
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39278966
72214
10.1038/s41598-024-72214-4
Article
Screening genotypes and optimizing ultrasonic extraction of phenolic antioxidants from Rheum ribes using response surface methodology
Ghasemi Ghader
Fattahi Mohammad mo.fattahi@urmia.ac.ir
mohamadfattahi@yahoo.com

Alirezalu Abolfazl
https://ror.org/032fk0x53 grid.412763.5 0000 0004 0442 8645 Department of Horticultural Sciences, Faculty of Agriculture, Urmia University, Urmia, Iran
15 9 2024
15 9 2024
2024
14 2154429 3 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The flowers and stems of Rhubarb (Rheum ribes L.) are known to contain effective antioxidant compounds that have potential antidiarrheal properties in traditional medicine. This study was conducted to screen various genotypes of Rhubarb for their phytochemical and antioxidant activity and optimize the extraction parameters using the response surface methodology (RSM). The study found high diversity among the different genotypes (G1–G13) in terms of their flowers and stems. The total phenolic content (TPC) in the flowers of R. ribes varied significantly, showing values between 9.80 and 81.53 mg GAE g–1 DW. In the stems, TPC ranged from 2.87 to 16.33 mg GAE g−1 DW. Similarly, the total flavonoid content (TFC) in the flowers ranged from 0.33 to 1.32 mg Qu g−1 DW, while in the stems, it was between 0.05 and 0.38 mg Qu g−1 DW. The antioxidant activity, indicated as µmol Fe2+ g–1 DW, varied from 7.42 to 59.87 in the flowers and from 0.14 to 15.99 in the stems. Hierarchical cluster analysis (HCA) identified five distinct clusters among the collected genotypes. Subsequent analysis of variance and principal components analysis (PCA) revealed that the flowers of G8 (G8F) from Tehran to Lavasan exhibited the highest total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity (FRAP). Given these findings, G8F was chosen for further optimization in the study. The RSM was designed based on a Box–Behnken design (BBD) to determine the optimal extraction conditions, including extraction temperature (30–80 °C), extraction time (5–15 min), and ethanol concentration (25–75%, ethanol to water, v/v). The responses measured were total phenolic content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC), 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging, and Ferric reducing/antioxidant power (FRAP) assays. The optimal extraction conditions for all responses or desirability indices were X1: 80 °C, X2: 15 min, and X3: 53.14%, which resulted in TPC (99.32 mg GAE.g−1 DW), TFC (3.00 mg Qu.g−1 DW), TAC (1.12 mg cyanidin-3-glucoside (Cy-g) g−1 DW), FRAP (110.22 µmol Fe+2/g DW), and DPPHsc (88.20%). The R2 values (0.91–0.99) indicated that the RSM models were acceptable.

Keywords

RSM
Optimization
Rhubarb
Flavonoid
DPPH
FRAP
Modeling
Subject terms

Biochemistry
Biological techniques
Plant sciences
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Iran, with a highly diverse climate, has a very varied flora1. This exceptional climatic natural resource of Iran provides promising plants for functional foods2. Rhubarb, belonging to the Polygonaceae family, is a perennial and herbaceous plant growing from short, thick rhizomes and is well-known as Rivas and Oshgun in Iran. In Iranian flora, the genus Rheum is represented by three species of Rheum persicum, Rheum turkestanicum, and Rheum ribes. Among them, Rheum ribes are well-known in most parts of Iran3. R. ribes has naturally grown in North and Central Asia in countries such as Iran, Turkey, and Iraq and is generally used as a vegetable4. Chemical constituents of medicinal plants and their biological activities are influenced by genetic and environmental factors5. Several compounds including anthraquinones chrysophanol, physcion, and emodin, the flavonoids quercetin, 5-desoxyquercetin, quercetin 3-O-rhamnoside, quercetin 3-O-galactoside, and quercetin 3-O-rutinoside have been reported in previous studies on R. ribes6–10.

Several physiological and biochemical processes in the human body may lead to the production of oxygen free radicals or reactive oxygen species (ROS). Excessive production of ROS can result in oxidative damage to important biological molecules like fats, proteins, and DNA, ultimately leading to various chronic diseases such as cancer, diabetes, aging, and other destructive diseases in human beings11. The food industry often employs synthetic antioxidants, including butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), and tert-butylhydroquinone (TBHQ), to prevent unwanted oxidation. However, concerns exist about their use in high concentrations, particularly since BHT and BHA have been linked to liver damage and carcinogenesis12. Recently plant secondary metabolites have received much attention in human and animal functional foods consumption13.

Polyphenols and flavonoids, which are found in fruits, vegetables, and medicinal plants, have been reported as potent natural antioxidants14–16 and shown to possess various beneficial properties such as antioxidant, antimutagenic, antiallergic, anti-inflammatory, and antimicrobial effects14,17–23. Natural secondary compounds are found to be well correlated with antioxidant potential24. The antioxidant effectiveness of natural products is often due to phenolic compounds25,26.

Bioactive compounds extracted from plant material are considered the first step in using phytochemicals in food preparation, pharmaceuticals, cosmetics, and sanitary industries27. The complexity of the extraction process of these compounds due to their entrapment by insoluble structures such as vacuoles of plant cells and lipoprotein bilayers, as well as the need for traditional methods for large volumes of solvent and related environmental limitations28, necessitates the use of modern techniques for the extraction of bioactive compounds. Over the last few years, modern techniques have been developed to overcome these problems. Among them, ultrasound-assisted extraction (UAE) is the most promising method29.

The extraction of compounds can be greatly influenced by various parameters, such as extraction temperature and time, solvent composition, and solid/liquid ratio. These factors can impact the amount, type, and structures of the extracted compounds29–32. To obtain the desired compounds in high yield, these parameters can be optimized. One way to achieve this is through the use of response surface methodology (RSM), which involves statistical and mathematical techniques. RSM is useful in developing new products or improving existing ones. In RSM, the goal is to optimize the responses by considering multiple variables. It is a powerful tool for investigating independent variables and their interactions and is commonly used in optimizing parameters for compound extraction processes30,31. RSM can reduce the number of experimental runs required while still evaluating multiple parameters and their interactions33,34. Also, RSM optimization was used for anthocyanins35,36, phenolic compounds29,30,36,37, and protein38,39 from different plant materials.

