1 X2+0.4 X1 X3+0.23 X2 X3-1.7 X12-1.07 X22-1.97 X32 (2)

The Box-Behnken matrix and experimental results for recovery of vasicine from acid-base extraction process are summarized in Table 1. Analysis of variance was used to assess the statistical significance of the quadratic model [31, 32]. The suitability and significance of the studied model could be better evaluated by utilizing ANOVA. The models were checked using a numerical method that used the correlation coefficient (R²) and the adjusted R² (R²adj). Each model should account for the observed variability in the obtained data, which is depicted by R². It is suggested that for a good statistical model, the R² value should be in the range of 0–1.0, much nearer to 1.0 for better fit. R²adj is described to modify the R² by taking into account the number of predictors in the given model. The results of ANOVA for amount of vasicine are depicted in Tables 2 and 3; statistical observations demonstrate that the regression model has a high coefficient of determination (R²=0.991), indicating that 99.1% of the variations in the extraction process of metabolites could be explained by the independent factors. Also, the R²adj (0.977) value explains the significance of the model [33]. There seems to be no significant difference between R² and R²adj values which is desirable for the model. “Adeq precision” measures the difference between signals to noise ratio, which for this model is 23.24. Since, it is >4.0, it is considered as an adequate desirable value [34]. In addition, a low coefficient of variation was observed (2.72%) which is usually desired, indicating good reliability for the experiments carried out in the process. ANOVA in Table 4 demonstrates the results of the lack of fit test for the models, which describes the variation in the data around the fitted model. In the present case, the F-value for lack of fit test is 6.06 which is not significant and implies that the models sufficiently describe the obtained data. Also, in the present study F-values are greater and p values are much less, with few exceptions depicted in Table 3, which implies that the coefficients obtained are significant in the model.

Table 2:

Analysis of variance for response surface quadratic model.

Source	DF	SS	MS	F-value
Model	9	32.87	3.65	67.84
A	1	1.62	1.62	30.09
B	1	4.65	4.65	86.4
C	1	0.1	0.1	1.88a
A2	1	10.62	10.62	197.25
B2	1	4.23	4.23	78.65
C2	1	14.34	14.34	266.41
AB	1	0.01	0.01	0.19a
AC	1	0.64	0.64	11.89b
BC	1	0.2	0.2	3.76a

All values are significant at 1%, ^bsignificant at 5%, ^anot significant.

A – solid loading (%), B – HCl/water (%), C – extraction time.

DF, Degrees of freedom; MS, mean square; SS, sum of squares.

Table 3:

Regression coefficients of the predicted second-order model for the response variables, amount of vasicine.

Model parameters	Regression coefficient	SE
Intercept	11.07	0.13
A	-0.45	0.082
B	0.76	0.082a
C	0.11	0.082
A2	-1.7	0.12
B2	-1.07	0.12
C2	-1.97	0.12
AB	0.05	0.12a
AC	0.4	0.12b
BC	0.23	0.12a
S.E	0.11
R2	0.991
Adj-R2	0.977
CV%	2.72
Adeq Precision	23.24

All values are significant at 1%, ^bsignificant at 5%, ^anot significant.

CV, Coefficient of variance; R², coefficient of multiple determinations; SE, standard error.

Table 4:

Analysis of variance for the lack of fit testing for vasicine.

Source	DF	SS	MS	F-value
Lack of fit	3	0.24	0.081	6.06a
Pure error	2	0.027	0.013
Total error	5	0.27	0.54

^aNot significant.

DF, Degrees of freedom; MS, mean square; SS, sum of squares.

The coefficients and standard error are depicted in Table 3. The corresponding F-values for coefficients indicate that the %HCl/water (X₂) produces the largest effect in extracting vasicine in the process (F-value: 86.4, p<0.0001). This was followed by solid loading (%) (F-value: 30.09, p<0.0001). This is required for an experimental analysis to check the adequacy of the applied model which can ensure an excellent estimation of real conditions [35]. This is depicted by Figure 5, which shows comparison between experimental and predicted data. It demonstrates good agreement between the axes (R²=0.991). Also, less difference between R² and R²adj for both the responses indicates experimental values are in good agreement with the predicted values.

Figure 5:

Plot of predicted response vs. the calculated response for vasicine.

