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

39289436
72424
10.1038/s41598-024-72424-w
Article
Color, proximate composition, bioactive compounds and antinutrient profiling of rose
Mallick Sharmila Rani sharmila@bsmrau.edu.bd

1
Hassan Jahidul jhassan@bsmrau.edu.bd

1
Hoque Md. Azizul 1
Sultana Hasina 1
Kayesh Emrul 1
Ahmed Minhaz 2
Ozaki Yukio 3
Al-Hashimi Abdulrahman 4
Siddiqui Manzer H. 4
1 https://ror.org/04tgrx733 grid.443108.a 0000 0000 8550 5526 Department of Horticulture, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706 Bangladesh
2 https://ror.org/04tgrx733 grid.443108.a 0000 0000 8550 5526 Department of Agroforestry and Environment, Faculty of Forestry and Environment, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, 1706 Bangladesh
3 https://ror.org/00p4k0j84 grid.177174.3 0000 0001 2242 4849 Laboratory of Horticultural Science, Faculty of Agriculture, Kyushu University, Fukuoka, 819-0395 Japan
4 https://ror.org/02f81g417 grid.56302.32 0000 0004 1773 5396 Department of Botany and Microbiology, College of Science, King Saud University, 11451 Riyadh, Saudi Arabia
17 9 2024
17 9 2024
2024
14 2169017 1 2024
6 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/.
Rose (Rosa sp.) is one of the most important ornamentals which is commercialize for its aesthetic values, essential oils, cosmetic, perfume, pharmaceuticals and food industries in the world. It has wide range of variations that is mostly distinguished by petal color differences which is interlinked with the phytochemicals, secondary metabolites and antinutrient properties. Here, we explored the color, bioactive compounds and antinutritional profiling and their association to sort out the most promising rose genotypes. For this purpose, we employed both quantitative and qualitative evaluation by colorimetric, spectrophotometric and visual analyses following standard protocols. The experiment was laid out in randomized complete block design (RCBD) with three replications where ten rose genotypes labelled R1, R2, R3, R4, R5, R6, R7, R8, R9 and R10 were used as plant materials. Results revealed in quantitative assessment, the maximum value of lightness, and the luminosity indicating a brightening of rose petals close to a yellow color from rose accessions R4, and R10, respectively which is further confirmed with the visually observed color of the respective rose petals. Proximate composition analyses showed that the highest amount of carotenoid and β-carotene was found in R10 rose genotype, anthocyanin and betacyanin in R7. Among the bioactive compounds, maximum tocopherol, phenolic and flavonoid content was recorded in R8, R6 and R3 while R1 showed the highest free radical scavenging potentiality with the lowest IC50 (82.60 µg/mL FW) compared to the others. Meanwhile, the enormous variation was observed among the studied rose genotypes regarding the antinutrient contents of tannin, alkaloid, saponin and phytate whereas some other antinutrient like steroids, coumarines, quinones, anthraquinone and phlobatanin were also figured out with their presence or absence following qualitative visualization strategies. Furthermore, according to the Principal Component Analysis (PCA), correlation matrix and cluster analysis, the ten rose genotypes were grouped into three clusters where, cluster-I composed of R3, R4, R5, R8, cluster-II: R9, R10 and cluster-III: R1, R2, R6, R7 where the rose genotypes under cluster III and cluster II were mostly contributed in the total variations by the studied variables. Therefore, the rose genotypes R9, R10 and R1, R2, R6, R7 might be potential valuable resources of bioactive compounds for utilization in cosmetics, food coloration, and drugs synthesis which have considerable health impact.

Keywords

Antioxidants
Antinutrient
Bioactive compounds
Color
Molar ratio
Rose petal
Secondary metabolites
Variability
Subject terms

Plant sciences
Biodiversity
King Saud UniversityRSP2024R219 Al-Hashimi Abdulrahman issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Rose is a highly significant decorative plant in the commercial floriculture business, with great economic, cultural, and symbolic value. Rose flowers are vital in the floriculture sector of Bangladesh, serving as cut flowers for various festivals and religious events, as well as being used for potted plants and garden plants. Rose blossoms possess not only aesthetic qualities but also serve as a fundamental component in the production of industrial goods. Rose water, rose oil, rose concrete, dried petals, dried buds, and rose absolute are the primary derivatives of roses. These products find applications in various industries such as cosmetics, perfume manufacturing, food production, and pharmaceuticals for drug development on a global scale1. The rose flower includes antioxidant components such as polyphenols, flavonoids, and phenolic acid, which are capable of capturing free radicals2. The antioxidant activities of rose hips3,4 and rose petals5,6 are known to be linked to their chemical makeup and phenolic compounds. The focus lies on utilizing bioactive compounds derived from nature to eliminate these free radicals. Within this particular context, the rose flower possesses the potential to serve as a valuable biological resource for enhancing nutritional attributes, in addition to its elevated levels of antioxidants. In Bangladesh, there is a variety of rose called Rosa kordesii. The petals of this rose contain high levels of antioxidants such as terpenoids, flavonoids, saponins, tannins, and phenolic compounds, which are effective in scavenging free radicals7. Furthermore, rose oil constituents possess strong antimicrobial properties that prevent bacterial infection8. Additionally, rose petal tea, which is devoid of caffeine, can alleviate moderate sore throat due to its high antioxidant content and antibacterial properties. The rose blossom can serve as an excellent resource for preparing functional food that promotes blood circulation, making it beneficial for individuals with high blood pressure. Moreover, rose petals are rich in vitamins, and when taken in an edible form, particularly vitamin C, they enhance the production of red blood cells9.

Plants create and store a significant quantity of natural bio-active chemicals and secondary metabolites, such as anthocyanins, flavonoids, phenolic acids, and carotenoids, possess significant economic and commercial value. They contribute to the smells in flowers, provide color, and serve as key components in pharmaceutical products10. Among the secondary metabolites, antioxidants have diverse and important impacts on health-related matters. During normal oxygen metabolism in the human body, reactive oxygen species (ROS) such as superoxide (O2-) and nitric oxide (NO) are naturally created as byproducts, along with highly reactive free radicals. Imbalance between the production and scavenging of reactive oxygen species (ROS) and free radicals can result in the presence of redox-active transition metal ions, such as iron (II) or copper. This imbalance leads to significant oxidative stress, causing the oxidation of cellular biomolecules such as DNA, lipids, and proteins. This process is associated with the development of chronic diseases including hyperlipidemia, hypertension, and cancer1.

The genus Rosa has a wide range of decorative plants, consisting of about 200 species and around 18,000 distinct cultivars of roses. These roses can be found in Asia, Europe, the Middle East, and North America11,12. The history of rose evolution reveals key characteristics of rose variation resulting from interspecific hybridization and polyploidization. The rose, belonging to the genus Rosa, is a highly significant flower known for its exceptional fragrance, captivating colors, and rich nutritional characteristics. In Bangladesh, around 10,000–12,000 hectares of land are dedicated to flower cultivation, with roses being the dominant variety. This plays a crucial part in the economic growth of the flourishing floriculture industry in Bangladesh13. However, there is a lack of official statistics regarding the production of rose flowers in Bangladesh. Moreover, the bioactive chemicals, which are abundant in rose flowers, are exclusively utilized as fresh flowers for events and are not employed in industrial processing. Conversely, Bangladesh imports a variety of processed rose products annually. Golden Rose is a highly sought-after brand in worldwide including Bangladesh that offers a range of processed rose products, including cosmetics and perfumes. Consumers of these processed products utilize them without being aware of their chemical composition. Meanwhile, scientists worldwide have conducted research on the chemical composition of diverse species within the genus Rosa. However, there is a limited amount of information on the heterogeneity of secondary metabolites in the available rose cultivars in Bangladesh. Therefore, this study provides a novelty approach to categorize the rose genotypes according to the diversity of secondary metabolites and their successive use potentialities. Thus, it has been postulated that the rose genotypes may exhibit diversity in their response to the color, secondary metabolites, nutritional and antinutritional profiling. The purpose of this study was to sort out the variations in color, antinutrients, and secondary metabolites among several rose genotypes. Additionally, to find the most promising rose genotypes that is rich in important secondary metabolites to use as a resource for commercial products and as a substitute for artificial food coloring agents.

