|Year : 2022 | Volume
| Issue : 2 | Page : 133-144
Enrichment of hydroalcoholic extract of Trigonella foenum-graecum L. seed with major components using TLC fingerprint, image analysis, and design of experiment studies
Jyoti Dahiya1, Anupam K Mangal1, Rajesh Bolleddu1, Sreya Dutta1, Surbhi Kharwar1, Kalyan Hazra2, Peyyala Venkata Vara Prasad3
1 Department of Pharmacognosy, Central Ayurveda Research Institute, Kolkata, West Bengal, India
2 Department of Chemistry, Central Ayurveda Research Institute, Kolkata, West Bengal, India
3 Department of Ayurveda, Central Ayurveda Research Institute, Kolkata, West Bengal, India
|Date of Submission||04-Feb-2022|
|Date of Acceptance||08-Jun-2022|
|Date of Web Publication||14-Sep-2022|
Department of Pharmacognosy, Central Ayurveda Research Institute, Kolkata, West Bengal
Source of Support: None, Conflict of Interest: None
BACKGROUND: The seed of famous Indian spice Trigonella foenum-graecum L. (Methi) of family Fabaceae is the source of a large number of chemical components and used in a number of herbal formulations as an extract. For the separation of these chemical components from the complex plant matrix, the development of an effective extraction method is necessary. The present study aimed to identify major chemical constituents in the fingerprint developed by thin-layer chromatography (TLC) and to design an optimized extraction process for the enrichment of Methi seed extract with the selected constituents. METHODS: TLC fingerprint along with the image analysis was used to find the major components depending upon the area under the curve. The independent variables and their range were identified by one-factor-at-a-time experiments. The selected range of variables was further optimized by response surface methodology using the design of experiment (DoE) study to develop an extraction method for the maximum recovery of the selected components. The analysis of variance was utilized to check the fit of the developed model on the basis of quality assessment parameters. RESULTS: The information generated in this study recommends the use of reflux method with 70.57 mL/g of 80% ethanol for 31.19 min at 69.76°C for the maximum extraction of major components from seeds. CONCLUSION: The developed extraction method can be used for lab scale extractions in plant drug standardization and scale-up extractions in herbal industries for the manufacturing of Methi seed formulations.
Keywords: Analysis of variance, one-factor-at-a-time, response surface methodology, thin-layer chromatography, Trigonella foenum-graecum L.
|How to cite this article:|
Dahiya J, Mangal AK, Bolleddu R, Dutta S, Kharwar S, Hazra K, Prasad PV. Enrichment of hydroalcoholic extract of Trigonella foenum-graecum L. seed with major components using TLC fingerprint, image analysis, and design of experiment studies. J Drug Res Ayurvedic Sci 2022;7:133-44
|How to cite this URL:|
Dahiya J, Mangal AK, Bolleddu R, Dutta S, Kharwar S, Hazra K, Prasad PV. Enrichment of hydroalcoholic extract of Trigonella foenum-graecum L. seed with major components using TLC fingerprint, image analysis, and design of experiment studies. J Drug Res Ayurvedic Sci [serial online] 2022 [cited 2022 Sep 25];7:133-44. Available from: http://www.jdrasccras.com/text.asp?2022/7/2/133/356053
| Introduction|| |
Natural resources such as plants are the source of many therapeutic chemical components and utilized as a medicine in the primary health-care system.Trigonella foenum-graecum L. is a valuable plant of the indigenous system of medicine and a famous Indian aromatic spice. The plant is also known as fenugreek in English and Methi in Hindi. Fenugreek seeds have a long history of use in Ayurveda and Unani system of medicine and possess a high nutritional value., Traditionally, the seeds are useful as a home remedy for many stomach-related disorders, fever, anemia, gout, epilepsy, chronic cough, etc. Recent discoveries have shown that fenugreek seeds have numerous pharmacological activities such as hepato-protective, anti-inflammatory, antiulcer, anticarcinogenic, antibacterial, etc. Chemically, Methi seeds contain alkaloids, saponins, steroidal saponins, flavonoids, amino acids, etc. The overall pharmacological activities of the Methi seeds are related to the collaborative effect of major chemical components such as trigonelline (pyridine alkaloid), diosgenin (steroidal sapogenin), and flavonoids—luteolin, apigenin, quercetin, vitexin, etc.,,, Hence, to enrich the extract of Methi seeds with major components, a suitable extraction process is required.