3.4.3 The combined effect of operational factors

A three-dimensional response surface and contour graph were plotted based on the obtained model equation to evaluate the interaction among the operational factors and to determine the optimum values of each parameter [36]. The effects of solid loading (%), %HCl/water, and extraction time on recovery of vasicine are shown in Figure 6. These plots show how low and high values of operational factors influence extraction response variables. In all of the plots of Figure 6, the yield of metabolites increases with increase in factor levels up to moderate level (0) and then decreases. For example, in Figure 6A, interaction between solid loading (%) (A) and %HCl/water (B) is plotted, where yield of metabolites increases as A increases from 0–10 and then decreases when it extends to 10. Similarly, as the solvent-solid ratio (B) reaches 6%, the yield reaches maximum and then decreases. This phenomenon is seen for all of the surfaces drawn based on interaction effects of different factors.

Figure 6:

Response surface plots for yield of vasicine showing effect of operational parameters.

3.5 Adsorption based elution

Vasicine is usually found along with other alkaloids in A. vasica, and its purification thus becomes difficult. Moreover, the alkaline nature of the molecule prolongs elution time through column chromatography. The current process was developed to rapidly isolate pure vasicine from the pool of other molecules. Silica gel is a weakly acidic, considerably inert adsorbent that has long been used to separate plant metabolites through column chromatography. Since vasicine is alkaline it binds reversibly to silica by weak ionic forces. Out of three grades of silica used in the current study, silica at 100–200 mesh worked well in isolating vasicine. It was observed that silica at 60–120 mesh did not adsorb vasicine completely (due to less surface area) and 230–400 mesh did not desorb it completely (due to very large surface area leading to strong adsorption). Thus, further studies were advanced with silica (100–200 mesh). Next, the amount of adsorbent was determined, which depicted that silica gel at 20 g/l was best in extracting pure vasicine. Above 20 g/l, the yield of vasicine remained the same (Figure 7B). Next, adsorption and desorption times were optimized which depicted that both at 30 min yielded maximum yield of vasicine. Above those values, the adsorption and desorption profiles remained the same (Figure 7C). Desorption was done with 70% ethanol/water, a considerably safe solvent for desorption of vasicine. Next, the effect of agitation on the time of desorption and yield was studied, where increase in agitation decreased the time of desorption, but affected the purity of vasicine isolated. It was observed that other impurities or undesired molecules were also rapidly eluted from adsorbent matrix along with vasicine. Therefore, agitation at 200 rpm for 30 min was fixed to achieve high yields of vasicine without compromising purity (Figure 7D). From the above studies it became clear that none of the factors nonlinearly influenced the adsorption process, as all of the factors became linear at one point of experimental observations. Thus, these factors were not considered for response surface based modeling and optimization.

Figure 7:

Adsorption studies. (A) Equilibrium adsorption isotherms for adsorption of vasicine and vasicine in extract (VNE) onto silica gel, C_e – final concentration of molecule in solution after adsorption (mg/l), Q_e – amount of molecule adsorbed by adsorbent (mg/g); (B) effect of adsorbent amount on yield of vasicine; (C) effect of time on adsorption and desorption; and (D) effect of agitation on desorption time and yield of vasicine.

Even though it became clear that adsorption based elution did isolate vasicine with significant purity, its mechanism of adsorption was required to be unraveled. Thus, adsorption isotherms were studied with both pure vasicine and extract containing a known amount of vasicine. The current isotherm studies depict the kinetics of adsorption of vasicine in extract (multisolute interactions). Figure 7A gives conventional plots of the liquid phase pigment concentration vs. solid phase pigment concentration. The adsorption data obtained were correlated to two commonly used equilibrium isotherm models: Langmuir [Eq. (3)] and Freundlich [Eq. (4)] [37]:

where C_e and q_e in Eqs. (3) and (4) are the solution and surface concentration for the molecule, respectively, K_L and Q_max are Langmuir constants, and K_f and n are Freundlich constants.