Materials and methods

Chemicals

The chemical and reagents used in this study such as hydrochloric acid (37%), sulphuric acid (95–98%), sodium tungstate, manganese sulphate, sodium carbonate (99.5%), calcium nitrate (≥ 95%), potaddium iodide, folin-ciocalteu reagent, gallic acid (98.0%) acetone (99.0%), hexane, quercetin hydrate (≥ 95%), ammonium hydroxide, diethyl ether (99.5%), ethanol, methanol, ferric chloride (97%), ferric sulphate (399.88 anhydrous basis), phosphomolybdic acid (47.5%), aluminium chloride, chloroform (99.5%), 2,2-bipyridyl (99.5%), ammonium thiocyanate, tannic acid, dl-α-tocopherol acetate, potassium permanganate, DPPH (2,2-diphenyl-1-picrylhydrazyl), ascorbic acid, sodium acetate, acetic acid, n-butanol, sodium hydroxide of trace grade were purchased from Sigma Aldrich (St Louis, USA) and used to prepare working solutions as well as for laboratory analysis.

Experimental design

The experiment was conducted at the Laboratory of Horticulture, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Bangladesh using ten (10) rose genotypes those are distinct from each other with color differences viz. R1 (Rosa meidrifora), R2 (Rosa damascena), R3 (Rosa grandiflora), R4 (Rosa alba), R5 (Rosa hybrid: Crystalline' × 'Playgirl), R6 (Hybrid tea rose), R7 (Rosa Tradescant), R8 (Rosa polyantha), R9 (Rosa 'Fragrant Delight') and R10 (Rosa floribunda) as plant materials (Fig. 1). The flowers at full-bloom stage were collected from the rose garden of BSMRAU at early morning and brought to the laboratory as soon as possible. After that, the outermost, innermost and basal part of petals from each flower of an genotypes were discarded and only the middle portion of petals from each flower were used as study sample those were divided into two parts14. One part of the selected petals was rapidly frozen and stored at − 40 °C until the extraction and analysis. While the rest part of fresh flowers was used to perform colorimetric analysis, pH measurement and drying purposes. For drying, the petals were spread on plastic net bags and shade dried for 1 week at 25 ± 2 °C. Then these shade dried petals were subjected for oven drying at 80 °C for 48 h until reaching a constant weight which were used for relative moisture content determination. Then dried petals were pulverized and preserved at − 40 °C for further analysis. The study was designed following randomized complete block design (RCBD) with three replications. The petals were collected from the six flowers of each genotype and used in extraction preparation for qualitative and quantitative analyses of color, bioactive compounds and antinutrient properties. All analyses were conducted on the data of the three biological repeats, each with three technical repeats.Fig. 1 Ten rose genotypes flowers’ used for analysis of color parameters and secondary metabolites.

Quantitative analysis of proximate composition and secondary metabolites

Color

The colors of the studied rose flowers were measured using a bench-top spectrophotometer (CR-5; Konica Minolta). Nine petals from three flowers per genotypes were randomly selected, with care taken not to include petals from the outermost and innermost layers. The selected petals were then measured at their mid-point of the adaxial surface. The color change was determined as L* indicates the darkness and lightness of color and ranges from 0 to 100 (L* = 0 means black and L* = 100 means white). Color parameters a* and b* extend from − 60 to + 60 [− a* = green and + a* = red; − b* = blue and + b* = yellow]. A white standard plate was used to calibrate the spectrometer before use to ensure the accuracy of the data. The hue angle (h°) is expressed in degrees from 0° to 360° (0° = red, 90° = yellow, 180° = green, and 360° = blue)6. The hue angle and Chroma (C) were calculated by following equations15.1 h=arctanb∗a∗

2 C=a∗2+b∗2⋯

pH

pH was determined by a digital pH meter (Digital Hanna pH Meter, Hand-Held, Pocket type pH Meter). To do so, petal extract was prepared by macerating the 0.5 g of petals in 5 mL of double distilled water and stirring for 2 h. The resulting aliquots were used for pH estimation and this quantification was repeated for three times for each genotype6.

Total soluble solids (TSS) (°Brix)

Total soluble solids (TSS) of fresh rose petals were measured by hand refractometer (Model: Atago N1, Japan). Firstly, it was calibrated by placing one drop of distilled water on the prism and looking on the scale as it is showing the horizontal line between blue and white color in 0 level. Then, a drop of juice generated after squeezing 1 g of sample was placed on the prism of hand refractometer and the soluble solids content was recorded as degree Brix (°Brix) by observing the scale16.

Total carotenoids content (mg/100 g)

The total carotenoid content of the rose petals was determined according to the method described by17. Rose petals of each genotype (100 mg) was extracted overnight with 5 ml of 80% acetone and stored at 4 °C in the dark for 24 h in the air tight test tube. After that, 1 mL supernatant was taken into 1 mL glass cuvette and absorbance was read in the spectrophotometer (Model: APEL, UV–VIS Spectrophotometer, PD- 303 UV, PD 33-3-OMS-101 b, Japan) at 663, 646, 470 nm corresponding to Chl a, Chl b and carotenoids, respectively where the 80% acetone was used as blank. For quantification of total carotenoids, the following equations was applied18.3 Chl aμg/mL=12.21A663-2.81A646

where 12.21 and 2.81 are the absorbance coefficient of the 663 and 646 nm wavelength.4 Chlbμg/mL=20.13A646-5.03A663

where 20.13 and 5.03 are the absorbance coefficient of the 646 and 663 nm wavelength.5 Carotenoidμg/mL=1000A470-3.27Chla-104Chlb229

where 1000 is the absorbance coefficient of the 470 nm wavelength.

For expressing the value in mg/100 g the formula was used μgmL×V×1001000×W

where V = Volume of acetone used (mL); and W = Weight of petal sample (g).

β-carotene (mg/100 g)

For analysis β-Carotene, 1 g fresh sample was blended thoroughly by mortar pestle and mixed with 10 ml acetone: hexane (4:6) solution. This sample was centrifuged at 6000 rpm for 15 min and the filtered with Whatman no. 1 filter paper. Then the optical density of the supernatant was measured at 663 nm, 645 nm, 505 nm and 453 nm by using a spectrophotometer (Model: APEL, UV- VIS Spectrophotometer, PD- 303 UV, PD 33–3-OMS-101 b, Japan) and β-Carotene was estimated by using following formula19.6 β-carotenemg100g=0.216OD663+0.452OD453-1.22OD645-0.304OD505

where the bold figure indicates optical density;

0.216; 0.452; 1.22; 0.304 = Absorbance coefficient of the respective absorbance.

Total anthocyanin content (AOA) (mg/100g)

The total anthocyanin content was analyzed by following the methods20 with some modifications. Briefly, 1 g of fresh rose flower petals were collected and grinded. For anthocyanin extraction this petal pastes were transferred to a 5 mL extraction solution comprising methanol, 6M hydrochloric acid and water mixture (70:7:23 v/v). After that, the extract solution was incubated at 4◦C in dark for 24 h. Thereafter, 2 mL of the extracted solution was taken in centrifuge tube where 2 mL water and 2 mL chloroform were added in each of the tube and centrifuged at 5000 rpm for 15min. Then 3 mL supernatant was carried to a glass cuvette, and absorbance was measured at optical density (OD) of 530 nm by using a spectrophotometer (Model: APEL, UV- VIS Spectrophotometer, PD- 303 UV, PD 33–3-OMS-101 b, Japan). The total anthocyanin content was measurement by using the following formula-7 QAt=A530×M-1×100

where QAt = Amount of total Anthocyanin, A530 = Absorbances at 530 nm, M = Fresh weight of the material used for extraction (g).

Total betacyanin content (TBC) (mg/100g DW)

Betacyanin content was determined using methanolic extract following the procedure of21 with some modification. To do so, the petals of ten rose genotypes were sun dried and then powered through grinding machine. After that, 2 g of dried petal powder was taken in the ten separate test tubes where 15 ml methanol was added and kept in room temperature for 24 h with intermittent shaking. Then filtered through Whatman No. 1 filter paper and the filtrated extraction sample was used for total betacyanin content (TBC) estimation. For TBC estimation, firstly 15 mL filtered extract was taken in the falcon tube and centrifuged at 6000 rpm for 15min. Then 2 mL of aliquot was diluted with 8 mL distilled water and absorbance reading was taken at 538 nm using spectrophotometer (Model: APEL, UV- VIS Spectrophotometer, PD-303 UV, PD 33–3-OMS-101 b, Japan). Betacyanin content (TBC) was calculated by using the following formula-8 Betacyaninscontentmg100gofDryweight=A×MW×V×DF×100L×W×€

where A = Absorbance at 538 nm (lmax), MW = Molecular weight of betanin (550g/mol), DF = Dilution factor (1), V = Volume of extract (mL), L (path length) = 1.0 cm, W = Sample weight (g).

