Bioactive compound extraction from giloy leaves and steam using ultrasound: bioactivity, antimicrobial, and LC–MS/MS study

Abstract

Giloy (Tinospora cordifolia) is a medicinal plant rich in bioactive compounds known for their diverse health benefits. This study examined the nutritional value and biological activity of giloy stems and leaves. Hence, giloy stem (GSE), and leaf extract (GLE) was extracted using green extraction technology, ultrasound, and screening secondary metabolites and bioactive compounds. Using a central composite rotatable design combined with RSM, GSE, and GLE's antioxidant activity, total phenolic content and yield were optimized for solvent-to-solid ratio, sonication time, and solvent concertation. The optimum extraction conditions were found to be 22.5:1, 40 min, and 75%. Additionally, the extract inhibited the growth of S. aureus and E. coli. Screening of optimized extract through LC/MS reported the presence of significant polyphenols. Bioactive substances such as catechin, malic acid, quercetin, kaempferol, ellagic acid, hesperidin, and berberine were found. These findings indicate that ultrasonication, a green extraction method, promotes GSE and GLE bioactive chemical extraction. Giloy can make tasty, long-lasting food and drinks as a functional ingredient.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-024-01810-x.

Introduction

Plants represent a rich source of essential resources for human well-being, generously provided by nature. For thousands of years, they have been a primary source of medicinal agents, valued across human cultures, as documented in studies on traditional medicine (Banwo et al., 2021; Tripathy and Srivastav, 2024; Verma et al., 2024). Many therapeutic compounds are extracted from medicinal plants or synthesized as derivatives based on natural ingredients. Tinospora cordifolia, commonly known as giloy, is essential in Indian medicine. Widely distributed across the Indian subcontinent, it is recognized for its potential health benefits, with research highlighting its effectiveness in managing conditions such as diabetes, jaundice, anemia, urinary infections, and dysentery, along with its immune-enhancing properties (Gupta et al., 2023). These medicinal effects are attributed to various secondary metabolites, such as alkaloids, diterpenoid lactones, terpenoids, lignans, steroids, and other phenolic compounds (Prasad et al., 2023). The broad range of health benefits of T. cordifolia has thus gained significant interest in both industrial and academic sectors (Tripathy et al., 2022; Verma et al., 2024).

Cost-effective extraction methods are essential to meet the interest without compromising quality and efficiency. Traditional extraction methods, such as soxhlet and maceration, have drawbacks like low yield, extended extraction times, and high solvent requirements (Pérez and Albero, 2023). Modern green extraction techniques offer a promising alternative: shorter extraction durations, improved extract quality, reduced solvent use, and enhanced environmental safety (Shrivastav et al., 2024; Usman et al., 2023). Among these methods, ultrasound-assisted extraction (UAE) is particularly advantageous due to its relatively low capital costs and scalability, making it a viable option for commercial applications (Bouloumpasi et al., 2024).

Numerous assays and equipment are now available to analyze and confirm the presence of phenolic and antioxidant compounds, with liquid chromatography-mass spectrometry being widely used for metabolomics research. This study aims to optimize extraction parameters, such as solvent concentration, extraction time, and liquid-to-solid ratio, for maximizing extract yield, total phenolic content, flavonoid content, and antioxidant potential through the UAE method, as well as to identify the bioactive compounds present in the optimized extract. The developed protocol may provide valuable insights for applications in both food and pharmaceutical industries.

Material and methods

Sample collection and preparation

Giloy plants were obtained from the Indian Institute of Technology Kharagpur campus. The gathered specimens were thoroughly cleansed using flowing tap water, followed by the separation of steam and leaves. The Giloy stems and leaves were dehydrated using a vacuum dryer (Labard Instruchem Pvt. Ltd., India) at 40 °C until their moisture content decreased to 8% based on their dry weight. The desiccated stem and leaves were ground into a fine powder using a mixer grinder (Bajaj Pvt. Ltd., India) with a particle size ranging from 150 to 125 μ. The resulting powder was then placed in an airtight container and stored in a deep freezer (Voltas, India) at − 18 °C for future usage.

Ultrasound assisted extraction of bioactive compounds

Ultrasonic extraction of dried powder giloy (1g) was performed using a probe-type ultrasound machine (Hielscher Ultrasonicator UP50, Germany) of 50 W power, 30 kHz frequency, 3 mm probe diameter, and 80 mm probe length, with various combinations of independent variables according to CCRD design. Ethanol is used as a solvent. The extraction sample was filtered through a Whatman No. 1 filter, and the filtered extract was centrifuged at 7000 rpm for 15 min at 0 °C. The superannuated extract was then placed in a rotary evaporator to evaporate the solvent, and crude extract was obtained.