It is obvious that adsorption rates and kinetics vary for a solute molecule in a single solute and multisolute systems. Since solvent extracts from plant matrices contain a combination of molecules, it is vital to advocate that vasicine follows the adsorption kinetics governing the multisolute systems. For this, isotherms were analyzed for both vasicine individually and extract containing vasicine. It was determined that the amount of vasicine in extract was 12% (HPTLC). Subsequently, the amount of vasicine adsorbed to silica was reduced by 25% in extract system than in single system (vasicine alone). Isotherm studies show that adsorption of vasicine as a single solute is better than in multisolute system. This is due to predominance of other molecules that are available in the extract (~88%) that compete during the adsorption process. This smaller ratio of vasicine with respect to other undesired molecules and impurities in total concentration, assists in successful desorption of pure vasicine firstly from the adsorbent pores with no elution of other pigments at 70% ethanol in water (v/v). Isotherm studies indicate that the adsorption model satisfies the Langmuir theory where the number of adsorption sites on silica is restricted and that the vasicine, along with other pigments, forms a monomolecular layer on the adsorbent (silica) at the saturated condition of adsorption. The current adsorption kinetics do not follow the Freundlich equation [Eq. (4)]. This is substantiated by the correlation coefficient (r²) values which are higher for Langmuir (0.99) than Freundlich (<0.9) isotherms [38]. The isothermal parameters are given in Table 6.

Extraction of natural compounds, especially alkaloids, is tough due to their low abundance in plant tissues which makes application of newly developed green technologies difficult in isolating them with significant yield and purity. Considering the difficulty in development of such processes, the current process, microwave assisted adsorption based elution (MAABE), considerably follows the general principles of green chemistry stated by Anastas and Warner [39], such as reusability (silica), complete usage of raw material (leaves), environmentally friendly (solvents), minimum toxicity (solvents), minimum use of solvents, efficient energy consumption, use of ambient conditions (temperature and pressure), renewable raw material (A. vasica is an easy growing interim crop and an invasive plant in some parts of India) and minimum byproducts formed making the developed process “green” compared to existing processes in the realm of natural product separation engineering.

The extract obtained during different stages of the process is depicted in Figure 8. The final yield of vasicine was 1.12% evidently substantiating a significant increase compared to initial yields of 0.42%, as given in Table 7. The optimized yields obtained depict an increase of 93.34% in total yield of vasicine. Since leaves of A. vasica contained 1.2% of vasicine, total recovery of vasicine obtained from the current process was determined. The final recovery obtained was 93.34%, which is very significant for any developed separation process. In comparison, the recovery obtained through conventional chromatography was a mere 35%. The final purity of vasicine obtained from the optimized run of the developed process was analyzed by HPLC-DAD and was found to be 95.17% (Figure 9). A similar adsorption based elution process was developed by us for isolation of acetyl shikonin from A. nobilis, using activated carbon as adsorbent and aqueous isopropanol as desorbing solution [8]. However, the current study deals with an alkaloid (vasicine) to be extracted from a much complex plant matrix (thick leaves). The former process will work in extracting the vasicine (alkaline) with low yields due to the neutral pH of activated carbon, whereas MAABE utilizes dilute acid to initially extract vasicine and exploits the weak acidic property of silica to adsorb alkaline vasicine, which then is selectively desorbed using aqueous ethanol. The commercially viable isolation process of vasicine used currently in industries includes many stages such as alcohol extraction for 72 h, then acid-base extraction for 2–24 h (including an acid, organic solvent, and a base), then extraction with an organic solvent (chloroform), and finally precipitation using organic solvent mixtures (an ether and acetone) [40]. The solvents used in the process are environmentally harmful, the time consumed is enormous (80–96 h) and the purity obtained for vasicine is 80%, whereas the process developed in the current study uses green solvents (6% HCl and 70% ethanol) and extracts vasicine with a purity of 95% within 5 h with a fewer number of stages. Thus, MAABE works fine for extracting commercially important alkaloids which otherwise are very difficult to isolate using conventional and currently available processes. The developed process with planned design and scale up could be easily commercialized as an economically viable, high yielding, and rapid batch isolation process for efficient recovery of commercially important vasicine from leaves of A. vasica.

Figure 8:

Extraction scenario at different stages of microwave assisted adsorption based elution (MAABE). (From left to right) 6% HCl/water extract of leaves filtered after microwave and 4 h agitated batch extraction, neutralized extract containing precipitated compounds (including vasicine), silica gel adsorbed extract, semi-dry pure precipitated vasicine after desorption (becomes yellowish white after complete drying).

Figure 9:

High performance liquid chromatography (HPLC) analysis of vasicine (purity=95.17%) extracted through microwave assisted adsorption based elution (MAABE).