R. ribes is rich in flavonoids, stilbenes, and anthraquinones, making it a valuable reservoir of phenolic antioxidants. As the quest for potent natural antioxidants continues within the scientific community, the introduction of new sources in essential. Although there are some reports on the chemical composition of R. ribes, there has been limited attention given to enhancing the extraction methods for its extracts8,10. To address this gap, our study evaluated different organs and genotypes of the plant, which is high in phenolic antioxidants, to select the best ones. We also optimized the extraction methods for phenolic and flavonoid compounds using RSM in the selected genotype, aiming to obtain maximum yields of polyphenols, total flavonoids, and antioxidants.

Materials and methods

Collection of plant materials

Flower and stem organs of wild-growing Rhubarb genotypes were collected from different provinces of Iran (Table 1). Since the plant is not an endangered plant, the collection of samples was done from natural habitats with the permission of the university only for academic purposes. The samples were collected for academic research in accordance with applicable institutional, national, and international guidelines, including the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. The collected plants were identified and deposited from a voucher specimen (no 1508) in the Herbarium of the Department of Horticulture at Urmia University, Iran. The species were identified by Mr. Shahram Bahadori, PhD student of the University of Tehran, Tehran, Iran. To achieve uniformity, the samples were air-dried at room temperature (25 °C) and in the darkness prior to being ground into a fine powder using a porcelain mortar and pestle for extraction. Dry powdered samples of R. ribes were stored in closed, sealed containers at 4 °C in the dark until extraction.Table 1 Locations and phytochemical attributes of different Rhubarb (Rheum ribes L.) genotypes and mean comparasion and ANOVA of Total Phenol Content, Total Flavonoid Content, antioxidant activity (FRAP).

Genotype	Sampling locations	Longitude	Latitude	Altitude (m)	Phenol: TPC (mg GAE. g−1 DW)	Flavonoid: TFC (mg Qu. g−1 DW)	Antioxidant: FRAP (µmol Fe+2.g−1 DW)	
Flower	Stem	# P value	Flower	Stem	# P value	Flower	Stem	# P value	
G1	West Azerbaijan-Oshnavieh	45.192°	37.09°	1943	67.80 ± 0.49b	11.47 ± 0.18b	**	1.06 ± 0.03b	0.34 ± 0.02ab	**	30.19 ± 0.64d	10.42 ± 0.72c	**	
G2	West Azerbaijan-Khoy	45.1°	38.385°	1593	11.70 ± 0.57i	5.70 ± 0.30ef	**	0.37 ± 0.02ef	0.15 ± 0.00e	**	9.51 ± 0.75 g	1.70 ± 0.01hi	**	
G3	West Azerbaijan-Takab	46.852°	36.519°	2191	30.31 ± 0.28d	16.33 ± 0.35a	**	0.95 ± 0.01bc	0.27 ± 0.02c	**	25.06 ± 1.05ef	3.55 ± 0.31f.	**	
G4	West Azerbaijan-Bukan	45.972°	36.607°	1718	15.59 ± 0.27 h	5.27 ± 0.21ef	**	0.52 ± 0.04e	0.17 ± 0.00e	**	9.28 ± 0.43 g	1.55 ± 0.09i	**	
G5	East Azerbaijan-Shabestar	45.364°	38.376°	1790	35.18 ± 0.40c	9.83 ± 0.57 cd	**	1.10 ± 0.06b	0.19 ± 0.00de	**	35.66 ± 0.33c	6.39 ± 0.10d	**	
G6	East Azerbaijan-Saray	45.559°	37.862°	1311	23.94 ± 0.66f.	4.44 ± 0.38f.	**	0.97 ± 0.05bc	0.06 ± 0.00f.	**	22.31 ± 1.26f.	0.37 ± 0.05jk	**	
G7	East Azerbaijan-Maragheh	46.322°	37.473°	2976	10.41 ± 0.21Ij	5.02 ± 0.30f.	**	0.83 ± 0.03 cd	0.07 ± 0.00f.	**	24.58 ± 1.70ef	0.98 ± 0.07ij	**	
G8	Tehran-Lavasan	51.614°	35.834°	2134	81.53 ± 1.08a	9.06 ± 0.66d	**	1.32 ± 0.07a	0.33 ± 0.00b	**	59.87 ± 0.70a	15.99 ± 0.51a	**	
G9	Markazi-Arak	49.750°	34.044°	2268	35.51 ± 0.48c	6.61 ± 0.49e	**	1.09 ± 0.01b	0.23 ± 0.02 cd	**	54.47 ± 2.09b	3.29 ± 0.02 fg	**	
G10	Kermanshah-Kermanshah	47.239°	34.42°	3035	26.91 ± 0.33e	2.78 ± 0.69 g	**	1.01 ± 0.06b	0.05 ± 0.00f.	**	30.23 ± 0.86d	0.14 ± 0.04 k	**	
G11	Zanjan-Zanjan	48.557°	36.735°	2399	17.57 ± 0.44 g	10.96 ± 0.20bc	**	0.43 ± 0.00ef	0.18 ± 0.00e	**	10.29 ± 0.54 g	5.52 ± 0.09e	**	
G12	Kurdistan-Baneh	45.911°	36.042°	2078	27.30 ± 0.28e	5.39 ± 0.43ef	**	0.76 ± 0.14d	0.14 ± 0.01e	**	25.58 ± 1.04e	2.48 ± 0.07gh	**	
G13	Kurdistan-Saqqez	46.310°	35.940°	1896	9.80 ± 0.10j	9.30 ± 0.76d	ns	0.33 ± 0.03f.	0.38 ± 0.0a	*	7.42 ± 0.64 g	13.28 ± 0.29b	**	
## P value					**	**		**	**		**	**		
#P value is a significant level in comparison between organs flower and stem.