Experimental design

Responses based on the collection of experiments were predicted utilizing a central composite rotatable design (CCRD). The formulation parameters were statistically optimized using response surface methods to provide the best extraction conditions. The independent variables used for investigating the responses, including Extraction yield, TPC, TFC, and AOC, were solvent concentration (%), liquid-to-solid ratio (L/S), and time (min) (Kumar et al., 2023). The process parameters for optimizing ultrasound-assisted bioactive compound extraction are shown in Table 1. The Response Surface Methodology (RSM) incorporates a design comprising 20 unique experimental runs, including five replicates at the central point. The resulting data for these experiments is shown in Table S1.

Table 1.

Process parameters for the optimization of ultrasound-assisted bioactive compounds extraction from giloy leaves and stems

Process parameters	Unit	Coded levels
		− 1	Center	+ 1
A: Solvent concentration	%	60	75	90
B: Time	min	10	40	70
C: L/S ratio	mg/mL	15	22.50	30

Extraction yield

The extracted giloy's yield was measured by comparing the weight of the dried giloy extract to the initial dried weight of the giloy powder. The result is quantified as a percentage and displayed below.

$$\text{Extract yeild}\left(\%\right)=\frac{\text{We}}{\text{Wp}}\times 100$$ 1

where We and Wp are wieght of giloy extract and powder respectively.

Biochemical analysis

The total phenolic content and antioxidant activity by the DPPH method of GSE and GLE were evaluated according to the method developed by Tripathy and Srivastav (2023b) with some modifications. Further, the total flavonoid content was measured using the protocol developed by Tripathy and Srivastav (2023a), which had some modifications. The supplementary file (S2. Material and Methods) discussed the detailed modified methodologies.

Modelling and process optimization

Polynomial regression was used to formulate the responses and determine the relationship between the variables and their outcomes. The models' development included fitting each experimental value into the quadratic equation presented hereafter.

$$Y={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_{11}{X}_{11}+{\beta }_{12}{X}_{12}+{\beta }_{22}{X}_{22}$$ 2

where ${\beta }_0$, (${\beta }_1,{\beta }_2$) and (${\beta }_{11},{\beta }_{12},{\beta }_{22}$) are the constants, linear and quadratic regression coefficients respectively and Xi^’s are the coded independent variables.

The objectives were combined into a complete composite function called D(x), which is referred to as the desirability function. It is expressed as follows:

$$\text{D}\left(\text{x}\right)={\left({\text{d}}_1·{\text{d}}_2·{\text{d}}_3·{\text{d}}_4\dots \dots \dots ..{\text{d}}_{\text{n}}\right)}^{1/2}$$ 3

The variables d₁, d₂, …, d_n indicate the individual responses, whereas n denotes the total number of reactions in the measure. Quantitative optimization identifies the precise location that optimizes the desired function. Curvature in the response surfaces, together with the desired function, can lead to the presence of multiple maxima.

Morphological analysis

The morphology of the giloy extract powder was analyzed using a ZEISS EVO 60 scanning electron microscope with an Oxford EDS detector under high vacuum settings (SEM: Carl ZEISS SMT, Germany) after gold coating.

Antimicrobial analysis

The antibacterial effectiveness of the produced films was evaluated using the BSAC disc diffusion susceptibility method in the grain processing unit laboratory of the AGFE department at IIT Kharagpur on 24th December 2023. The evaluation involved using two bacterial species, specifically E. coli (a gram-negative species) and S. aureus (a gram-positive species). The two bacterial species were grown in sterile Muller Hinton Broth at 37 °C until the colonies reached (200–300) × 10⁶ CFU/mL concentration. Turbidity was measured using a spectrophotometer at 600 nm optical density (OD) wavelength after cultivating E. coli and S. aureus to an absorbance at 600 nm OD.

Identification of bioactive compounds by LC/MS

An LC–MS/MS system, including a Waters 2695 separation module and a Micromass Quattro Micro Triple Quadrupole mass spectrometer, was used to analyze polyphenols and metabolites in optimal stem extracts. Data analysis was conducted using MassLynx 4.1 software, as described by Kaur et al. (2021) and Sarkar et al. (2022), with some adjustments. The supplementary file (S2. Material and Methods) discussed the detailed modified methodologies.