For betanin, € (mean molar absorptivity) = 6.5×104 L/mol cm in H2O.

Tocopherol (VitE) (mg α-tocopherol/100 g DW)

Tocopherol content of the rose petals was analyzed by following the methods described by22. 1 g of the sample was macerated in 20 ml of ethanol and filtered in a test tube. Thereafter, 1 ml of 0.2% ferric chloride ethanolic solution and 1 ml of 0.5% α -dipyridyl solution were added to 1 ml of the filtrate in a new test tube. The solution was further diluted with distilled water to 5 ml, and the absorbance was measured at 520 nm. To prepare the ∝ -tocopherol standard, 100 mg ∝ -tocopherol was taken in 100 mL absolute ethanol and four concentrations 0.2, 0.4, 0.6, 0.8 and 1.0 mg/mL was made. After that, the standard curve was drawn and the concentration of Tocopherol content (Vit. E) equivalent (mg d-alpha-tocopherol/100 g) was calculated by using the following standard curve gradient:9 Y=1.9283x-6.2896⋯

R2=0.9661.

where Y = Absorbance of samples, x = Tocopherol content (VitE).

Total antioxidant activity (IC50) (µg/mL FW)

The bioactive properties of rose like total antioxidant activity (TAA), total phenolic content (TPC) and total flavonoid content (TFC) were determined from methanolic extract of rose petals. For determination of these properties of roses the methanolic extract was prepared. Initially, 1 g of fresh rose petals sample were weighed with electronic precision balanced (Digiscales, Germany) and immersed in methanol (25 ml) in the test tube. Then, test tube was placed in a shaking water bath (JSR JSSB-50T) at 30°C for two and half h. Then the sample was centrifuged at 6000 rpm for 15 min and the supernatant was filtered with the help of funnel and Whatman filter paper (no. 42) and stored at 4°C in a refrigerator for further analysis.

Antioxidant activity of the rose petals was analyzed using DPPH radical scavenging assay (RSA). This assay is based on the measurement of the scavenging ability of antioxidants towards the stable radical. It was conducted according to the procedure of23 with some modifications. For antioxidant assay, extracts of each rose petal samples and standard ascorbic acid solution (2 mg ascorbic acid dissolved in 2.5 ml distilled water and mix thoroughly) were prepared into several concentrations of 10, 20, 40, 80, 100 and 200 μg/ml and methanol were added to make the total volume 3 ml. Then 1 ml methanolic DPPH solution (0.004 mg DPPH was added with 100ml of methanol and mixed properly) was added to every test tube and the reaction mixture was kept at dark place for 30 min. Then, the reading was recorded at 517 nm against blank (methanol) by using spectrophotometer (Model: APEL, UV- VIS Spectrophotometer, PD- 303 UV, PD 33–3-OMS-101 b, Japan). Then, the radical scavenging activity was estimated by following formula-10 %Radicalscavengingactivity=A0-A1A0×100

where A0 = Absorbance of control (3 ml methanol + 1 ml methanolic DPPH solution), A1 = Absorbance of sample.

Inhibition concentration (IC50) was used to specify antioxidant capacity and was determined from the graph that plotted % radical scavenging activity against concentration of extract for standards and the test sample. The values of IC50 used in this study were generated from the regression line graph that plotted by % radical scavenging activity of 4 concentrations of the standards (20, 40, 80, and 100 μg/ml) against 4 concentrations of each extract test sample. IC50 means the concentration of sample which can scavenge 50% of DPPH free radical in DPPH free radical scavenging assay where lower IC50 value corresponds with a higher antioxidant activity24. IC50 was calculated by using the formula as below -11 IC50=(z-b)a

where z was replaced by 50 in the above equation; value of a and b was found from regression line plotted for each sample separately.

Total phenolic content (TPC) (mg GAE/100 g, FW)

Total phenolic content (TPC) was analyzed by following the Folin-Ciocalteu procedure25. For TPC estimation, previously prepared methanol extract solution was used. For preparing stock solution, 5 ml of FC reagent was pipetted in a conical flask and added 45 ml of water. For preparing the 7.5% Na2CO3, 7.5 g of sodium carbonate was added with 100 ml of distilled water in a 100 ml conical flask. For the preparation of gallic acid standard, 100 mg gallic acid powder weighed and diluted in 100 ml of distilled water. Then different concentrations (10, 20, 40, 60, 80, 100 μg/ml) of gallic acid were measured for preparing calibration curve. For the estimation of TPC, 0.5 ml of the sample extracts were taken in a test tube. FC reagent (2.5 ml) was added into the sample and the solution was incubated for 10 min. Then 2 ml of 7.5% sodium carbonate was mixed with the solution and the resultant mixture was incubated again at 30°C for 1 h. The absorbance reading of the rose petal samples and the gallic acid standard were measured at 760 nm by using spectrophotometer (UV–VIS PD-303 UV Spectrophotometer; APEL Co.) against the methanol as blank. Standard curve was prepared using Microsoft excel using absorbance of gallic acid at the concentration of 10, 20, 40, 60, 80, 100 μg/ml. TPC readings were measured against the gallic acid standard calibration curves and expressed as mg of gallic acid equivalents per 100 g (dry weight) by following the equation as below -12 y=mx+c

where y = Absorbance of samples, x = Total phenolic content (TPC); Value of c and m was found from the regression line plotted against the standard concentrations.

Total flavonoid content (TFC) (mg QE/100 g, FW)

Aluminium chloride colorimertic method was followed for quantification of total flavonoid content28 using the previously prepared methanolic extract. For the measurement of TFC, quercetin was used to draw the standard calibration curve. For this, the stock solution of quercetin was prepared by dissolving 1 mg of quercetin in 10 ml of methanol. Then different concentrations (10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 μl) of quercetin were taken in Eppendorf tube and methanol was also added in it to make the final volume 1000 ul. After that, 100 μl of sample extract was taken into the Eppendorf tube and 400 μl of methanol was also added. Then each sample extract was separately mixed with 100 ul of 10% AlCl3 (w/v) and 100 μl of 1M sodium acetate. It was then incubated at room temperature and was kept in dark condition for 40 min followed by the measurement of absorbance at 420 nm using spectrophotometer (UV–VIS PD-303 UV Spectrophotometer; APEL Co.) where the methanol was used as blank. The expected outcomes of TFC were calculated from the quercetin standard calibration curve (10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 μl)) and expressed as mg quercetin equivalent (QE)/100 g (FW) by following the equation as below-13 Y=mx+c

where y = Absorbance of samples, x = Total flavonoid content (TFC); Value of c and m was found from regression line plotted against the standard concentrations.