##P value was calculated to determine the significant difference between genotypes.

ns, *, ** are shown non-significant, significant at P ≤ 0.05, P ≤ 0.01 respectively.

Significant values are in [bold].

Extraction of flower and stem organs of R. ribes genotypes

The dried flower and stem materials (1 g) were extracted in separate vials (50 ml) containing 20 ml of ethanol 75% using ultrasonic cleaner device (E 120 H Model: Elmasonic, Singen, Germany) for 30 min at 30 °C with ultrasonic frequency of 37 kHz. In the present study, these extraction conditions are considered as normal extraction method. Then, the extracts were centrifuged (4000 g, 15 min) and poured into clean vials. The extracts were kept at four °C and darkness to measure the parameters. This study was performed to examine the TPC and TFC and antioxidant activity (FRAP) in different genotypes of R. ribes (Table 1).

Solvent extraction of dried flower of G8 using the RSM

For ultrasonic-assist extraction, dried flower material of G8 (100 mg) was used in separate vials. Independent factors were temperatures (X1), time (X2), and different ratios of ethanol to water (X3) (Table 2). Then, the extracts were centrifuged (4000 g, 15 min) after being poured into clean vials. The prepared were kept at 4 °C in darkness until the parameters were evaluated.Table 2 The experimental runs with five responses including TPC; Total Phenol Content ;TFC, Total Flavonoid Content; antioxidant activity (DPPH and FRAP); and TAC, Total Anthocyanins Content; and three factors including of X1, Ultrasonic temperature (°C); X2, Ultrasonic Time (min); X3, Ethanol to water (%).

	Factors	Observed	
Run	X1	X2	X3	TPC	TFC	TAC	FRAP	DPPHsc%	
1	0	0	0	83.00	3.05	2.74	91.23	76.88	
2	0	0	0	84.00	3.12	2.65	91.07	77.56	
3	0	0	0	84.00	3.18	2.73	91.15	78.46	
4	0	− 1	− 1	61.50	2.04	0.89	72.42	74.96	
5	0	− 1	1	88.43	2.95	1.36	94.19	85.53	
6	0	1	1	82.21	2.71	1.17	89.35	84.07	
7	1	0	1	91.57	3.16	3.07	91.32	78.86	
8	1	1	0	100.07	3.08	1.00	110.48	82.44	
9	− 1	0	1	94.36	3.08	1.43	93.65	75.61	
10	− 1	1	0	104.21	3.00	1.33	97.23	88.00	
11	− 1	0	− 1	74.00	2.50	2.35	83.62	77.48	
12	0	1	− 1	69.79	2.55	0.29	84.16	76.34	
13	1	0	− 1	78.79	2.61	0.50	82.55	66.02	
14	1	− 1	0	98.50	2.79	1.11	101.17	82.11	
15	− 1	− 1	0	94.00	2.51	1.36	107.80	84.07	
	Symbols	Coded levels				
Variables				− 1	0	1				
Ultra-sonic temperature	X1			30	55	80				
Ultra-sonic Time	X2			5	10	15				
Ethanol to water ratio (%)	X3			25	50	75				

Total Phenolic Content (TPC)

The total phenolic content was determined using a modified colorimetric assay with Folin-Ciocalteu reagent, as described by Slinkard and Singleton40. A 10 µl aliquot of the prepared extracts was mixed with 90 µl of deionized water and 600 µl of Folin-Ciocalteu reagent (10%) and allowed to react for 10 min. Then, 480 µl of sodium carbonate (7.5%) was added, and the mixture was kept in the dark for 30 min. The absorbance of the resulting solution was measured at a wavelength of 760 nm using a spectrophotometer (Unico 2100UV Single Beam UV/Vis, Shanghai, China). Deionized water and Gallic acid (GAE) were used as blank and standard, respectively. The calibration curves were plotted based on Gallic acid, and the results were expressed as milligrams of Gallic acid equivalents per gram of dry weight (mg GAE g−1 DW).

Total flavonoid content (TFC)

The total flavonoid content was determined using a colorimetric method with aluminum chloride, as described by Modareskia et al.41. A 100 µl aliquot of the extract was mixed with 2 ml of deionized water in a 15-ml tube, followed by the addition of 150 µl of 5% sodium nitrite solution. The mixture was allowed to react for 5 min at room temperature before adding 300 µl of 10% aluminum chloride (AlCl3·6H2O). After 6 min, 1 ml of 1 M sodium hydroxide was added, and then distilled water was added to reach a final volume of 5 ml. The mixture was then vortexed thoroughly and the absorbance was immediately recorded at 380 nm using a spectrophotometer (Unico 2100UV Single Beam UV/Vis, Shanghai, China). The flavonoid content was expressed as mg of quercetin equivalents per gram of dry weight (mg Qu g−1 DW).

Total anthocyanins content (TAC)

The total anthocyanin content of extracts was estimated using the pH differential method42. The absorptions were determined in a spectrophotometer (Unico 2100UV Single Beam UV/Vis, Shanghai, China) at 530 and 700 nm in buffers at pH 1.0 and 4.5, using the equation of A = [(A530 − A700) pH1.0 − (A530 − A700) pH4.5]. Total anthocyanin content was calculated as mg cyanidin-3-glucoside equivalents per one g of dry weight (mg Cy-g.g−1 DW) by the following Eq. 43:1 TAC=A×MW×V×DF×100ε×100

A = absorbance, MW = molecular weight (449.2 g/mol), DF = dilution factor, ε = molar absorbance coefficient (26,900 L/mol/cm).