To separate these components from the complex cellular matrix, it is imperative to identify the best and effective extraction method. Nowadays, a variety of modern techniques are present, but the conventional extraction techniques are still the first choice on account of its easiness and cost-effective results., The yield in the extraction process is mainly affected by parameters such as extraction method, solvent used (polar or nonpolar), extraction time, extraction temperature, solvent volume, solvent percentage, pH, particle size of the solute, etc., A minor change in the values of these factors can create a difference in the extractive value. So, it is essential to develop and optimize the extraction process for the separation of major principle components from medicinal plants. Moreover, the selected factors may influence the extraction process either independently or interactively, so it is vital to study the effect by both the ways, i.e., one-factor-at-a-time (OFAT) and response surface methodology (RSM).,
OFAT is a conventional way in which at a time, the effect of only one factor is tested that fails to elaborate the collaborating effect of different factors,, whereas RSM, a multivariate analysis, is an ideal method for the development and optimization of the extraction process. In RSM, statistical tools are applied for the modeling of multidimensional analysis, and the analysis of variance (ANOVA) is used to evaluate the comparative contribution of different factors.,, Chromatographic studies are suitable for the resolution and identification of chemical components in qualitative and quantitative analysis for plant drug standardization., Quantitative analysis that includes the estimation of one or more component does not provide a complete picture of plant’s chemical make-up. Moreover, biological effects of medicinal plants are the result of the synergistic effect of multiple constituents; thus, it seems necessary to develop a complete chemical profile for which chemical fingerprinting has proven to be a powerful technique and to reflect the chemical diversity in plants. The fingerprint analysis can be carried out using any of the analytical techniques, but thin-layer chromatography (TLC)/high performance thin-layer chromatography (HPTLC) is more often used in the quality studies of plant drugs.
In this context, the present study was planned to carry out extraction studies on Methi seeds to enrich its extract with the major constituents. After preliminary and OFAT studies, the TLC fingerprint profile was developed, and major components with high area under the curve (AUC) were selected. Then, the RSM studies were used to increase the AUC of these selected components. To the best of our knowledge, till now, no reports are available for the optimization of the extraction process by DoE studies for the enrichment of crude extract with major components using TLC chemical fingerprint and image analysis technique.
| Materials and Methods|| |
The seeds of Trigonella foenum-graecum L. were procured from the local market of Kolkata [Figure 1]. The identity was confirmed by botanical standardization as per the API standards, and voucher specimen was submitted in the museum of Department of Pharmacognosy, Central Ayurveda Research Institute, Kolkata (voucher specimen no.: CARI-Kol/SD/14).
All the chemicals, solvents, and reagents used were of analytical grade. The qualitative TLC for the fingerprint analysis was performed on TLC plates with 0.2 mm coating of silica gel G 60F254 on aluminum base (E. Merck [India] Ltd.).
Instrumentation and chromatographic conditions
Qualitative analysis was carried out using CAMAG HPTLC system and WINCATS software version 1.4.7 in the Chemistry Department, Central Ayurveda Research Institute, Kolkata. The samples (10 µL) were applied using CAMAG automatic TLC sampler ATS 4 as an 8-mm band and were developed in CAMAG twin trough 10 × 10 saturated chambers with an optimized solvent system. The sample at every level was prepared by dissolving residue in 10 mL of respective solvents. The developed plates were air dried and visualized under ultraviolet (UV) light (Camag TLC visualizer 1). Further, the plates were derivatized with anisaldehyde-sulphuric acid reagent followed by heating at 110°C. The plates were again observed below white light. Images were captured at wavelengths of 254 nm, 366 nm, and under white light after derivatization. All the images (fingerprints) were documented and stored as JPEG file for further image processing.