Minerals (g/100g DW) and Moisture content (MC) (%)

Mineral content (Na, K, Ca, Mg, Fe) was estimated following the procedure described by27 with the help of device and method of an atomic absorption spectrophotometer (AAS). For preparing working sample, 0.5 g sample powder was taken in a 50 ml conical flask after that, 5ml of a mixture (5:1) of HNO3 and HCIO4 (Nitric perchloric acid) added and digested through a sand bath for 3–4 h. Then the digested sample mixture was filtered with Whatman no. 42 (2.5μm particle retention) filter paper and final volume was made up to the final volume of 100 ml with distilled water in a 100 ml volumetric flask. For minerals quantification, 10 ml sample extract was shifted to 50 ml volumetric flask and final volume made 50 ml with distilled water. Afterwards, the intensity of Na, K, Ca, Mg and Fe was estimated through AAS (atomic absorption spectrophotometer; model-PinAAcle 900H; PerkinElmer). The following formula was used to quantify the concentration of minerals in rose petals.14 %Mineral=samplereading×Finalvolume×DilutionfactorSampleweight

The fresh rose flower petals were used for measurement of water content. Initially, the fresh weight of the sample recorded and thereafter, dried to a constant mass in an oven at a temperature of 100°C for 48 h. Then final weight was recorded and percent (%) moisture estimated on the basis of fresh and dry masses of rose petals in g by using the following equation28.15 %Moisture=Initialweightg-Finalweight(g)InitialWeight(g)×100

Alkaloid content (ALK) (g/100g)

Alkaloid content of rose flowers was measured according to the methods described by29. 0.5 g of the power sample was mixed with 200 ml of 10% acetic acid in ethanol. The mixture was covered with aluminium foil and incubated at room temperature for 4 h. After that, the mixture was filtered, and concentrated to about 1/4 of its original volume in a water bath. Thereafter, concentrated ammonium hydroxide was added drop by drop to the extract until complete precipitation was occurred. Then, the solution was allowed to stable, and the precipitate formed was washed with dilute ammonium hydroxide and then again filtered. The residue was oven dried at 40 °C and weighed, and the alkaloid content was measured as:16 %Alkaloid=Finalweightof sampleInitialweightofsample×100

Phytate content (PHT) (g/100g)

The protocol described by30 was used for estimation of Phytate content of rose genotypes. Briefly, 2 g of the powder sample was soaked in 100 ml of 2% HCL for 3 h and filtered with Whatman no 1 filter paper. 25 ml of the filtrate was thereafter transferred into another conical flask and 5 ml of 0.3% ammonium thiocyanate solution along with 53.3 ml of distilled water was added to the filtrate. The solution was titrated against standard ferric chloride solution (0.001 95 g of iron per mL) until a reddish-brown color appearance which persisted for 5 min was noticed. Phytate content was calculated by the following ways:17 %Phytate=Tirervalue×0.00195×1.19×100

Saponin content (SPN) (g/100g)

The saponin content in rose petals was quantified by following the method described by31. For this, 0.5 g of the powder sample was measured into a conical flask containing 50 ml of 20% ethanol. The solution was heated in a hot water bath for 4 h at 55 °C and filtered and the filtrate preserved in a test tube, after that the residue was re-extracted again with 50 ml of 20% ethanol. Then both filtrates were mixed together and kept on the hot water bath at 90 °C until it concentrated to 20 ml. The obtained solution was transferred into a 250 ml separating funnel containing 20 ml of diethyl ether. The aqueous layer was collected; 20 ml of n-butanol was added to it and then washed thrice with 10 ml of 5% sodium chloride meanwhile the ether layer was discarded. The mixture was oven dried at 40 °C to constant weight, and the percentage saponin content of the sample was calculated as:18 %Saponin=WeightoffinalfiltrateWeightofsample×100

Tannin content (TNN) (mg TAE/100g)

Folin–Denis method was followed to estimate the Tannin contents of the rose flower samples32. For preparation of Folin–Denis reagent, 100 g of sodium tungstate (Na2WO4·2H2O) and 20 g of phosphomolybdic acid was dissolved to 750 ml of distilled water into which 50 ml of 85% phosphoric acid (H3 PO4) was added. After that, the mixture was refluxed for 2 h and then cooled to 25 °C and diluted to 1000 mL by adding distilled water. This solution stored at 4 °C and used for further analysis. Accurately weighed 0.5 g of the powdered sample was transferred to a 250 mL conical flask and 75 mL of water was added into it. The flask was gently heated and boiled for 30 min, centrifuged at 2000 rpm for 20 min and the supernatant collected in 100 mL volumetric flask and the volume made up of 100 mL with distilled water. Next, 1mL of the sample extract was transferred to a 100 mL volumetric flask containing 75 mL water. 5 mL of Folin–Denis’s reagent, 10 mL of sodium carbonate solution (35 g of anhydrous sodium carbonate was added to 100 mL of water and dissolved at 70–80 °C and then cooled it which turned into a clear liquid before use) were mixed and diluted to 100 mL with distilled water and preserved for 30 min. Then absorbance reading was taken at 700 nm with spectrophotometer (Model: APEL, UV- VIS Spectrophotometer, PD-303 UV, PD 33-3-OMS-101 b, Japan). The tannin concentration was determined by the standard graph of tannic acid solution at the concentration of 0–100 mg/mL. The concentration of Tannin content was calculated by using the following equation:18 Y=0.0051x+0.0789,

R2=0.9638

Antinutrient to mineral molar ratios

Molar ratios of antinutrient/minerals were determined for predicting minerals bioavailability of rose genotypes. Thee molar ratios were calculated by using the following formula33.19 Antinutrient:MineralMolarRatio=Conc.ofantrinutrientmg100g/MolarmassofantrinutrientgmolConc.ofmineralmg100g/Molarmassofmineralgmol

where the molar mass of Phytate- 660 g/mol; Tannin- 636.5 g/mol; K (Potassium)-39.0983g/mol; Ca (Calcium)- 40 g/mol; Mg (Magnesium)- 24.31 g/mol and Fe (Iron)- 56g/mol.

The suggested critical values used to predict the bioavailability were calcium : phytate < 6, phytate : iron < 1, and phytate : calcium < 0.24.

Antinutritional phytochemical screening through visualize the color change of extract solution

The analytical observation was done to notify the presence and identification of bioactive compounds in the methanolic extracts of rose petals powder following the standard procedures as reported in the previous report of34. For testing the presence of steroids, coumarins, quinones, anthraquinones and phlobatanins, 2 mL of methanolic extracts were taken every time. After that, methanolic extracts were added to equal quantity of chloroform and 0.5ml of concentrated sulfuric acid was also added drop by drop and formation of brown ring confirmed the presence of phytosteroids. Addition of equal volume of 10% NaOH solution and development of yellow color solution indicates the presence of coumarin in the rose genotypes and addition of 1 mL 2% HCl and development of red color precipitates indicates the presence of anthraquinones. Again, treating the methanolic extract with 1.5 mL of conc. H2SO4 and the formation of red to blue color confirmed the presence of quinones whereas addition of 0.5mL of 10% ammonia and formation of pink color precipitates indicates the presence of phlobatanin.

Statistical analyses

All the recorded data on flower color parameters, secondary metabolites and antinutrient properties represent the mean values of three technical replications and were subjected to compare by two-way analysis of variance (ANOVA). The mean separation was done following least significant difference (LSD) test at 5% level of significance (P < 0.05). Furthermore, correlation matrix, cluster analysis, were performed to note the interrelationship among the studied variables and the rose genotypes of the study. Afterwards, principal component analysis (PCA) was performed to show the patterns of all the measured correlated color parameters, secondary metabolites and minerals in the reduced dimensions of newly obtained factors those were denoted as- Dim1 (PC1), Dim2 (PC2). The dendrogram cluster analysis was performed to sort out the most promising rose genotype according to the factor loadings and the contributions of each of the studied dependent variables using different packages (agricolae, facatominer, factoextra, ggplot2, corrplot) of R program (version 4.1.2). All data were reported as the mean value of three determinations ± standard deviation (SD).

Results

Color

The CIELAB system, established by the International Commission on Illumination, was employed to analyze the color of different rose genotypes as indicated in Table 1. The system utilizes L* to quantify the lightness of the color, ranging from white to black. Additionally, a* and b* indicate distinct color directions, with a* ranging from green to red and b* ranging from blue to yellow. Lastly, c* is used to measure the chroma of the color. Table 1 shows that the R4 genotype had the lightest L* value (79.16), which was similar to the R10 genotype (76.06) for white and yellow flowers, respectively. The R3 genotype had a L* value of 63.55, followed by R8 with a value of 61.08. The darkest L* value of 18.99 was observed in the R7 genotype, corresponding to blackish red roses. In terms of other color coordinates, the a* value varied from 48.49 to -3.66. The highest value of 48.49 was observed in R6, which represents red color flowers. This value was similar to R2 (46.90, hot pink color) and R1 (44.85, orange color), but significantly different from the other rose genotypes. The lowest value of -3.66, indicating white color flowers, was recorded in the R4 genotype. In addition, the values of b* for the other color directions varied from positive to negative, ranging from 60.13 to − 3.66. This corresponds to the color spectrum from yellow to purple in a flower. The highest b* value, 60.13, was obtained from R10, which showed a statistically significant difference compared to all other rose genotypes. Following R10, R9 had a b* value of 26.07, while R5 had the lowest value of -3.67. The rose genotype R10 exhibited the maximum color saturation, with a C* value of 60.19, which was statistically distinct from the other rose genotypes. Nevertheless, R6 exhibited the second highest value (50.68), which was statistically comparable to R1 (47.77) and R2 (46.91), while R4 had the lowest vividness of color (13.35). However, the decrease in values of a* and the increase in values of b* are associated with the perception of darkness and lightness. Similarly, the highest luminosity (h°) was observed at R10 (87.56), indicating a brightening of rose petals close to a yellow color. This was followed by R9 (47.13), R7 (19.89), R1 (19.19), and R6 (16.86). The last three values were statistically similar to each other but different from the rest. Therefore, the values of the parameters accurately depicted the color patterns of the flower, aligning with the visual observations of the rose genotypes' blossom color.Table 1 Colorimetric parameters of 10 rose genotypes.