Ferric reducing antioxidant power (FRAP) assay

The FRAP method established by Žugić et al.44 was employed to determine the antioxidant activity of the extracts. A 50 µl of the prepared extracts were mixed with a FRAP reagent consisting of 25 ml of 300 mM acetate buffer, 2.5 ml of 10 mM TPTZ in 40 mM HCl, and 2.5 ml of 20 mM FeCl3·6H2O. The mixtures were shaken and incubated at 37 °C for 5 min before measuring the absorbance at 593 nm using a UV–VIS spectrophotometer (Unico 2100UV Single Beam UV/Vis, Shanghai, China). The concentration of Fe2+-TPTZ (reducing capacity) was calculated by comparing the absorbance at 593 nm with the standard curve of the Fe(II) solutions (ferrous sulfate heptahydrate).

DPPH free radical scavenging activity

The free radical scavenging activity of the extracts was assessed using a modified version of the method described by Nakajima et al.45. In brief, 10 µl of the prepared extracts were mixed with 2 ml of a solution of DPPH (1,1-diphenyl-2-picrylhydrazyl) in methanol, which contained 95% free radicals. The mixture was shaken and incubated for 30 min in the dark at room temperature before measuring the absorbance. A UV–VIS spectrophotometer (Unico 2100UV Single Beam UV/Vis, Shanghai, China) was used to monitor the decrease in absorbance at 517 nm against a control. The scavenging capacity was determined using a colorimetric assay, and the percent DPPH scavenging effect was calculated using the following formula:2 DPPHsc \%=Abscontrolt=30min-Abssamplet=30minAbscontrolt=30min×100

Abs sample indicates the absorbance of DPPH solution with-, and Abs control shows the absorbance of DPPH solution without extracts at 517 nm.

RSM

In this study, a Box-Behnken design (BBD) with three center points was employed to investigate the effects of three independent variables on five dependent responses. The independent variables were ultrasonic temperature (X1) at three levels (30, 55, and 80 °C), ultrasonic time (X2) at three levels (5, 10, and 15 min), and ethanol-to-water ratio (X3) at three levels (25, 50, and 75%). The complete design comprised 15 experimental runs with three replications of the center points (all factors at level 0) (Table 2). The independent variables were transformed into three levels (− 1, 0, 1), and the responses of TPC, TFC, TAC, FRAP, and DPPHsc% were fitted to a quadratic polynomial model.3 Yn=b0+∑i=13biXi+∑i=13biiXij+∑i≠j=13bijXiXj

where TPC, TFC, FRAP, DPPHsc%, and TAC stand (Yn) for the predicted responses of factors of X1–X3; b0 is the constant coefficient; b1, b2, and b3 are the linear coefficients; b11, b22, and b33 are the quadratic coefficients; and b12, b13, and b23 are the cross-product coefficients. The accuracy of the estimated coefficient was analyzed by the ANOVA method and the model accuracy was obtained using R2 and the F test at 1 and 5%.

Statistical analysis

In the present study, data analyses were conducted using SAS Version 9.4 statistical software. Cluster analysis and correlation analysis were performed using RStudio (version 1.2.5019), available at http://www.rstudio.com/. Additionally, principal component analysis (PCA) was executed using Minitab 16.2.4 software. The three-dimensional and contour plots associated with response surface methodology (RSM) were generated using Design Expert software version 10.

Results and discussion

Selecting the superior genotype

The main goal of the present study was the collection of some genotypes of R. ribes and the selection of the best genotype for the optimization process by RSM. The scheme of work is presented in Fig. 1. The phytochemical and biological features of plants are influenced by environmental and genetic factors46.Fig. 1 The schematic design of stages of the current study including collection, selection, and optimization using the Response Surface Methodology.

In the present work, the phytochemical analysis of flowers and stems of R. ribes was investigated in thirteen collected genotypes (G1–G13) from different regions of Iran. The amounts of TPC, TFC, and antioxidant activity (FRAP reducing power) of genotypes are shown in Table 1. The results of the analysis of variance showed that there was a significant difference in all the examined characteristics (TPC, TFC, and FRAP) among the genotypes and organs (P < 0.01). The flower TPC among the genotypes varied from 9.80 to 81.53 mg GAE g−1 DW. In stem organs, TPC varied from 2.87 to 16.33 mg GAE g−1 DW. Also, the TFC in flower and stem organs ranged from 0.33 to 1.32 mg Qu g−1 DW and 0.05 to 0.38 mg Qu g−1 DW, respectively. In G8, the flower organ antioxidant activity was about four times more than in other genotypes (Table 1). A high and significant correlation was observed between total phenol, total flavonoid, and antioxidant activity (Fig. 2a). Secondary metabolites like phenols and flavonoids are parameters related to antioxidant activity47. Similarly, a positive correlation between TPC and TFC and antioxidant activity has been reported in rhubarb48. Many studies have shown that rhubarb is among plants with a high amount of phenols and flavonoids49–51. It seems that the differences in total phenolic, flavonoid, and anthocyanin content among genotypes may be attributed to genetic and environmental variations. As previously reported in earlier studies, secondary metabolites like TPC and TFC are not only affected by environmental conditions such as temperature, altitude, rainfall, light, and soil parameters but also genetic factors like gene expression and enzyme accumulation and activity like PAL: Phenylalanine ammonia-lyase and TAL: L-Tyrosine ammonia-lyase is also involved52,53. The interaction of these two factors contributes to the final amount of TPC, TFC, and antioxidant activity. Accordingly, we selected the flower organ of the G8 sample as the elite organ and genotype for further optimization using response surface methodology (RSM).Fig. 2 Correlation and multivariate analysis among thirteen studied genotypes of R. ribes based on three phytochemical properties of TPC: Total phenolic content, TFC: Total flavonoid content, and FRAP: ferric reducing antioxidant power (a), correlation (b), circular hierarchical cluster analysis (HCA) with Ward’s method-based (c) Scatter plot of genotypes drawn based on two first principal components.