Statistical analysis

The extraction, phytochemical analysis, and antioxidant investigations were replicated three times. The results were reported as the mean value with the standard deviation of three similar trials conducted concurrently. The mean and standard deviation were calculated using Microsoft Office Excel 2016 statistical analysis. The data was examined using a one-way analysis of variance (ANOVA) with a confidence level of p ≤ 0.05. The optimization process was conducted utilizing Design of Analysis software version 13.

Results and discussions

Multiple regression results and model adequacy analysis

The study utilized Response Surface Methodology (RSM) with a Central Composite Rotatable Design (CCRD) to optimize solvent concentration (%), liquid-to-solid ratio (L/S), and extraction time (minutes) for extracting bioactive compounds from giloy leaves and stems. The optimization focused on maximizing extraction yield, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity (AOC). Analysis of variance (ANOVA) results indicated the statistical significance of the models, with F-values of 26.78, 23.18, 14.75, and 30.49 for extraction yield, TPC, TFC, and AOC in giloy leaves, and 24.65, 34.56, 12.81, and 24.95 for the same parameters in stems, respectively. Each response displayed a low p-value (p < 0.05), signifying model significance.

Quadratic equations [Eq. (4)–(11)] provided empirical relationships between independent variables and responses. High R² values, recorded at 0.960 and 0.957 for extraction yield, 0.954 and 0.969 for TPC, 0.930 and 0.920 for TFC, and 0.965 and 0.957 for AOC, underscored a strong correlation between the CCRD approach and the quadratic models developed (Table 2). Generally, the R² value rises with the addition of new variables, though it may not always indicate statistical relevance. The adjusted R² (Adj. R²) offers a more reliable measure of model adequacy, as it accounts for multiple variables and reflects only significant improvements in model quality. In this study, the Adj. R² values were 0.924 and 0.918, 0.913 and 0.914, 0.867 and 0.848, and 0.933 and 0.919 for extraction yield, TPC, TFC, and AOC of giloy leaves and stems, respectively. These values confirm the models’ predictive reliability for optimal conditions for maximum extraction yield and TPC from giloy leaves and stems.

Table 2.

Estimated coefficients and corresponding ANOVA data representing the relationship between process parameters and response variables

Sources	Coefficient for the responses
	Leaves				Stem
	YEILD	TPC	TFC	AOC	YEILD	TPC	TFC	AOC
Intercept	24.68	243.25	225.49	7.97	30.74	267.49	239.71	6.86
Ap	− 1.26	8.11	− 5.83**	0.023*	− 0.59*	8.74	− 5.69*	0.08*
Bq	0.78	11.94	− 0.73*	0.056*	1.97	18.47	1.91*	0.11*
Cr	0.56*	4.89**	4.93*	− 0.084*	1.34	− 1.15*	5.95*	− 0.13*
AB	− 0.44*	− 23.04	− 1.08*	0.034*	0.01*	− 17.85	− 6.02*	− 0.08*
AC	− 0.40*	8.03	5.93*	− 0.67	1.19**	− 0.63*	5.28*	− 0.47
BC	− 0.71	7.01	15.10	− 0.58	0.79*	11.66	11.41	− 0.39
A2	− 3.95	− 13.52	− 17.98	1.34	− 4.88	− 19.44	− 18.51	1.12
B2	− 2.52	− 13.78	− 22.22	1.16	− 3.08	− 14.98	− 23.22	0.96
C2	− 2.54	− 17.10	− 22.37	0.73	− 2.67	− 24.22	− 23.19	0.59
ANOVA
Pmodel	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001	< 0.0001
Plof	NS	NS	NS	NS	NS	NS	NS	NS
F value	26.78	23.18	14.75	30.49	24.65	34.56	12.81	24.95
R2	0.960	0.954	0.930	0.965	0.957	0.969	0.920	0.957
Adj. R2	0.924	0.913	0.867	0.933	0.918	0.941	0.848	0.919

TPC, total phenolic contents; TFC, total flavanoid content; AOC, antioxidant activity; NS, not significant (p > .05); lof, lack of fit; Adj, adjusted

All terms are significant at p < 0.05 else marked as * and **; ** 0.05 < p < 0.1, *p > 0.10