Rose genotypes	Flower color		Color parametersx	
L*	a*	b*	C*	h°	
R1	Orange	42.00 ± 5.89 dy	44.85 ± 1.61 a	15.86 ± 6.11 c	47.77 ± 3.44 b	19.19 ± 6.31 c	
R2	Hot Pink	45.26 ± 0.36 d	46.90 ± 1.98 a	− 0.88 ± 0.76 h	46.91 ± 1.98 b	− 1.07 ± 0.91 de	
R3	Baby Pink	63.55 ± 2.53 b	16.72 ± 3.82 de	− 1.66 ± 1.78 i	16.84 ± 3.95 d	− 4.98 ± 5.14 e	
R4	White	79.16 ± 1.53 a	− 3.66 ± 0.27 g	12.84 ± 0.38 e	13.35 ± 0.39 d	− 74.09 ± 1.14 g	
R5	Purple	58.28 ± 2.21 c	14.34 ± 1.53 e	− 3.67 ± 0.59 j	14.80 ± 1.62 d	− 14.32 ± 0.73 f	
R6	Red	30.21 ± 2.10 e	48.49 ± 2.44 a	14.65 ± 1.34 d	50.68 ± 2.05 b	16.86 ± 2.15 c	
R7	Blackish Red	18.99 ± 0.37 f	28.54 ± 1.07 b	10.33 ± 0.56 f	30.35 ± 1.19 c	19.89 ± 0.35 c	
R8	Multicolor	61.08 ± 3.07 bc	18.39 ± 8.99 d	0.61 ± 1.42 g	18.44 ± 8.97 d	3.35 ± 4.87 d	
R9	Salmon	64.20 ± 0.32 b	24.26 ± 3.08 c	26.07 ± 1.92 b	35.73 ± 0.73 c	47.13 ± 5.72 b	
R10	Yellow	76.06 ± 0.90 a	2.56 ± 0.30 f	60.13 ± 1.20 a	60.19 ± 1.19 a	87.56 ± 0.33 a	
xColor parameters base on CIE (International Commission on Illumination) system for color representation: (L*: Lightness, a*: greenness (−) to redness (+), b*: blue (−) to yellow (+), c*: saturation of the color, h°: huge angle). ySimilar letters in each column indicate insignificant differences determined using a least Significant Difference test (P < 0.01); ± SD.

pH and TSS (°Brix)

A notable fluctuation in pH and total soluble solids (TSS) (°Brix) among the 10 rose genotypes was demonstrated in Fig. 2. The acidity of rose petals varied among different genotypes, with the lowest pH recorded in R1 (4.50), which was comparable to R7 (4.50), R6 (4.57), and R2 (4.70). On the other hand, the highest pH was observed in R4 (5.60), which was similar to R8 (5.50) and R9 (5.40), and statistically similar to R3 and R5 (5.30). Conversely, the highest total soluble solids (TSS) level was found in R1 (9.40°Brix), while the lowest was seen in R3 (7.10°Brix).Fig. 2 pH and total soluble solids (TSS) (°Brix) of rose genotypes. (Bars indicate ± SE and where statistically significant differences showed by different letters at P < 0.05.)

Total Carotenoids and β-carotene (mg/100g)

There was a significant statistical variation (P < 0.05) in the total carotenoids and β-carotene concentration noticed across ten different rose genotypes (Fig. 3). The rose genotypes R10 had the highest total carotenoids content at 108 mg/100 g fresh weight (FW). The second highest was R1 at 72 mg/100 g FW, followed by R9 at 67 mg/100 g FW. The lowest carotenoids content was found in R4 at 6 mg/100 g FW. All rose genotypes were statistically distinct from each other. From a β-carotene perspective, the highest concentration of β-carotene was found in R10 (47 mg/100 g FW), followed by R9 (42 mg/100 g FW), and R1 (39 mg/100 g FW). These values were statistically distinct from each other. Furthermore, all the remaining genotypes exhibited statistically equivalent levels of accumulation, which were below 5 mg. However, the lowest accumulation was recorded in the rose genotypes R3, with a value of 1 mg per 100 g. Notably, the rose genotypes R5 and R7 did not exhibit any detectable β-carotene content.Fig. 3 Total carotenoids (mg/100 g FW) and β-carotene (mg/100 g FW) of rose genotypes (Bars indicate ± SE and where statistically significant differences showed by different letters at P < 0.05.)

Total Anthocyanin and Betacyanin (mg/100 g)

The column graph (Fig. 4) clearly demonstrates that there were statistically significant variations in the overall anthocyanin and betacyanin content among the 10 rose genotypes. In terms of total anthocyanin content, the R7 genotype accumulated the maximum quantity of anthocyanin (196 mg/100g FW), which was comparable to R6 (191 mg/100g FW), R2 (189 mg/100g FW), and R1 (183 mg/100g FW). On the other hand, R4 accumulated the lowest amount (3 mg/100g FW). The total anthocyanin content varied between 196 and 3 mg per 100 g FW.Fig. 4 Total anthocyanin (mg/100 g FW) and betacyanin (mg/100 g DW) of rose genotypes (Different letters in bar statistically different from each other at P < 0.05. also, Bars indicate ± SE).

Conversely, the betacyanin content was determined based on the dry weight of the samples. The highest amount of betacyanin was found in the R7, with a concentration of 22.63 mg per 100g of dry weight (DW) which is equivalent to the amount of anthocyanin. However, it also indicates more than double the concentrations than that of the R1 (10.43 mg/100g) and R6 (9.91 mg/100g) genotype while the lowest betacyanin content was observed in R10.

Tocopherol Content (Vit.E) (mg/100 g DW)

The tocopherol content in the rose genotypes examined ranged from 400.08 to 300.95 mg/100 g DW. The highest concentration was found in the R8 genotype (400.08 mg/100 g DW), which was significantly different from the other genotypes. However, the second greatest value was achieved from R6 (400.05 mg/100 g DW), which was equal to the value acquired from R1 (400.05 mg/100 g DW). On the other hand, the lowest value was seen in R10 (300.95 mg/100 g DW), which was statistically close to the value in R9 (300.97 mg/100 g DW) (Table 2).Table 2 The bioactive components and Total Antioxidant Activity of ten rose genotypes.

Rose genotypes	Tocopherol (mg α-tocopherol/100 g DW)	Total phenolic content (mg GAE/100 g, FW)	Total flavonoid content (mg QE/100 g, FW)	Total antioxidant activity	
IC50 (µg/mL FW)	
R1	400.05 ± 0.01 bx	303.07 ± 1.00 e	0.76 ± 0.03 j	82.60 ± 1.00 g	
R2	400.01 ± 0.01 d	293.41 ± 2.10 f	11.21 ± 0.11 g	5536.48 ± 1.16 c	
R3	400.01 ± 0.01 cd	229.20 ± 1.00 j	27.77 ± 0.21 a	–	
R4	400.01 ± 0.01 d	241.87 ± 0.15 i	19.16 ± 0.01 b	–	
R5	400.01 ± 0.02 d	256.48 ± 1.00 h	12.56 ± 0.01 f	5302.24 ± 2.00 d	
R6	400.05 ± 0.02 b	533.18 ± 1.01 a	14.39 ± 0.03 e	5777.53 ± 5.77 a	
R7	400.04 ± 0.20 bc	371.63 ± 0.56 d	7.15 ± 0.01 h	5618.93 ± 3.79 b	
R8	400.08 ± 0.01 a	394.54 ± 0.01 b	4.80 ± 0.10 i	1451.957 ± 5.77 f	
R9	300.97 ± 0.01 e	376.93 ± 0.02 c	17.19 ± 0.01 d	1507.33 ± 1.00 f	
R10	300.95 ± 0.03 e	270.68 ± 0.10 g	17.71 ± 0.02 c	4248.713 ± 1.01 e	
x In each column data represented as means ± Standard Deviations followed by different letters are statistically different at p < 0.005 as calculated by Least Significant Different Test (LSD Test).