Principal component-(PCA) and Hierarchical cluster analysis (HCA)

In Fig. 2b, three phytochemical traits and 26 samples (13 Genotypes × 2 organs) were subjected to HCA. All samples were categorized into five groups with different colors (Fig. 2b). Group I contained G2F, G1S, G12S, G13F, G13S, G8S, G11F, G4F, G11S, G5S and G3S, while group II included G4S, G2S, G12S, G9S, G7S, G6S, G10S, group III contained G9F, G5F and G1F, in group IV involved G8F and finally group V included G6F, G3F, G10F, G12F, G7F. TPC, TFC, and high DPPH scavenger activity were the highest in group IV (G8F). Different plant sources from various environmental origins have led to the choice of the elite genotype for further optimization.

In the principal component analysis (PCA) of all the traits evaluated in the populations, PC1 had 91.4% and PC2 had 6.3% of the variance (Fig. 2c). This analysis determined the main component and correlation of traits, and their relationship to populations. Traits with positive arrows showed a positive correlation, whereas two traits with undirected arrows showed a negative correlation. G8F demonstrates a strong correlation with phytochemical indices of TPC, TFC, and DPPH free radical scavenger activity. The result of HCA and PCA confirmed the superiority of G8F similar to the results of ANOVA analysis.

Response surface methodology (RSM)

Nowadays, R. ribes are consumed as a vegetable in most parts of the world, namely Iran, Turkey, China, Korea, and Western countries54. The presence of functional antioxidant metabolites like anthraquinones and flavonoids in its organs has caused it to attract the attention of the food industry and traditional medicine18,49. In the present study, the stem and flower of the genotypes were investigated and the results showed that they have different TPC, TFC, and antioxidant activity (FRAP). Although many studies have been done on different organs of rhubarb50,55. However, no studies have been conducted on rhubarb flowers, which possess higher phenolic content and antioxidant properties. One of the initial priorities of natural antioxidant-containing resources is their rapid extraction with available and safe solvents29. To achieve this aim, ethanol, and water was used as green and available solvents56. Behnken Box Design (BBD) with three central points through RSM methodology was used to achieve optimal extraction of the flower. The experimental and prediction design and responses for RSM were performed in three replications. The levels of independent variables (X1–X3) and obtained results are given in Table 2. In the present study, extracts were evaluated to optimize TPC, TFC, TAC, alongside their antioxidant properties using two methods: FRAP and DPPH (%). The correlation analysis of raw data from the experimental runs (Table 2) indicated a strong positive correlation between the evaluated TPC and both FRAP (+ 0.87) and DPPH (+ 0.58). This suggests a significant influence of total phenolic compounds on antioxidant effects; however, the FRAP method proved to be more effective than DPPH in assessing antioxidant activity in this plant. Additionally, a relatively high and significant correlation (+ 0.61) between FRAP and DPPH% indicates that both antioxidant methods succeeded in estimating antioxidant properties.

Fitting the RSM models

Table 3 shows the results of ANOVA and error for the RSM models of the five evaluated responses. The models accurately predicted these responses, with statistical significance (P < 0.01). In RSM, a lack-of-fit p-value greater than 0.05 indicates that the model fits well (Table 3). Furthermore, the regression models (R2 and R2-adj) for TPC, TFC, TAC, FRAP, and DPPHsc% with satisfactory coefficients of multiple determinations (TPC = R2 > 0.99 and R2-adj > 0.97; TFC = R2 > 0.97 and R2-adj > 0.91; TAC = R2 > 0.99 and R2-adj > 0.97; FRAP = R2 > 0.97 and R2-adj > 0.92; DPPHsc% = R2 > 0.94 and R2-adj > 0.85) showed the alias fitness of the RSM models in the present study (Table 3). The RSM models based on the experimental data for each of the responses are presented according to the following equations:4 YTPC=83.6667+3.4643X2+18.1250X3-7.2500X2X3+24.7262X12+6.3333X22-22.7024X32

5 YTFC=3.084517+0.209583 X2+0.641250X3-0.198750X2X3-0.638475X22-0.584308X32

6 YTAC=2.70567+0.96116 X3+1.32114 X1X3-0.80727X12-2.20565X22-1.35479X32

7 YFRAP=91.1479+13.3405 X3+9.9382 X1X2-12.0870X2X3+17.6801X12+8.3686X22-24.4006X32

8 Y%DPPH=77.63176+7.31707 X3+7.35772 X1X3-1.42276 X2X3+12.25442X22-7.07079X32

where Y is the predicting responses and X1, X2, and X3 are temperature, time, and different ethanol-to-water ratios, respectively.Table 3 Assess the suitability of a second-order polynomial model fit for the experimental data, analysis of variance (ANOVA) and regression coefficients were estimated for TPC, TFC, TAC, DPPHsc%, and FRAP values.

	Regression Coefficients (β)	
	TPC	TFC	TAC	FRAP	DPPH	
Intercept, X0	83.67***	3.12***	2.71***	91.48**	77.63*	
Linear	
 X1	0.29	0.069	− 0.099	0.40	− 1.09	
 X2	1.73*	0.13*	− 0.12	0.71	2.15	
 X3	9.06***	0.28***	0.38***	5.72**	4.41**	
Quadratic	
 X1X2	− 2.16	− 0.05	− 0.02	4.97*	0.85	
 X1X3	− 1.90	− 0.007	0.87***	− 0.31	3.68*	
 X2X3	− 3.63*	− 0.19*	0.10	− 4.14*	− 4.21*	
Cross product	
 X1X1	12.36***	0.001	− 0.30**	7.72**	3.02	
 X2X2	3.17*	− 0.27**	− 1.21***	4.97*	5.25*	
 X3X3	− 11.35***	− 0.28**	− 0.57***	− 11.42***	− 6.16**	
R2	0.99	0.97	0.99	0.97	0.94	
F value (model)	60.87***	18.06***	69.40***	21.01**	10.12*	
F value (lack of fit)	16.94	2.88	11.12	3.89	17.60	
Ultra-sonic temperature X1, Ultra-sonic Time X2, Ethanol to water ratio (%) X3.