^psolvent concetration (%); ^qTime: ^rL/S

Effects of UAE parameters on extraction yield

Maximizing extraction yield is essential, especially in natural and pharmaceutical products, as a higher yield can reduce total production costs (Putra et al., 2023). The impact of three independent variables on yield was analyzed through significant coefficients (p < 0.05) in a quadratic polynomial equation. Results showed that extraction yield (Y1) was significantly affected by solvent concentration, sonication time, and solvent-to-solid ratio (p < 0.05) with two first-order effects (A and B), three second-order effects (A², B², and C²), and an interaction effect (BC) for leaves, and two first-order effects (B and C) and three second-order effects (A², B², and C²) for stems. Linear coefficients in the predictive equations [Eqs. (4), (5)] for Y1 indicated that positive coefficients for B and C enhanced extraction yield with increased sonication time and solvent-to-solid ratio.

$${\text{Yield}}_{\text{Leaf}}=24.68-1.\text{26A}+0.\text{78B}+0.\text{56C}-0.\text{44AB}-0.40\text{AC}-0.\text{71BC}-3.{\text{95A}}^2-2.{\text{52B}}^2-2.{\text{54C}}^2$$ 4

$${\text{Yield}}_{\text{Stem}}=30.74-0.\text{59A}+1.\text{97B}+1.\text{34C}-0.00\text{7AB}+1.\text{19AC}+0.\text{79BC}-4.{\text{88A}}^2-3.0{\text{8B}}^2-2.{\text{67C}}^2$$ 5

Three-dimensional (3D) response surface plots [Figs. 1(A–C), 2(A–C)] illustrate the relationships between extraction parameters and yield. In Figs. 1(A) and 2(A), the response surface plot shows solvent concentration versus sonication time's influence on extraction yield at a constant solvent-to-solid ratio of 22.5 mL/g. Results revealed that prolonged sonication time notably boosted yield. The interaction effect also indicated that extended sonication time achieved higher yields (25.63%), as sonication enhances hydration, fragmenting materials, and increasing solute mass transfer without substantial solvent degradation (Li et al., 2024).

Fig. 1.

Response surface plots for the effect of (A) solvent concentration and time for extraction yield, (B) solvent concentration and L/S ratio for extraction yield, (C) time and L/S ratio on extraction yield, (D) solvent concentration and time for TPC, (E) solvent concentration and L/S ratio for TPC, (F) time and L/S ratio on the TPC, (G) solvent concentration and time for TFC, (H) solvent concentration and L/S ratio for TFC, (I) time and L/S ratio on TFC, (J) solvent concentration and time for AOC, (K) solvent concentration and L/S ratio for AOC, (L) time and L/S ratio on AOC of the giloy leaf extract

Fig. 2.

Response surface plots for the effect of (A) solvent concentration and time for extraction yield, (B) solvent concentration and L/S ratio for extraction yield, (C) time and L/S ratio on extraction yield, (D) solvent concentration and time for TPC, (E) solvent concentration and L/S ratio for TPC, (F) time and L/S ratio on the TPC, (G) solvent concentration and time for TFC, (H) solvent concentration and L/S ratio for TFC, (I) time and L/S ratio on TFC, (J) solvent concentration and time for AOC, (K) solvent concentration and L/S ratio for AOC, (L) time and L/S ratio on AOC of the giloy stem extract

Figures 1(B) and 2(B) illustrate solvent concentration versus solvent-to-solid ratio with sonication time fixed at 40 min. Solvent concentration had a minimal effect on yield, with values increasing from 60 to 90%. Higher solvent concentration either positively or negatively influenced yield: increased concentration raised solvent density, which could decrease extraction yield (Shen et al., 2023). Yield improvements only occurred when the solvent-to-solid ratio increased alongside solvent concentration.

The 3D surface plots in Figs. 1(C) and 2(C) show time and solvent-to-solid ratio effects at a constant 75% solvent concentration. The plots indicated that increased solvent-to-solid ratio and sonication time positively impacted extraction yield. This increase is likely due to enhanced mass transfer from the ultrasound's mechanical effects (Buvaneshwaran et al., 2023). Higher solvent-to-solid ratios allow more significant solvent infiltration into cells and compound diffusion into the solvent. However, with a low solvent-to-solid ratio, the solvent reaches saturation more quickly, limiting overall extraction yield (Zakaria et al., 2021).