The total phenolic content (TPC) (mg GAE/100 g, FW)

Table 2 demonstrates that the various rose petal extracts exhibited significant variance in their total phenolic content (TPC). The R6 genotype exhibited the maximum concentration of TPC 533.18 mg GAE/100 g, FW, whereas the lowest concentration was observed in the R4 genotype, with a value of 241.87 mg GAE/100 g, FW.

The total flavonoid content (TFC) (mg QE/100 g, FW)

Upon examining Table 2, it becomes evident that all the rose genotypes exhibited significant variations in the accumulation of flavonoid contents (TFC). The highest level of flavonoid content was observed in R3 (27.77 mg QE/100 g, FW), whereas the lowest concentration was reported in R1 (0.76 mg QE/100 g, FW).

Total Antioxidant Activity (IC50) (µg/mL FW)

The antioxidant activity of rose petals was assessed by determining the IC50 value, which represents the concentration of the sample needed to block 50% of DPPH free radicals. Thus, in the DPPH experiment, greater IC50 values indicate lesser antioxidant activity, and vice versa. Table 2 clearly shows that the IC50 values of 10 rose extracts had a substantial impact on their ability to scavenge free radicals (p < 0.05). The R1 rose genotype had the most potent antioxidant activity, as evidenced by its lowest IC50 value of 82.60 µg/mL FW. Among the other rose genotypes, R5, R6, R7, R8, R9, and R10 exhibited an IC50 value greater than 250 µg/mL FW, indicating their inactivity in free radical scavenging action. Regrettably, the antioxidant activity of two rose genotypes, R3 and R4, was not observed in this experiment.

Minerals (g/100g) and Moisture content (%)

The results from Table 3 clearly demonstrate that the accumulation of mineral elements such as sodium (Na), potassium (K), calcium (Ca), iron (Fe), and moisture content varied significantly among the ten rose genotypes, with the exception of magnesium (Mg). Moreover, within the composition of these minerals, the concentration of potassium was particularly notable in the rose genotypes. The sodium (Na) concentration in the petals of ten different rose genotypes varied significantly. The highest accumulation of Na was seen in genotype R5 (0.092 g/100g DW), followed by genotype R9 (0.085 g/100g). Simultaneously, the lowest recorded value was obtained from R10 (0.062 g/100g), which was exactly the same as R7 (0.065 g/100g). The analysis found that the potassium level varied between 1.408 and 0.984 g/100g where the maximum concentration was seen in R2, while the lowest value was found in R4, which was statistically similar to R6. The average value of calcium content differed across the roses where the R1 and R3 had the highest accumulation of Ca at a concentration of 0.19 g/100g, whereas R6 and R7 had the lowest concentration at 0.14 g/100g. The accumulation of Fe, varied greatly, ranging from 0.090 to 0.017 g/100g. The top accumulator was R4, while R9 was the lowest accumulator of Fe.Table 3 Comparisons of mineral matters and moisture percentage of ten different color rose genotypes.

Rose genotypes	Sodium (Na g/100g)	Potassium (K g/100g)	Calcium (Ca g/100g)	Magnesium (Mg g/100g)	Iron (Fe g/100g)	Moisture (%)	
R1	0.079 ± 0.003 cx	1.288 ± 0.001 c	0.19 ± 0.000 a	0.102 ± 0.001 a	0.039 ± 0.002 c	84.81 ± 1.110 d	
R2	0.078 ± 0.002 c	1.408 ± 0.001 a	0.15 ± 0.002 f	0.102 ± 0.001 a	0.060 ± 0.002 b	82.50 ± 0.900 e	
R3	0.074 ± 0.004 cd	1.288 ± 0.001 g	0.19 ± 0.000 a	0.101 ± 0.001 a	0.055 ± 0.002 b	68.51 ± 0.510 g	
R4	0.072 ± 0.002 de	0.984 ± 0.001 f	0.19 ± 0.001 b	0.102 ± 0.001 a	0.090 ± 0.010 a	84.74 ± 0.200 d	
R5	0.092 ± 0.002 a	1.328 ± 0.001 b	0.15 ± 0.002 f	0.101 ± 0.001 a	0.037 ± 0.003 c	87.07 ± 1.010 ab	
R6	0.067 ± 0.002 ed	1.146 ± 0.001 f	0.14 ± 0.000 g	0.102 ± 0.001 a	0.024 ± 0.001 de	87.31 ± 0.100 a	
R7	0.065 ± 0.003 f	1.207 ± 0.001 e	0.14 ± 0.000 g	0.101 ± 0.001 a	0.020 ± 0.001 ef	81.06 ± 1.010 f	
R8	0.079 ± 0.004 c	1.247 ± 0.001 d	0.16 ± 0.001 e	0.101 ± 0.001 a	0.025 ± 0.001 de	85.63 ± 0.110 cd	
R9	0.085 ± 0.002 b	1.288 ± 0.001 c	0.17 ± 0.000 d	0.102 ± 0.001 a	0.017 ± 0.001 f	85.38 ± 0.200 cd	
R10	0.062 ± 0.003 f	1.328 ± 0.001 b	0.18 ± 0.001 c	0.103 ± 0.001 a	0.028 ± 0.001 d	86.25 ± 0.120 bc	
x The column under each parameter, data were represented at Mean ± Standard Deviation and data with the various letters are differ significantly from each other (p < 0.05).

Meanwhile, the rose petals exhibited a significant variation in moisture content, with the greatest reported in the rose genotype R6 (87.31%). This value was statistically equivalent to that of R5 (87.07%) and the water content in R3 was significantly low, measuring at 68.51%.

Antinutrient properties (g/100 g DW)

Upon examining Table 4, it is evident that there were notable variations in the levels of alkaloids, phytate, saponin, and tannins among the rose genotypes. The alkaloid content of rose petals in this investigation varied from 1.24 to 14.64 g/100 g DW (Table 4). Of the ten rose genotypes, R2 had the greatest alkaloid content accumulator (14.64 g/100 g DW), followed by R1 (9.52 g/100 g DW), and R10 (1.24 g/100 g DW) had the lowest. Of these three metrics, the phytate content indicated a very small quantity of present (Table 4). However, R6 differs greatly from the others due to its higher amount (0.63 g/100 g DW). The majority of the saponin (14 g/100 g DW) was found in the rose petals of R6 and R9, with R3 and R7 following closely behind (12 g/100 g DW), and R1 containing the least (4.03 g/100 g DW). The data displayed in Table 4 indicates that there was a notable variation in the tannin content among the ten rose genotypes, ranging from 143.55 to 198.05 mg TAE/100g DW. The highest tannin conserver in this testing was R7 (198.05 mg TAE/100g DW), followed by R6 (180.57 mg TAE/100g DW). Conversely, the R2 genotype had the least amount of tannin (143.55 mg TAE/100g DW), and it was statistically comparable to the R10 and R4 containers (143.97 and 145.47 mg TAE/100g DW, respectively).Table 4 The antinutritional components (Alkaloid, Phytate, Saponin and Tannin) of ten rose genotypes.

Rose genotypes	Dry weight basis	
Alkaloid (g/100 g)	Phytate (g/100 g)	Saponin (g/100 g)	Tannin (mg TAE/100g)	
R1	9.52 ± 0.20 bx	0.26 ± 0.002 c	4.03 ± 0.07 d	164.54 ± 1.36 c	
R2	14.64 ± 0.21a	0.32 ± 0.010 b	8.00 ± 0.10 c	143.55 ± 0.69 g	
R3	6.16 ± 0.03 c	0.05 ± 0.002 f	12.00 ± 0.1 b	158.01 ± 1.74 d	
R4	2.77 ± 0.30 g	0.02 ± 0.001 g	8.00 ± 0.20 c	145.47 ± 1.18 g	
R5	5.52 ± 0.04 d	0.09 ± 0.002 d	8.00 ± 0.20 c	151.64 ± 1.89 e	
R6	4.04 ± 0.02 e	0.63 ± 0.002 a	14.00 ± 0.10 a	180.57 ± 2.94 b	
R7	0.24 ± 0.02 j	0.26 ± 0.001 c	12.00 ± 0.10 b	198.05 ± 0.32 a	
R8	3.68 ± 0.20 f	0.02 ± 0.002 g	8.00 ± 0.10 c	180.09 ± 1.60 b	
R9	1.60 ± 0.10 h	0.07 ± 0.001 e	14.00 ± 0.10 a	148.37 ± 1.19 f	
R10	1.24 ± 0.03 i	0.02 ± 0.001 g	8.00 ± 0.20 c	143.97 ± 1.52 g	
x The column under data of each parameter, were represented at Mean ± Standard Deviation and data with the various letters are differ significantly from each other (p < 0.05).