*, **, *** are shown significant at P ≤ 0.05, P ≤ 0.01, P ≤ 0.001 respectively.

Impact of extraction conditions on the TPC based on RSM

To visualize the effects of process variables (X1 to X3) on the yields of total phenolic content (TPC) based on their interaction, 2D and 3D contour plots were generated (Fig. 3a–c). In each plot, the maximum yield is indicated by the optimum point of two independent factors on any response content. As shown in Fig. 3a–c, the TPC increased with increasing ultrasonic time and temperature. The highest yield of phenol (104.21 mg GAE g−1 DW) was obtained at 30 °C, 15 min, and a 50% ethanol-to-water ratio. According to Guo et al.57, increasing the temperature can improve the extraction efficiency of phenolic compounds in plant materials. The interaction between time and temperature revealed that although high temperatures significantly influenced the extraction of phenolic compounds, at lower temperatures, an increase in extraction time up to 15 min resulted in an increase in total phenolic content. However, it has been reported that at elevated temperatures, there is a risk of changes and alterations in phenolic compounds. Furthermore, the interaction between time and the ethanol-to-water (ET/W) ratio showed that with an increase in ET/W across all temperatures, particularly at 30 °C and 80 °C, there was a notable increase, indicating the significant importance of the ethanol-to-water ratio. Additionally, the interaction between ET/W and time demonstrated that regardless of the extraction duration, an increase in the ethanol-to-water ratio enhances the extraction efficiency of phenolic compounds. High temperatures can soften plant tissue, disrupt interactions between phenolic compounds and other compounds, and increase solubility. The formation of cavitation bubbles due to the reduction of surface tension of the solvent by increasing temperature and vapor pressure can also contribute to the increased efficiency of phenolic compound extraction58. However, extraction at low temperatures may require a longer extraction time to achieve a desirable yield. Ultrasonic treatments can enhance solvent penetration into plant tissue and increase the efficiency of extraction, as reported in many studies59,60. The increase in the extraction of phenolic and flavonoid compounds with sonication time can be attributed to cavitation, which is due to sound waves propagating in the solid–liquid phase, leading to the formation of contraction and expansion cycles. Other effects, such as emulsification, diffusion, and tissue damage, can also contribute to the desired extraction of raw materials. However, at higher temperatures, the amount of extraction may be reduced due to oxidation that can occur when exposed to ultrasound waves61.Fig. 3 Contour plot and three-dimensional (3D) response surface plots of the interactions of three independent variables (X1 to X3) on Total Phenol Content (TPC) (a–c); X1 (ultrasonic temperature (°C)), X2 (ultrasonic time (min)), and X3 (ethanol to water (%)).

Impact of extraction conditions on the TFC based on RSM

Figure 4a–c illustrates the 3D surface and 2D contour plots of the total flavonoid content (TFC), which are based on the effects of different variables including temperature (X1), ultrasonic time (X2), and ethanol-to-water ratio (X3). The interaction between temperature and time showed that the TFC increased until approximately 11 min, and from 11 to 15 min, there was no significant effect on the increase of total flavonoids. It is worth noting that the extraction of flavonoid compounds occurs more efficiently at higher temperatures within shorter time frames. Additionally, the interaction between temperature and the ET/W ratio revealed that with increasing ethanol concentration up to 65%, the extraction performance improved across all assessed temperatures, after which no significant changes were observed. However, at higher temperatures with lower ethanol-to-water ratios, the extraction efficiency increased. The influence of time and ET/W on total flavonoid content was additive, meaning that an increase in time to between 9 and 11 min along with an ethanol-to-water ratio increase to 65% resulted in higher yields. Beyond this point, a decreasing trend was observed. At temperatures above 70 °C, the maximum extraction capacity of total flavonoids is achieved in shorter time frames, approximately around 7 min. This indicates that higher temperatures enhance the efficiency of flavonoid extraction, allowing for effective results in less time. The amount of TFC in optimized conditions was 3.16 mg Qu g−1 DW, which increased by 58.22% compared to the normal conditions of extraction, i.e. studying the diversity of genotypes. Because of the short time needed for ultrasonic-assisted extraction, the flavonoid compounds yield increased in higher temperatures. It has been reported that very high-temperature caused to degradation of flavonoids such as anthocyanins and flavan-3-ol compounds due to sensitivity, therefore the temperature should not be increased indefinitely62. Similar to the results of the present study, Wang et al.60 Reported that under ultrasonic-assisted solvent extraction, increasing the time and temperature to a specified level, the extraction efficiency of flavonoids was increased. Pandey et al.58 also reported that the container diameter and ultrasonic temperature are two important factors in the extraction of flavonoid compounds, and increasing the temperature significantly increases the extraction efficiency.Fig. 4 Contour- and 3D response surface plots of the interactions of three independent variables (X1 to X3) on Total Flavonoid Content (TFC) (a–c); X1 (ultrasonic temperature (°C)), X2 (ultrasonic time (min)), and X3 (ethanol to water (%)).

Optimization of extraction parameters for total anthocyanin content (TAC)

The extraction of anthocyanin compounds from rhubarb was influenced by X1–X3. 3-D surface and 2-D contour plots for TAC based on the interaction between variables X1–X3 are shown in Fig. 5a–c. Three-dimensional surface plots for TAC in the interaction of X1 and X2 are presented in Fig. 5a. As temperatures enhance, anthocyanin extraction efficiency increases until an extraction time of 10 min (X1 and X3) and (X2 and X3) interaction is also shown in Fig. 5b and c respectively. Based on the obtained results, the maximum extraction of anthocyanins was observed at a temperature of 80 °C, time of 10 min, and ethanol to water ratio of 75%. In other words, at a higher temperature, the extraction rate of the anthocyanins is maximized. Based on interaction of X1–X2, The optimal temperature for the extraction of TAC is approximately 50 °C, with an ideal extraction time ranging from 9 to 11 min. Furthermore, the highest TAC was achieved at the maximum combinations of the ethanol-to-water (ET/W) ratio and temperature, specifically at 75% ethanol and 80 °C. Additionally, the interaction between ET/W and time revealed that the highest TAC was observed with an ET/W ratio between 55 and 60% and an extraction time of 9–11 min. It has been reported that in high temperatures there is an increase in the amount of extractable extracellular anthocyanins60. Also, in the higher ethanol-to-water ratio, the efficiency of extraction of anthocyanins has increased similar to previous reports60,63.Fig. 5 Contour- and 3D response surface plots of the interactions of three independent variables (X1 to X3) on Total Anthocyanins Content (TAC) (a–c); X1 (ultrasonic temperature (°C)), X2 (ultrasonic time (min)), and X3 (ethanol to water (%)).