Image J analysis[
Images saved in JPEG files were analyzed by Image J software (Java-based program). The software is used for minor modifications such as cropping, light adjustments, the elimination of irregularities, etc., and for the determination of AUC. The process is described in brief here: after minor adjustments, the images were rotated to 90° to the right, and a rectangular area representing a lane was selected. Then, plot profile option was clicked to generate a 2D graph showing the intensity of pixels along a line. For each plot, the x-axis represents the distance (Rf), whereas the y-axis represents pixel intensity (AUC). The area was then obtained by valley-to-valley integration, and a data matrix representing the AUC along the Rf was saved in the csv (comma-separated values) file. Other adjustments such as smoothing, baseline removal, peak identification, etc., are done as per the requirement.
| Preliminary Screening|| |
Selection of extraction solvent
The effect of extraction solvent was studied with six different solvents of increasing polarity (chloroform, acetone, ethyl acetate, ethanol, methanol, and water). Plant material (2 g) was refluxed separately in 100 mL of different solvents for 2 h at their respective boiling point. The solvent with maximum residue weight was used in the next study, the selection of extraction technique.
Selection of extraction technique
A plant material (2 g) was extracted in 100 mL of the selected solvent using reflux (2 h) and soxhlet (8 h), at the boiling point of the selected solvent and using cold maceration (24 h) at the room temperature. The technique with maximum residue weight was used in further experiments.
Fingerprint development and the selection of major components
The extract with the highest residue weight was utilized for the fingerprint development by hit-and-trial method. The residue was dissolved in 10 mL solvent, and 10 µL was applied as band, varieties of solvents in varying ratio were tried [Table 1]. A solvent system (chloroform:ethyl acetate:methanol:glacial acetic acid in the ratio 7:2:0.5:1), resulting in a well-resolved TLC fingerprint profile, was then selected for further studies. From the developed fingerprint, bands with the highest AUC are selected as major components.
The influence of different parameters such as the extraction temperature, solvent percentage, solvent volume, extraction time, and cycle of extraction was studied by OFAT method. Here, for each experiment, 2 g of coarsely powdered crude drug was used, and extracts received were analyzed for residue weight.
The effect of extraction temperature was examined at 40°C, 50°C, 60°C, 70°C, and 80°C. The effect of solvent percentage was studied with five different solvent % (20%, 40%, 60%, 80% aqueous ethanol and pure ethanol). The influence of solvent volume was inspected with five different volumes of the selected solvent percentage (20, 40, 60, 80, and 100 mL/g of sample) in preselected conditions.
Further, the impact of time on extraction was studied for the different time period ranging from 10 to 320 min at five levels (10, 20, 40, 80, 160, and 320 min), and the extraction time was again confirmed by extraction cycle studies (x, 2x, 3x, etc., where x is the selected extraction time in the previous study).
After the proper inspection of different parameters in OFAT studies, design expert software version 7 (Stat-Ease Inc., Minneapolis) was utilized for the development and optimization of extraction procedure using CCR-design of RSM. The studies were done at five levels (−α, −1, 0, +1, + α), and the interactive effect of three selected factors on three responses (the AUC of three major selected components) was studied. The design of experiments has a total of 20 runs, and the optimization was done to maximize the content of major components. [Table 2] depicts the levels of independent variables. Actual and coded values of variables in different combinations are listed in [Table 3].
|Table 3: Experimentally obtained results of CCRD design for AUCs of T-1, T-2, and T-3|
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The second-order polynomial equation was used to check the fitness of response variable and to obtain the regression coefficients:
where Y is the response; A, B, and C are the variables; β0 is the regression coefficient for the intercept; β1, β2, and β3 represent the regression coefficients for linear terms; β11, β22, and β33 are regression coefficients for quadratic terms; and β12, β13, and β23 are regression coefficients for cross-product terms. The ANOVA was used to test the results statistically for the determination of quality assessment parameters of the model. The effect of variables on each response was represented in [Figures 7–9]. [Figure 10] shows the correlation between predicted and actual values.