Effect of independent variables on TPC

The total phenolic content (TPC, Y2) was notably affected by the solvent concentration (%), liquid-to-solid ratio (L/S), and extraction duration (minutes) (p < 0.05). These factors significantly influenced both leaf and stem samples' linear effects (A and B) and the quadratic terms (A², B², and C²). However, interaction effects varied, with three interactions (AB, AC, and BC) impacting the leaves and two interactions (AB, and BC) affecting the stems. Based on the linear coefficients and the predicted equations for Y2 [Table 2, Eqs. (6) and (7)], the positive coefficients for factors A, B, and C indicated that increasing solvent concentration and extraction time led to higher leaf TPC levels. Conversely, a negative coefficient for C suggested that an increased solvent-to-solid ratio was associated with a reduced TPC in stems.

$${\text{TPC}}_{\text{Leaf}}=243.25+8.\text{11A}+11.\text{94B}+4.\text{89C}-23.0\text{4AB}+8.0\text{3AC}+7.0\text{1BC}-13.{\text{2A}}^2-13.{\text{78B}}^2-17.10{\text{C}}^2$$ 6

$${\text{TPC}}_{\text{Stem}}=267.49+8.\text{74A}-1.\text{15B}-17.\text{85C}-17.\text{85AB}-0.\text{63AC}+11.\text{66BC}-19.{\text{44A}}^2-14.{\text{98B}}^2-24.{\text{22C}}^2$$ 7

The 3D response surface plots [Figs. 1(D–F) and 2(D–F) for leaves and stems] illustrate the relationship between TPC and the extraction parameters. As shown in Figs. 1(D) and 2(D), TPC was lower with reduced solvent concentration, while the solvent-to-solid ratio remained constant at 22.5 mL/g. These interaction effects suggested that prolonged sonication time could yield a higher TPC. Similar patterns were observed in Figs. 1(A) and 2(A), where extended sonication time at elevated extraction temperatures led to higher extraction yields. Prolonged sonication likely enhanced solvent diffusion into the cells, increased the formation rate of cavitation bubbles and improved the solubility and release of phenolic compounds from the cells (Pradal et al., 2016), ultimately resulting in increased phenolic yield.

At a constant sonication time of 40 min, the impact of varying solvent concentrations and solvent-to-solid ratios on TPC is depicted in Figs. 1(E) and 2(E). The TPC value initially rose with an increase in the solvent-to-solid ratio, peaking at a certain point before beginning to decrease. At a ratio of 22.5 mL/g, TPC reached 254.13 mg GAE/g. However, as the solvent-to-solid ratio declined, a marked decrease in TPC was observed, which could be attributed to the dilution effect from a lower solvent-to-solid ratio, resulting in reduced TPC. These findings align with the study by Irakli et al. (2018), who observed that TPC in olive leaf extracts increased during the first 30 min of ultrasonic exposure, only to decline after extended exposure due to structural degradation caused by ultrasonic waves.

Figures 1(F) and 2(F) show the surface plots, displaying sonication time versus solvent-to-solid ratio at a solvent concentration of 75%. These plots reveal that extraction time had minimal impact on TPC, while interaction effects highlighted that maximum TPC was achieved at the highest solvent-to-solid ratio and sonication duration. This effect could be attributed to ultrasonic waves disrupting plant cell walls during sonication, which promoted the release of intracellular materials into the solvent, enhancing TPC levels. The UAE process generally progresses in two stages: an initial washing phase, where soluble compounds dissolve at the matrix surface, followed by a slower diffusion phase, where mass transfer is governed by diffusion and osmosis (Bimakr et al., 2017). However, excessive sonication exposure can degrade phenolic compounds in the extract (Dobrinčić et al., 2020).

Effect of independent variables on TFC

The total flavonoid content (TFC, Y3) was significantly affected by solvent concentration (%), liquid-to-solid ratio (L/S), and extraction time (minutes) (p < 0.05), particularly in three second-order effects (A², B², C²) and one interaction effect (BC) for both leaves and stems. The linear coefficients and predicted equations [Eqs. (8), (9)] for Y3 in Table 2 show that a positive coefficient for C indicated an increase in TFC with a higher L/S for leaves. In contrast, for stems, TFC increased with higher values of B and C. Conversely, a negative coefficient indicated that TFC decreased as the variable increased.

$${\text{TFC}}_{\text{Leaf}}=225.49-5.\text{83A}-0.\text{73B}+4.\text{94C}-1.0\text{8AB}+5.\text{94AC}+15.10\text{BC}-17.{\text{99A}}^2-22.{\text{24B}}^2-22.{\text{38C}}^2$$ 8

$${\text{TFC}}_{\text{Stem}}=239.71-5.\text{69A}+1.\text{19B}+5.\text{95C}-6.0\text{2AB}+5.\text{28AC}+11.\text{41BC}-18.{\text{51A}}^2-23.{\text{22B}}^2-23.{\text{19C}}^2$$ 9

The response surface 3D plots illustrate the relationship between TFC and the extraction parameters, as shown in Figs. 1(G–I) and 2(G–I) for leaves and stems. Figures 1(G) and 2(G) depict the interaction between solvent concentration and sonication time, showing that high solvent concentration led to low TFC when the solvent-to-solid ratio was constant at 22.5 mL/g. Higher solvent concentration increases viscosity, which can reduce solvent penetration into the plant matrix, slowing mass transfer and lowering extraction efficiency (Cui et al., 2024). Furthermore, increasing sonication time initially raised TFC, likely due to improved solvent diffusivity and the enhanced desorption of flavonoids from cells (Pradal et al., 2016). However, prolonged extraction reduced TFC, likely due to heat-generated flavonoid degradation.