Optimization of extraction factors for antioxidant activity based on (FRAP) assay

According to the FRAP assay, the antioxidant activity was evaluated based on the reduction rate of Fe3+ (ferric iron) to Fe2+ (ferrous iron). The effect of ultrasonic temperature (X1), time (X2), and ethanol-to-water ratio (X3) factors on the antioxidant activity of rhubarb flowers is shown in Fig. 6a–c. It was observed that the maximum reducing power was recorded at 80 °C and 15 min. Also, by increasing the ethanol-to-water ratio to 50 percent, antioxidant activity showed an upward trend. Sharmila et al.64 reported that they obtained the highest antioxidant levels in the ethanol to water meddle range (60%), which was consistent with our results. Similar results regarding the ethanol-to-water ratio influencing FRAP activity have also been reported in the extraction of olive leaves29. Based on the X1 and X2 interaction, by increasing the temperature and time of ultrasonic, high extraction efficiency was achieved in antioxidant (FRAP) activity (Fig. 6a). (X1 and X3) and (X2 and X3) interaction is also shown in Fig. 6b and c respectively.Fig. 6 Contour- and 3D response surface plots of the interactions of three independent variables (X1 to X3) on ferric reducing antioxidant power (FRAP) (a-c); X1 (ultrasonic temperature (°C)), X2 (ultrasonic time (min)), and X3 (ethanol to water (%)).

Optimization of extraction factors for antioxidant activity based on (DPPH) assay

DPPH free radical scavenging is a widely used tool for estimating the antioxidant activity of plant extracts65. Based on the DPPH assay, the antioxidants were able to reduce the purple-stable DPPH to the yellow-colored diphenylpricryhydrazine. According to the interaction between variables X1–X3 3D surface and contour plots for the DPPH scavenging assay are shown in Fig. 7a–c, the highest antioxidant activity was obtained in 15 min, 30 °C of temperature, and a 50% ethanol to water ratio. Accordingly, the low temperature prevents the degradation of compounds and decrement of antioxidant activity, and the expected result was as reported by Jaafar et al.66. In the mentioned optimal condition DPPHsc was 88%. Increasing the ethanol-to-water modification of proteins and polyphenols' nature reduces the eventual dissolution67–69.Fig. 7 Contour- and 3D response surface plots of the interactions of three independent variables (X1 to X3) on DPPH free radical scavenging (a–c); X1 (ultrasonic temperature (°C)), X2 (ultrasonic time (min)), and X3 (ethanol to water (%)).

In assessing the antioxidant effects measured by DPPH% and FRAP, it was observed that the response trends of both methods to variations in the three factors were notably similar. This similarity allows us to confidently regard the commonalities between the two methods as indicative of a parallel pattern. Specifically, the interaction between extraction time and temperature in the DPPH method demonstrated a consistent increase in antioxidant activity with prolonged extraction time, independent of temperature conditions. In contrast, the FRAP method revealed a marked enhancement in antioxidant activity at elevated temperatures when combined with extended extraction times. Thus, given the alignment of results observed in the DPPH analysis, it can be concluded that higher temperatures and longer extraction durations are optimal for maximizing antioxidant activity. Furthermore, the interactions between the ethanol-to-water (ET/W) ratio and both time and temperature highlighted that increasing the ET/W ratio significantly improved extraction performance for both DPPH and FRAP methods within the established optimal parameters. Overall, the findings suggest that elevating the ethanol-to-water ratio enhances the extraction of phenolic compounds and flavonoids, thereby increasing antioxidant activity. Additionally, applying higher temperatures alongside a greater ethanol-to-water ratio at fixed extraction times further contributes to enhanced antioxidant efficacy(Figs. 6a–c and 7a–c).

Simultaneous optimization of valuables for responses

The main advantage of RSM is the simultaneous optimization of responses according to dependent variables. Based on RSM models the highest desirability value of evaluated responses was obtained at 80 °C of temperatures; for 15 min and 53.14% of ethanol-to-water ratio (Fig. 8). The desirability values exceeded 0.812 under the optimal conditions. The corresponding values under optimum conditions for TPC, TFC, TAC, and antioxidant assay DPPH and FRAP were 99.32 (mg GAE g−1 DW), 3 (mg Qu g−1 DW), 1.12 (mg Cy-g g−1 DW), FRAP (110.22 µm Fe+2g−1 DW) and 88.20 (DPPHsc %), respectively. To confirm of obtained results, three replicated evaluations were done under optimal conditions. The yield values for TPC, TFC, TAC, FRAP, and DPPHsc % were found to be 99.34 ± 0.18, 3.11 ± 0.09, 1.13 ± 0.11, 111.18 ± 1.39, and 87.95 ± 0.28, respectively. These results confirm the model's fitness with the experimental data. The interaction between variables X1, X2, and X3 is illustrated in Fig. 9a–c. Figure 9a shows the interaction of X1 and X2 when X3 is set in the middle range. In this interaction, when the temperature is at its highest value, with the increase of time, the average value of the dependent variables increases, but when the time approaches the maximum, the values of the parameters show a decreasing trend. In the interaction between variables X1 and X3, when X2 is set in the middle range (Fig. 9b), With the increase of both variables X1 and X3, the value of the dependent variables also increases, and the highest value of the dependent variables was observed at the maximum of the variables. In the interaction between variables X2 and X3, X1 was in the middle range (Fig. 9c). With the increase of variable X3 and without the effect of variable X2, the amount of dependent variables also increases, and the highest amount of dependent variables was observed in the maximum value of X3. As variable X3 increased, without the effect of variable X2, the dependent variables also increased.Fig. 8 Profiles for predicted values and desirability of process parameters. Independent variables X1 to X3 (Temp., Time, and Et:w ratio) on responses of TPC (Total Phenol Content), TFC (Total Flavonoid Content), TAC (Total Anthocyanin Content), and antioxidant activity (DPPH and FRAP).