| Results|| |
Preliminary screening (the selection of extraction solvent and technique)
The best solvent and extraction technique was selected on the basis of extract’s residue weight. Out of the six solvents and three techniques, the highest residue weight was obtained with ethanol as solvent and soxhlet as extraction technique. The residue weight obtained by the reflux technique was slightly lesser than soxhlet; results were presented in [Figure 2].
|Figure 2: Effect of different (A) solvents and (B) extraction techniques on residue weight under preliminary studies|
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Development of fingerprint and validation studies
The selected ethanol extract was exploited in the development of the TLC fingerprint profile, and a well-resolved fingerprint was obtained with mobile phase chloroform:ethyl acetate:methanol:glacial acetic acid in the ratio 7:2:0.5:1. The fingerprint profile under UV254, UV366, and visible light after derivatization with anisaldehyde-sulphuric acid reagent was shown in [Figure 3]. The stability studies of fingerprint were also done as shown in [Figure 4].
|Figure 3: TLC fingerprint profile under (A) 254 nm, (B) 366 nm, and (C) visible light after derivatization|
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|Figure 4: Fingerprint showing three major components and the stability of fingerprint: (A) sample on the plate for 3 h prior to chromatography, (B) and (C) fresh sample applied immediately, and (D) sample prepared 3 h prior to chromatography and applied at the time of chromatography|
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From the developed fingerprint, three major components T-1 (blue band at Rf0.32), T-2 (blue band at Rf0.38), and T-3 (blue band at Rf0.55) were selected as shown in [Figure 4].
One-factor-at-a-time (the selection of an independent variable and experimental limits)
The results of OFAT studies are presented in [Figure 5]. The effect of temperature was studied at five levels, and the highest residue weight was obtained at 70°C with a little difference in residue from 60°C to 80°C. For the solvent percentage, 80% ethanol showed a maximum residue weight followed by 100% ethanol and 60% ethanol, but with a large difference in residue weight. The effect of solvent volume of 80% ethanol was also studied at five levels; maximum residue weight was obtained in between 60 and 80 mL/g of sample. The extraction period between 20 and 40 min results in high and comparable residue weight, the extraction time was further confirmed by extraction cycle studies, and it was found that only one cycle of extraction was sufficient as gain in the residue weight was less than 10% in the next cycle.
|Figure 5: Effect of different parameters on residue weight under OFAT studies: (A) temperature, (B) ethanol percentage, (C) solvent volume, (D) time, and (E) extraction cycle|
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Design of experiment
From the results of OFAT studies, three independent variables are selected for further studies by RSM, and their experimental limits are: A = temperature (60°C–80°C), B = solvent volume/g of drug (60–80 mL/g), and C = time (20–40 min); the extraction technique (reflux), ethanol percentage (80%), and extraction cycle (01 cycle) were kept constant.
The effect of three selected independent variables on three investigated responses, i.e., the AUC of three major components (T-1, T-2, and T-3), was carried out by applying the second-order polynomial equation in the central composite rotatable design (CCRD) of RSM. The design of the extraction method and the result of different extracts are presented in [Table 2] and [Figure 6]. The ANOVA was used for the statistical examination of the developed extraction procedure. The values of regression coefficients and other quality assessment parameters are given in [Table 3].