The solvent concentration and solvent-to-solid ratio effects on TFC at a constant sonication time of 40 min are shown in Figs. 1(H) and 2(H). TFC increased with the solvent-to-solid ratio up to a certain point, after which it began to decline, particularly at 22.5 mL/g. Additionally, higher solvent concentration reduced TFC, possibly due to the dilution effect, as a higher diluted solvent resulted in lower total phenolic content (Liu et al., 2023).

Figures 1(I) and 2(I) display the response surface for sonication time versus solvent-to-solid ratio at a fixed solvent concentration of 75%. The surface plots indicate that extended extraction time reduced TFC while increasing the L/S ratio enhanced TFC. This could be attributed to the ultrasonic waves breaking down the plant cell wall, releasing intracellular compounds more efficiently into the solvent, which increased TFC. However, excessive sonication exposure eventually led to the degradation of phenolic compounds (Abi-Khattar et al., 2022).

Effect of independent variables on AOC

The antioxidant activity (AOC, Y4) was significantly impacted by solvent concentration (%), liquid-to-solid ratio (L/S), and extraction time (minutes) (p < 0.05). These factors notably affected leaf and stem samples, mainly the quadratic terms (A², B², and C²) and two interaction terms (AC and BC). Based on the linear coefficients and the predictive models for Y4 [Table 2, Eqs. (10) and (11)], positive coefficients for factors A and B suggest that increasing the solvent concentration and extraction time led to higher antioxidant levels in the leaves and stems. In contrast, a negative coefficient for C implies that a higher solvent-to-solid ratio was linked to reduced antioxidant levels for both leaves and stems.

$${\text{AOC}}_{\text{Leaf}}=7.97+0.0\text{3A}+0.0\text{5B}-0.0\text{8C}+0.0\text{3AB}-0.\text{67AC}-0.\text{58BC}+1.{\text{34A}}^2+1.{\text{16B}}^2+0.{\text{73C}}^2$$ 10

$${\text{AOC}}_{\text{Stem}}=6.86+0.0\text{8A}+0.\text{11B}-0.\text{13C}-0.0\text{7AB}-0.0\text{47AC}-0.\text{39BC}+1.{\text{12A}}^{2}0+0.{\text{97B}}^2+0.{\text{59C}}^2$$ 11

The three-dimensional (3D) response surface plots [Figs. 1(J–L) and 2(J–L)] visually represent the relationships between extraction parameters and yield. In Figs. 1(J) and 2(J), the plot illustrates how solvent concentration and sonication time affect AOC at a fixed solvent-to-solid ratio of 22.5 mL/g. The results indicate that extended sonication time considerably enhanced antioxidant levels. Additionally, an increase in solvent concentration raised the antioxidant content in the extract, likely because higher solvent concentrations improve the dissolution and extraction of antioxidants from the plant material by optimizing solvent compatibility with these compounds (Rodríguez De Luna et al., 2020).

Figures 1(K) and 2(K) depict the influence of solvent concentration and solvent-to-solid ratio with sonication time set at 40 min. Solvent concentration positively impacted yield, meaning that as solvent concentration increased, antioxidant levels rose. However, antioxidant activity was lower when the solvent-to-solid ratio was at its highest, likely due to a dilution effect where the more diluted solvent decreased total phenolic content (Liu et al., 2023).

The 3D surface plots in Figs. 1(L) and 2(L) show the effects of time and solvent-to-solid ratio at a constant solvent concentration of 75%. These plots indicate that longer sonication time positively affected extraction yield, possibly due to increased mass transfer from the mechanical action of ultrasound (Buvaneshwaran et al., 2023). Moreover, with a lower solvent-to-solid ratio, antioxidant capacity was higher. The reduced solvent volume minimizes dilution, resulting in a more concentrated antioxidant solution corresponding to higher antioxidant activity (Ponticelli et al., 2024).