Antinutrients to molar ratios

The molar ratios of [PHT]: [Ca], [Ca]: [PHT], [PHT]: [Fe], [PHT]: [K], [Mg]: [PHT], [TNN]: [Fe], and [PHT + TNN]: [Fe] were determined based on the analyzed data of antinutrients and minerals of rose genotypes and presented in Table 5. Out of the ten rose genotypes, all except for R6 exhibited [PHT]: [Ca] ratios below the crucial value of 0.24 and [Ca]: [PHT] ratios above the critical value of 0.6. The rose genotype R6, however, had [PHT]: [Ca] and [Ca]: [PHT] ratios of 0.273 and 3.667, respectively. Based on the Table 5, it is evident that the rose genotypes R6 and R7 exhibited a [PHT]: [Fe] ratio that exceeded the threshold value of 1, indicating a lower bioavailability of iron. On the other hand, R4 had the lowest ratio of 0.019, suggesting the highest absorption of iron in nutrition. Regarding [PHT], the 10 rose genotypes exhibited variances in [K] molar ratios, which varied from 0.001 to 0.033. Table 5 shows that the molar ratios of [Mg]: [PHT] in rose genotypes varied from 4.396 to 139.819. Among them, R10, R4, R8, R3, and R9 had larger amounts of magnesium, with molar ratios of 139.819, 138.462, 137.104, 54.842, and 39.560, respectively. The molar ratios of [TNN]: [Fe] were adjusted within the range of 0.142 in R4 to 0.871 in R7 rose genotypes. The molar ratio of [Fe] for [PHT + TNN] ranged from 0.161 in R4 to 2.884 in R6, with R6 having the greatest ratio.Table 5 Bioavailability of minerals in response of the molar ratio of phytates and tannins to minerals of ten rose genotypes.

Rose genotypes	[PHT]: [Ca]	[Ca]: [PHT]	[PHT]: [Fe]	[PHT]: [K]	[Mg]: [PHT]	[TNN]: [Fe]	[PHT + TNN]: [Fe]	
R1	0.083	12.058	0.566	0.012	10.651	0.371	0.937	
R2	0.129	7.734	0.453	0.013	8.654	0.210	0.663	
R3	0.016	62.700	0.077	0.002	54.842	0.253	0.330	
R4	0.006	156.750	0.019	0.001	138.462	0.142	0.161	
R5	0.036	27.500	0.206	0.004	30.468	0.361	0.567	
R6	0.273	3.667	2.227	0.033	4.396	0.662	2.889	
R7	0.113	8.885	1.103	0.013	10.546	0.871	1.974	
R8	0.008	132.000	0.068	0.001	137.104	0.634	0.702	
R9	0.025	40.071	0.349	0.003	39.560	0.768	1.117	
R10	0.007	148.500	0.061	0.001	139.819	0.452	0.513	
Critical Value	< 0.2457,58	> 6.0 favorable59	< 160	–	–	–	–	

Qualitative assessment of bioactive compounds through visual color changes of the extract solution

The phytochemical screening tests largely determined the presence or absence of steroids, coumarins, quinones, anthraquinones, and phlobatannins in the ten rose genotypes. This was done using color reactions and visual color changes in the extracts, as shown in Table 6. The presence of phytosteroids, coumarin anthraquinones, quinones, and phlobatanin was confirmed through various chemical reactions, including the formation of a brown ring, the development of a yellow-colored solution, the precipitation of red-colored solids, a color change from red to blue, and the precipitation of pink-colored solids, respectively (Fig. 5. A1, A2, A3, A4, A5). The analysis of the ten rose genotypes revealed the presence of steroids in R1, R4, and R10 genotypes, coumarines in R1, R3, R4, R9, and R10 genotypes, quinones in R1, R2, R6, and R7 genotypes, anthraquinone in R1, R3, R6, and R7 genotypes, and phlobatanin exclusively in the R6 rose genotype.Table 6 Screening of rose genotypes according to the presence or absence of antinutrients phytochemicals.

Rose genotypes	Phytochemicals	
Steroids	Coumarines	Quinones	Anthraquinone	Phlobatanin	
R1	+ vex	+ ve	+ ve	+ ve	–	
R2	–	–	+ ve	–	–	
R3	–	+ ve	–	+ ve	–	
R4	+ ve	+ ve	–	–	–	
R5	–	–	+ ve	–	–	
R6	–	–	+ ve	+ ve	–	
R7	–	–	+ ve	+ ve	–	
R8	–	–	–	–	–	
R9	–	+ ve	–	–	–	
R10	+ ve	+ ve	–	–	–	
x+ ve sign implies the presence of phytochemicals and –absence.

Fig. 5 Methanolic extract color changes confirmation of the presence of antinutrients in rose genotypes. (A1) Steroids, (A2) Coumarines, (A3) Quinones, (A4) Anthraquinone and (A5) Phlobatanin.

Correlation coefficient analysis

The Pearson correlation coefficient was employed to evaluate the intra and interrelationships among the 25 variables under investigation. The correlation matrix visually represents the degree of both positive and negative association between the colorimetric parameters, secondary metabolites, and mineral contents (Fig. 6). In the event of a positive correlation, an increase in one variable will result in a corresponding increase in another variable, whereas a negative correlation indicates that an increase in one variable will lead to a decrease in another one. The circles in Fig. 6, colored in blue and red, indicate positive and negative correlations, respectively. The intensity of the color represents the strength of the correlation between the variables. Vacant cells indicate an inconsequential association at a 5% level of significance. The Pearson's correlation coefficient and correlation matrix revealed a significant link between colorimetric features and secondary metabolites, ranging from moderately strong to extremely strong. However, this correlation was not observed with mineral matters. Among these factors, a significant positive correlation (R2 = 0.95) was observed between the total carotenoid and β-carotene content. This suggests that an increase in total carotenoid content is associated with a corresponding increase in the concentration of β-carotene. The total carotenoid concentration exhibited a significant positive correlation with h° and b* (R2 = 0.71, 0.79). Moreover, there was a significant and positive relationship between the β-carotene content and both the b* value and hue angle, with R2 values of 0.84 and 0.79, respectively. Conversely, the overall amount of anthocyanin was highly and positively associated with a*, phytate, and betacyanin (R2 = 0.89, 0.86, and 70), whereas it exhibited a substantial negative association with L* and pH (R2 = 0.914, 0.819). Furthermore, there was a significant positive relationship between the betacyanin and tannin concentration (R2 = 0.75), as well as a substantial negative association with L* (R2 = 0.874). In addition, there was a significant positive association between the phytate content and a* (R2 = 0.89), as well as a negative link with L* (R2 = 0.77). Additional observations revealed a negative correlation between the minerals (Na, K, Ca, Mg, and Fe) and the levels of anthocyanin, betacyanin, and phytate. It was observed that the rose blossom became darker in color as the levels of anthocyanin, betacyanin, and phytate increased. Conversely, the flower's color faded with an increase in mineral content. From an antioxidant perspective, the IC50 value demonstrated a significant inverse relationship with Ca (R2 = 0.85), similar to the correlation between TSS and pH (R2 = 0.82) (Fig. 6).Fig. 6 Correlation matrix in between and among colorimetric parameters, secondary metabolites and mineral contents (25 variables) of rose genotypes. (Color Scale in the right side depicts that, the blue color indicating positive correlation and red color negative as color intensity increases the correlation increases and same as when decreases). [L: (L*) Lightness, a: (a*) greenness (−) to redness (+), b: (b*) blue (−) to yellow (+), c:(c*) saturation of the color, h: (hº) huge angle, TSS: total soluble solids X-carn: total carotenoid content, VitA: β- carotene, AOA: anthocyanin, BTC: betacyanin, VitE: tocopherol, TPC: total phenol content, TFC: total flavonoid content, IC50- total antioxidant activity, Alk: Alkaloid, PHT: phytate, SPN: saponin, TNN: tannin, Na: sodium, K- potassium, Ca: Calcium, Mg: Magnesium, Fe: iron, MC: moisture content].