|Figure 6: Fingerprint and image analysis of extracts obtained by design matrix combinations|
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Effect of parameters on T-1 AUC
The AUC for T-1 ranged from 233.56 to 7706.5; the results are depicted in [Figure 7] and [Table 3]. The highest AUC was achieved at 70°C, 70 mL/g solvent, and 30 min, whereas the lowest was noticed at 75.95°C, 75.95 mL/g solvent, and 35.95 min. The model was highly significant with P value < 0.0001, and quadratic terms A2, B2, and C2 are found to have an important role in the model. The lack of fit was nonsignificant with f value = 0.88 and fit perfectly to the model with P value = 0.5563. The coefficient of determination R2, predicted R2, and adjusted R2 are 0.9761, 0.9546, and 0.8882, respectively. The fitted second-order polynomial equation derived from software using RSM was given below:
|Figure 7: Response surface (3D) plots showing the effect of variable on T-1 AUC: (A) effect of temperature and solvent volume, (B) effect of temperature and time, and (C) effect of solvent volume and time on T-1 AUC|
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The above equation showed that only the linear term B and interactive term AC exhibits the positive influence, whereas all other terms show the negative influence. Various response surface graphs showing the influence of parameters were generated for T-1 AUC and shown in [Figure 7].
Effect of parameters on T-2 AUC
The experimental AUC of T-2 component is given in [Table 3]; the AUC varied from 269.23 to 2742.95. The highest was recorded under 70°C, 70 mL/g solvent, and 30 min, whereas the lowest was noticed at 80.00°C, 70 mL/g solvent, and 30 min. The ANOVA shows that all terms are significant with P < 0.0001 except BC. The model was significant at a low level of P value (<0.0001) and a high level of f value (143.41); whereas the lack of fit was not significant on the account of high P value (1.000) and low f value (3.265e-005). The values of R2, predicted R2, adjusted R2, and adequate precision for AUC of T-2 were 0.9923, 0.9854, 0.9889, and 29.493, respectively. The model also possesses low coefficient of variation (CV%), 7.80. The second-order polynomial equation representing the model was given below:
The equation shows the positive influence of B, C, and BC, whereas all other terms show the negative influence. The 3D response surface graphs are shown in [Figure 8], displaying the effect of variables on T-2 AUC.
|Figure 8: Response surface (3D) plots showing the effect of variable on T-2 AUC: (A) effect of temperature and solvent volume, (B) effect of temperature and time, and (C) effect of solvent volume and time on T-2 AUC|
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Effect of parameters on T-3 AUC
The AUC of the extracted T-3 under different combinations of variables are in the range from 1867.6 to 9410.82, respectively [Table 3]. The conditions resulting in the maximum AUC are 70°C, 70 mL/g solvent, and 30 min, whereas 60°C, 70 mL/g solvent, and 30 min results in the minimum T-3 AUC. In accordance with the ANOVA, linear terms A, B, and C and interactive terms A2 and B2 are significant. Although a low P value (<0.0001) and high f value (17.41) make the model significant, a high P value (0.997) and low f value (0.021) make the lack of fit not significant. The values of R2, predicted R2, adjusted R2, adequate precision, and CV% for T-3 AUC are given in [Table 4]. The second-order polynomial equation representing the model was given below:
|Table 4: Regression coefficients and quality parameters for investigated responses|
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The equation shows that linear terms of A, B, and C; interactive term AC exhibited the positive effect, whereas all other terms show the negative effect. The impact of extraction parameters on T-3 AUC is depicted in [Figure 9] as 3D surface plots.
|Figure 9: Response surface (3D) plots showing the effect of variable on T-3 AUC: (A) effect of temperature and solvent volume, (B) effect of temperature and time, and (C) effect of solvent volume and time on T-3 AUC|
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For the verification of model, numerical optimization method was used. The input parameters were entered for a specific range, whereas the responses are designed to achieve a maximum value. The solution with temperature (°C) = 69.76, solvent volume (mL/g) = 70.57, and time (min) = 31.19 with a maximum desirability of 0.899 was used for the extraction. The experimental results are almost same as that of software-predicted values of response depicted in [Table 5].
|Table 5: Predicted and experimental values of response for software predicted solution for optimum extraction conditions|
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| Discussion|| |
The present study was conducted to develop an optimized method for the extraction of major components from the seeds of Trigonella foenum-graecum L., commonly known as Methi seeds. Extraction is a key step in the separation of important constituents from the plant matrix. As this process involves many parameters that can alter the recovery of constituents, a proper systematic study is required. The present study involves preliminary experiments, fingerprint development, image analysis, OFAT, and RSM studies.