Optimization and process validation

Table 3 demonstrates the model's accuracy by comparing the predicted values with the experimental data. The optimal conditions for the current investigation include a solvent concentration of 75%, an extraction period of 40 min, and an L/S ratio of 22.5:1. Slight adjustments were made to the projected optimal conditions for validation. The L/S ratio was 23:1, but the other two parameters were remained constant. The calculated discrepancy between predicted and observed values is below 5%. As all calculated RSE values were below 5%, as shown in Table 3, the findings suggest an insignificant difference between the actual values and predicted data, thus confirming the model's validity. Also, the desirability parameter was 0.711 at optimum conditions. This confirmation highlights that the selected parameters for the ultrasound-assisted extraction (UAE) procedure are ideal for obtaining the maximum yield of green extract and bioactive components from giloy stem powder.

Table 3.

Predicted and experimental values of extract yield, total phenolic content (TPC), and DPPH with the percentage of error at optimized extraction conditions

Response	Leaf			Stem
	Predicted vales	Experimental value	Error (%)	Predicted vales	Experimental value	Error (%)
Solvent concentration (%)	75	75	–	75	75	–
Time (min)	40	40	–	40	40	–
L/S ratio (mg/mL)	22.5	23	–	22.5	23	–
Extraction yield (%)	24.68	25.04	1.46	30.74	31.31	1.85
TPC (mg GA/g of sample)	243.25	245.87	1.08	267.49	264.37	1.12
TFC (mg QA/g of sample	225.49	222.97	1.12	239.71	236.97	1.14
AOC (%)	7.97	8.24	3.40	6.86	7.19	4.89

TPC, total phenolic contents; TFC, total flavanoid content; AOC antioxidant activity

Morphological analysis

Scanning electron microscopy (SEM) was conducted to examine the microstructural and morphological changes in the untreated stem powder and the residue left after ultrasound-assisted extraction of phytochemicals. The untreated sample exhibited intact, smooth cell walls [Fig. 3(A, B)]. In contrast, the ultrasound-treated sample displayed noticeable surface disruptions and a more open structure, facilitating the release of cellular components, including phytochemicals, into the extraction solvent [Fig. 3(C, D)]. These structural alterations can be attributed to the propagation of ultrasound waves, which generate and collapse cavitation bubbles (Kaur et al., 2023). The cavitation process and resulting microstreaming enhance porosity, as observed in the ultrasound-treated samples (Chemat et al., 2017). SEM images showed apparent differences in structure, with untreated samples remaining intact compared to sonicated samples' porous, cracked cell walls. These findings were similar to those of Karunanithi and Venkatachalam (2019) and Zhou et al. (2018) reported.

Fig. 3.

Surface morphologies of giloy stem powder (A, B) before extraction and (C, D) after extraction

Antimicrobial analysis

This study explored the antimicrobial potential of plant extracts against common human enteric pathogens, known as indicator organisms, due to their association with various diseases and infections. Notable variations were observed in the antimicrobial effects of giloy stem extract on pathogenic strains, such as E. coli and S. aureus. Across all tested concentrations, both Gram-positive and Gram-negative bacteria displayed strong antibacterial responses. Using the agar disc diffusion method, the antibacterial impact of the extract was validated, with inhibition zones measured for the ultrasound-treated extract of Tinospora cordifolia stem (Fig. S1). For the ultrasound-treated stem extract, the inhibition zone reached 26 mm for E. coli and 19 mm for S. aureus. Similarly, ultrasound-treated leaf extract showed inhibition zones of 23 mm for E. coli and 20 mm for S. aureus. These results highlight the extract’s strong effectiveness against Gram-positive and Gram-negative bacteria, likely due to bioactive compounds like phenols and flavonoids. Comparable findings on the antibacterial effects of giloy extract were reported by Paul et al. (2022) and Singh et al. (2022). This research suggests that ultrasonic extraction effectively reduces the presence of harmful bacteria, thereby helping to minimize contamination.