Fig. 9 Contour- and 3D response surface plots of the desirability amount of the five responses (TPC, total phenol content; TFC, total flavonoid content, TAC, total anthocyanins content; antioxidant activity, DPPH; and antioxidant activity, FRAP) based on the interactions of independent variables (a) ultrasonic temperature/ ultrasonic time; (B) ultrasonic temperature/ Ethanol to water; and (C) ultrasonic time/Ethanol to water.

Accuracy of RSM models

The accuracy of the model's predictions was assessed by calculating the R2 values, which represent the correlation between the experimental data and the data obtained from the RSM models. A high R2 value (> 0.948) indicates a satisfactory model fit and a strong relationship between the RSM models and the experimental data (Fig. 10a–e).Fig. 10 Plots of the square of the correlation coefficient (R2) between observed and predicted values. The responses included (a) TPC (total phenol content), (b) TFC (total flavonoid content), (c) TAC (total anthocyanins content), (d) antioxidant activity (DPPH), and e: antioxidant activity (FRAP) through RSM response surface methodology.

Conclusion

The phytochemical analysis of flowers and stems of R. ribes was conducted across thirteen genotypes (G1–G13) collected from various regions of Iran. The total phenolic content (TPC) in flowers exhibited considerable variation, ranging from 9.80 to 81.53 mg GAE g–1 DW, while in stem organs, TPC ranged from 2.87 to 16.33 mg GAE g–1 DW. Total flavonoid content (TFC) in the floral and stem tissues varied from 0.33 to 1.32 mg Qu g–1 DW and from 0.05 to 0.38 mg Qu g–1 DW, respectively. The antioxidant activity, measured as µmol Fe2+ g–1 DW, in the flowers ranged from 7.42 to 59.87, and in the stems, it varied from 0.14 to 15.99, with the highest value observed in genotype G8. Considering that the flower samples of genotype G8 exhibited superior levels of phenolic and flavonoid compounds as well as antioxidant activity compared to the other genotypes, it was selected for further optimization studies using Response Surface Methodology (RSM). The extraction of TPC, TFC, and antioxidant activity from flower of rhubarb depends on the solvent nature, extraction time, and their interaction. The results of the experiment indicated that among the TPC, TFC, and TAC, the total phenolic content exhibited the highest value and demonstrated a strong correlation with antioxidant methods. The interaction between time and temperature revealed that, although high temperatures significantly facilitated the extraction of phenolic compounds, lower temperatures with extended extraction times up to 15 min also resulted in increased total phenols. The results indicated that TFC increased significantly until approximately 11 min of extraction time. Flavonoid extraction was more effective at higher temperatures and shorter extraction times. Additionally, increasing the ET/W ratio to 65% enhanced extraction efficiency across all temperatures. However, higher temperatures favored lower ET/W ratios. The solvent type is the most crucial factor affecting the extraction of antioxidant capacity. Traditional methods like maceration take longer and require high solvent content, which is not immune to temperature and can cause the decomposition of several compounds. Ultrasonic waves cause mechanical fluctuations that enhance the solvent's penetration into cellular materials and improve the mass transfer of compounds. The solvent polarity used for extraction correlates with the recycling of polyphenols from plant material. Different solvent fractions, temperatures, and ultrasonic durations significantly affect the extraction efficiency of bioactive compounds from rhubarb. RSM helps avoid degradation and achieve the best yields of components during the extraction process. In analyzing the antioxidant effects using the DPPH% and FRAP methods, we found that both methods responded similarly to changes in the three factors (X1–X3). This consistency allowed us to draw reliable conclusions about their parallel behavior. For the DPPH method, longer extraction times resulted in increased antioxidant activity. In contrast, the FRAP method showed that higher temperatures and prolonged extraction times also enhanced antioxidant activity. Therefore, to maximize antioxidant activity, it is recommended to use longer extraction times and higher temperatures. Optimal conditions for maximum antioxidant activity, total phenol, total flavonoids, and anthocyanin simultaneous recovery were an extraction temperature of 80 °C, time of 15 min, and ethanol-to-water ratio of 53.14%. Under optimal conditions, the experimental yield of TPC, TFC, TAC, DPPHsc, and FRAP were 99.32 mg GAE.g−1 DW, 3 mg Qu g−1 DW, 1.12 Cy-g g−1 DW, 110.22 µmol Fe++/g DW, 88.20% respectively, which was highly close to the real yield values. The results of present study introduce ultrasonic extraction an applicable and cost-effective method for the food and nutraceutical industries. The study showed that ultrasonic extraction can extract valuable phenol and flavonoids with antioxidant potential, using only inexpensive and green non-hazardous solvents. Howover supplementary studies are needed to facilitate the transfer of ultrasonic as an industrial antioxidant extraction method with a recently applicable technique like micro-capsulation.

Acknowledgements

The authors wish to thank the research council of Urmia University for supporting this work.

Author contributions

G.G.: investigation, analytical software, analysis, writing—original draft, drowning the Figs. 1 and 2; M.F.: supervision, visualization, analytical software, conceptualization, Methodology, Data curation, Writing-review and editing, project administration, drowning of Figs. 3, 4, 5, 6, 7, 8, 9 and 10; A.A. advisor, methodology, writing-review and editing

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethical statement

Plant sampling were comply with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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