The preliminary studies disclosed that ethanol results in the maximum residue weight that might be due to the fact that ethanol has the capacity to extract both the polar and nonpolar components on the account of presence of −OH and −C2H5 group. The soxhlet technique showed a higher residue weight than the heat reflux method with a minor difference of 5.11 mg. But reflux technique was carried out only for 2 h, whereas soxhlet extraction takes around 8 h, and moreover the difference in the contents is very less. So it is more convenient to use reflux technique for further experiments.,
TLC is an effective and powerful tool for the development of chemical profile, and in the present research, TLC fingerprint was utilized to find major constituents in association with image analyzing software such as ImageJ., The ImageJ software helped in the determination of the AUC values for the resolved bands in TLC fingerprint analysis.
The developed TL-Chromatogram under UV lights 254 and 366 nm does not show any major constituents. On derivatization with anisaldehyde-sulphuric acid reagent, three major blue color bands at Rf 0.32, 0.38, and 0.55 with AUC 4533.78, 1348.76, and 5332.88 are observed and designated as T-1, T-2, and T-3, respectively. In the present research, for OFAT studies, it was presumed that a higher residue weight results in a higher content of selected components (AUC); thus, OFAT studies were conducted based on the residue weights, and later, the same results were found in RSM studies.
The selection of extraction temperature is the key step in the extraction process; with an increase in temperature, the residue weight increases that might be due to the fact that increased temperature decreases the surface tension and solvent viscosity that improves sample wetting and diffusion of solvent in the plant matrix., The residue weight reached the maximum value at 70ºC, and beyond this, it starts decreasing, possibly because of the degradation of bioactive at a higher temperature. The residue weights from 60ºC to 80ºC were comparable with lesser difference in values, so for further experiments in RSM, temperature range from 60ºC to 80ºC was selected.
The effect of aqueous ethanol concentration at 20%, 40%, 60%, and 80% was compared with the results of pure ethanol. 80% ethanol resulted in the highest residue weight and thus used in further experiments. For the proper extraction and immersion of sample, the use of optimal solvent volume is essential as most of the plant matrix swell during the extraction. 60 mL of solvent per gram of crude drug results in the maximum residue weight followed by 80 mL/g with marginal difference. So, for the further extraction processes, 60–80 mL/g solvent was selected. The duration of extraction is another important influential factor for the extraction of bioactive. In general, the extraction of components increases with an increase in the extraction time. An optimum time is required for the diffusion of solvent into the cells and the desorption of components from the cell to establish equilibration between solvent and matrix (Fick’s law of diffusion).,, The residue weight increases up to 20 min and starts decreasing after 20 min, which might be due to the degradation of components on the extended extraction; difference in residue weight between 20 min and 40 min is very less. So, 20–40 min was chosen for the optimization by RSM. Further confirmation of the extraction time was made through extraction cycle; it is important to finalize the number of extraction cycle for the complete extraction of bioactive. In the present case, no significant increase in the residue weight observed with the repeated extraction cycle. Hence, for the complete extraction of components from plant material, only one cycle of extraction was sufficient.
The final extraction optimization studies were done by the RSM. The experimentally obtained results showed that for the all three selected components, maximum AUC was obtained with 70°C, 70 mL/g, and 30 min.
The ANOVA was used for the statistical determination of significant terms in the present model. The values of regression coefficients of linear, quadratic, and interactive terms for different response and quality assessment parameters are given in [Table 4]. The coefficient of determination (R2) explains the effectiveness of the model; R2 for all the responses are close to 1, which was desirable to make a model significant with a good fit. The predicted R2 and adjusted R2 were in reasonable agreement with the difference of less than 0.2 for all responses that show a good degree of correlation between actual and predicted values, whereas the adequate precision that measures the signal-to-noise ratio was greater than 4, which represents a good fit of the model for proper extraction.