Analysis of optimized extract using LC–MS

LC–MS/MS was used to qualitatively evaluate the chemical composition of phenolic compounds in wine. LC–MS/MS was chosen for its exceptional selectivity and sensitivity. The analysis employed gradient high-performance liquid chromatography (HPLC) with absorbance detection and mass spectrometry (MS) with an electron interface (Kamboj et al., 2023). A total of 33 prominent phyto-compounds were found in the stem extract. These compounds were verified using our internal library and existing literature (Table 4). The main factors used to verify the chemicals were examining high-energy fragmentation patterns and the m/z ratio. The extract comprises various prominent chemicals, including catechin, quercetin, kaempferol, ellagic acid, hesperidin, and berberine. The compounds exhibit substantial pharmacological promise, including antioxidant, anti-obesity, anticancer, antidiabetic, immunity-enhancing, and antiulcer activities (Garg et al., 2022). Malic acid, a dicarboxylic acid, is the chemical that causes the sour taste in food. It is frequently employed as a supplement in several food applications (Sharma et al., 2021). More precisely, berberine, a crucial constituent found in giloy extract, has qualities that safeguard the cardiovascular system. Thakur et al. (2022) demonstrated that it benefits health by decreasing endothelium infection. Furthermore, many additional compounds were identified, exhibiting diverse uses in food and human health domains. Including these chemicals will significantly improve the efficacy of the created products in various health-enhancing activities.

Table 4.

Identification of bioactive compounds present in giloy stem by LC–MS

S. nos.	Compound	Formula	Retention time (Rt)	Mass	Mass fragments
1	Methylnaltrexone	C21H26NO4+	27.92	357.85	357.85, 311.81, 284.09, 282.09
2	O-galloylnorbergenin iii	C20 H18O13	15.19	480.58	151.04
3	Ellagic acid	C14H6O8	39.45	302.62	228.75, 199.72, 149.4, 121.11
4	Ethyl gallate	C9H10O5	18.24	198.78	149.09, 121.91
5	Chlorogenic acid	C16H18O9	33.55	354.49	199.66
6	Mangiferin	C19H18O11	15.61	423.04	328, 308, 28, 258.68
7	Galloylhexose	C13H16O10	34.42	335.00	279.52, 209.85, 168.95
8	Di-O-galloyl-2,3-(S)-hexahydroxydiphenoyl-scyllo-quercito	C34H26O21	29.09	481.99	149.02
9	Kaempferol-3-O-rutinoside	C27H30O15	1.42	595.37	225.88, 258.74
10	Magnoflorine	C20H24NO4+	34.43	337.69	282.41, 240.82, 209.85,196.84
11	Malic Acid	C4H6O5	1.28	136.34
12	Catechin	C15H14O6	32.68	292.50	228.20, 19.12
13	Quercetin	C15H10O7	49.47	303.47	112.52
14	Aurasperone D	C31H24O10	34.59	556.47	339.6, 228.01
15	Gallopamil	C28H40N2O5·HCl	34.66	485.50	–
16	Pumiloside	C26H28N2O9	32.68	512.72	–
17	Gravolenic acid	C14H16O6	33.75	280.61	–
18	Citrusin B	C27H36O13	20.21	568.00	–
19	Kaempferol	C15H10O6	27.92	286.09	–
20	9,10- Dihydroxy-12, 13- epoxyoctadecanoate	C18H34O5	18.24	331.54	–
21	Jatrorrhizine	C20H20NO4+1	43.65	338.67	–
22	Berberine	C20H18NO4+	34.66	336.51	–
23	Palmatine	C21H24NO4 +	34.66	351.99	–
24	Curcumin	C21H20O6	0.936	369.91	–
25	Hesperidin	C28H34O15	29.09	611.22	–
26	N-trans-feruloyl-4'-O-methyldopamine	C19H21NO5	33.67	344.36	–
27	N-Trans-feruloyltramine	C18H19NO4	13.56	315.38	–
30	Coumaroylquinic acid	C16H18O8	43.65	338.67	186.75,149.82, 116.47
31	Kaempferol diglucoside	C27H30O16	29.10	611.22	255.72, 256, 198
33	Digalloyl-hexose-malic acid I	C24H24O18	30.69	599.22	481.99, 456.06, 334.36

Supplementary Information

Below is the link to the electronic supplementary material.

Author contribution

Salam MaheshKumar Singh: Conceptualization; Data curation; Formal analysis; Methodology; Writing—original draft; Writing—review & editing, Soubhagya Tripathy: Data curation; Formal analysis; Methodology; Validation; Software, Writing—original draft; Writing—review & editing, Prem Prakash Srivastav: Supervision; Validation; Visualization; Writing—review & editing.

Funding

No funding has been received for this research work.

Availability of data and materials

Data will be available on request.

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Declarations

Conflict of interest

The authors declare no conflict of interest. To affirm the integrity of our submission, we confirm that the manuscript is not concurrently under consideration or submitted elsewhere. It adheres strictly to the journal format specified in the author's instructions, and there are no conflicts of interest. All co-authors have endorsed the manuscript and consent to its submission to “Food Science and Biotechnology”.

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