The significance of each coefficient was determined by f value and P value. For a significant model, the f value must be high and P value must be small (P < 0.05), whereas the lack of fit that measures the adequacy of model was not significant if f value is small with high P value. For all the three responses, the model is significant and the lack of fit is not significant, as desirable for a good model. Low CV for all response indicates a high degree of exactness and consistency of experimental values.
The second-order equation representing the different linear, quadratic, and cross-product terms possess the regression coefficients with positive and negative values. The positive terms showed the positive effect of variable on response, whereas the negative term means either the negative effect or zero effect of variable on response.
For T-1, only linear term B and interactive term AC exhibited the positive influence, which means an increase in solvent volume tends to increase the extraction of T-1, whereas the interactive term of AC showed an increase in temperature results in a higher content only when the extraction was done for a longer period or vice versa. For T-2 AUC, linear terms B and C and their interactive term BC showed the positive effect, which means a high solvent volume and a long duration of extraction was needed for the extraction of T-2. For T-3, linear terms A, B, and C; interactive term AC exhibited a positive effect that indicates that for the proper extraction of T-3, high temperature, solvent volume, and time required.
RSM analysis showing the effect of different parameters on AUC of three major components is presented in [Figures 7–9]. The 3D response curves provide the visualized effect of variables on AUCs. For T-1, T-2, and T-3, [Figures 7A], [8A], and [9A], respectively, showed the effect of (A) temperature and (B) solvent volume on the AUCs; the highest AUCs for T-1, T-2, and T-3 were obtained at 70°C and 70 mL/g of solvent. The value of AUCs of the three components tends to increase up to 70°C and 70 mL/g of solvent and starts decreasing afterward.
[Figures 7B], [8B], and [9B] showcase the effect of (A) temperature and (C) time on the AUCs of major components; the highest AUCs were gained at 70°C and 30 min. The value of AUCs inclined up to 70°C and 30 min and thereafter starts decreasing. Similarly, [Figures 7C], [8C], and [9C] display the influence of (B) solvent volume and (C) time; on the AUCs of the selected components, the uppermost values of AUCs were received with 70 mL/g of solvent and 30 min of extraction. After attaining the highest values, the AUCs start declining. The possible reasons for these results are already discussed under OFAT studies.
The graphs between predicted and actual values are presented in [Figure 10]A–C for T-1, T-2, and T-3, respectively. In all the three graphs, the data values are evenly split by the 45° line that represents a good fit of the developed method. It also gives a clear vision of good correlation between predicted and actual values.
|Figure 10: Graphs showing the correlation between predicted and actual values: (A) T-1 AUC, (B) T-2 AUC, and (C) T-3 AUC|
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Further, the model is verified and a solution with high desirability for all the three response was obtained. The results of software-predicted AUC and experimentally obtained AUC are in 98% agreement. Hence, the software-predicted solutions can be utilized for all future extractions. The present work, for the first time, uses a chemical profile by TLC to find the major components, and an image analysis technique was used for the determination of AUC. Further, for the very first time, the enrichment of hydroalcoholic extract was made with three major selected components simultaneously.
| Conclusion|| |
For the preparation of extracts with a high content of bioactive, a careful development, optimization, and validation of extraction process are required. The optimization of the extraction method for the targeted bioactive (three responses) of Methi seeds was successfully achieved by RSM. The effect of three independent variables selected by OFAT method was further optimized at the five-level CCRD. This is the first report on the optimization of extraction conditions for the maximum extraction of three major components, simultaneously from the Methi seeds. The second-order polynomial model and the ANOVA effectively explain the effect and significance of each parameter for each response. The validated information developed during the present study can be used for the preparation of Methi seed extract with a high level of bioactive, as many pharmacological activities are reported in the hydroalcoholic extract, and the enrichment of extract with selected major components may result in an increased efficacy of optimized extract. These technical results are expected to be useful in lab scale extractions during the plant drug standardization and for industrial scale-up extraction in the manufacturing of Methi seed formulations.
The authors thankful to the Director-General, CCRAS, for providing the necessary facilities.
Financial support and sponsorship
CCRAS, Ministry of Ayush, New Delhi.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]