Principal component analysis (PCA)

The previous section findings revealed that the variables made a substantial contribution to the classification of the rose genotypes. The PCA was conducted to ensure consistency of the data and assess the extent of variation among variables. Principal Component Analysis (PCA) is a form of multivariate analysis that transforms large, intricate datasets with associated variables into groupings in order to uncover the most influential characteristics. The PCA biplot diagram visually represented the relationships, both similarities and dissimilarities, among the various parameters in Fig. 8A,B. The diagram specifically focused on the first dimension (PC1) and the second dimension (PC2); the initial two principal components (PCA), account for 54.6% of the overall data variance. Specifically, PC 1 and PC 2 individually account for 32.4% and 22.2% of the variance, respectively. The factor loadings and scores for the first two principal components (Dim 1 and Dim 2) of color parameters, secondary metabolites, and mineral matters of ten rose genotypes (Fig. 7) indicated that a positive score on Dim1 was associated with L, b, pH, Vit A, X.Carn, TCP, Na, Ca, Mg, and Fe (ranging from 0.05 to 0.3), while all the other variables had moderate to low negative scores (ranging from -0.04 to -0.25). Conversely, eleven variables, namely pH, Vit E, TFC, AOA, BTC, Na, Fe, PHT, ALK, SPN, and TNN, exhibited a positive score in Dim 2. In contrast, the variables L, b, c, h, MC, TSS, VitA, X. carn, TCP, IC50 K, Ca, and Mg contributed negatively to the score in Dim 2.Fig. 7 Factor loadings for the first two principal (Dim 1 and Dim 2) components of color parameters, secondary metabolites and mineral contents of rose genotypes. [L: (L*) Lightness, a: (a*) greenness ( −) to redness ( +), b: (b*) blue ( −) to yellow ( +), c:(c*) saturation of the color, h: (hº) huge angle, TSS: total soluble solids X-carn: total carotenoid content, VitA: β- carotene, AOA: anthocyanin, BTC: betacyanin, VitE: tocopherol, TPC: total phenol content, TFC: total flavonoid content, IC50- total antioxidant activity, Alk: Alkaloid, PHT: phytate, SPN: saponin, TNN: tannin, Na: sodium, K- potassium, Ca: Calcium, Mg: Magnesium, Fe: iron, MC: moisture content].

Upon examination of Fig. 8A,B, it is clear that the variables X. carn, b, VitA, h, c, a, L, pH, TSS, PHT, BTC, and AOA had the greatest influence on the selection of rose genotypes. Consequently, the 10 different rose genotypes were clearly separated into three groups as a result of a positive association in both directions. In this case, the two genotypes R9 (salmon color) and R10 (yellow color) that have the greatest influence on the variables may be easily differentiated from the others due to their strong positive correlation with both dimensions of the biplot. However, the biplot displays both the observations and variables in a given orientation along the PC axis (Dim1 and Dim2) concurrently. The orientation of the variable arrows signifies the direction in which the contribution of the related variable experiences the greatest rise, while the length of the arrows represents the magnitude of the change in that direction.Fig. 8 Principal Component Analysis (PCA) of color parameters, secondary metabolites and mineral contents of rose genotypes. (A) PCA of variables showing their major contribution; (B) Biplot diagram of PCA illustrating the clustering of ten rose genotypes towards the major contribution. [L: (L*) Lightness, a: (a*) greenness (−) to redness (+), b: (b*) blue (−) to yellow (+), c:(c*) saturation of the color, h: (hº) huge angle, TSS: total soluble solids X-carn: total carotenoid content, VitA: β- carotene, AOA: anthocyanin, BTC: betacyanin, VitE: tocopherol, TPC: total phenol content, TFC: total flavonoid content, IC50- total antioxidant activity, Alk: Alkaloid, PHT: phytate, SPN: saponin, TNN: tannin, Na: sodium, K- potassium, Ca: Calcium, Mg: Magnesium, Fe: iron, MC: moisture content].

Cluster analysis

Cluster analysis was conducted using the K-means algorithm to group ten rose genotypes based on 25 quantitative attributes. To do this, a dendrogram was constructed and then divided at a rescaled distance of 7.5. This division resulted in the formation of three separate clusters of roses, each exhibiting significant similarities in terms of the studied attributes (Fig. 9). Table 7 provides a compilation of three clusters of rose genotypes in relevance with the CIELAB system and their visual evidence. The cluster I consisted of four rose genotypes (R3, R4, R5, R8), which accounted for 40% of the plant population, just like cluster III (R1, R2, R6, R7). Cluster II consisted of R9 and R10 rose genotypes, which accounted for 20% of the population. Considering the contribution of rose genotype in the CIELAB it has been revealed that the cluster I with the highest contribution of L* and the lowest b* value and which visualized the light color flower, cluster II with the highest contribution of b*, h° and cluster III with the highest contribution of a*, C* and appeared as bright color and dark color, respectively.Fig. 9 Dendrogram cluster of ten rose genotypes in associated with 25 dependent variables.

Table 7 Flower cluster based on 25 variables in relation to color parameters and visual evidence among ten rose genotypes.

Rose genotypes cluster	Frequency (%)	In relation to CIELAB	Visual evident	
I (R3, R4, R5, R8)	4 (40)	Light color

> L*

< b*

		
II (R9, R10)	2 (20)	Bright color

> b*, h°

		
III (R1, R2, R6, R7)	4 (40)	Dark color

> a*, C*

		
L* measures the lightness of color, a* ranged from green to red, b* blue to yellow, C* chroma of the color and h° represents the brightness of the rose petals.

Discussion

The rose is a type of flower that is both edible and decorative. It is known for containing secondary metabolites, which have both nutritional and medicinal characteristics. Additionally, roses can be used as natural food coloring agents. The color of flowers is a prominent characteristic that is highly valued by customers and also holds ecological significance. The composition of floral color is influenced by various elements such as secondary metabolites in cell pigments, the shape of epidermal cells35, the pH of cell sap36, and mineral content37. The most influential factor among them is the buildup of a certain type of pigments (anthocyanins and flavonoids)38. However, these bioactive compounds such as phenolic contents, anthocyanins, flavonoids, tocopherols etc. were varied significantly among the Rosa spp. in this study. This finding was concurred with the previous results where it has been claimed that the accumulation of these substances in plants influenced by several issues viz., environmental factors (soil nutrients, temperature, light), plant genotypes, season of cultivation, altitude as well as under different stresses39.

The genus Rosa, which includes roses, has a remarkable range of characteristics such as a delightful scent, distinctive shape, and a wide array of colors. Nevertheless, there is still a lack of documentation regarding the measurement of color in rose genotypes and its correlation with secondary metabolites, metal ions, and antinutrient characteristics. Additionally, the categorization of rose genotypes based on these features has not been recorded.

In the investigation, it was found that the flower with the highest L* value was R4, which had a white color. The red color rose with the maximum a* value was R6. Additionally, the flower with the highest b* value was R10, which corresponded to a yellow color. These findings were congruent with the visually seen colors of the flowers (Table 7). Prior studies have indicated that anthocyanins contribute to the development of red to purple hues, while carotenoids are responsible for the generation of yellow to red colors in ornamental plants40,41. In other study it was noticed that in potted multiflora chrysanthemum, only the b* value showed a high positive connection with total carotenoids (r = 0.881, P < 0.01) and lutein (r = 0.804, P < 0.01)42. Nevertheless, the results of this investigation have also demonstrated a resemblance to the previous study, indicating that R10, a yellow-colored rose sample, exhibited the highest concentration of total carotenoids. This concentration was found to be positively associated with β-carotene, and both of these compounds displayed a significant positive connection with h° and b*. Nevertheless, the β-carotene content could not be detected in the R5 (Purple color) and R7 (Blackish red color) rose genotypes throughout this research study. However, R6 (red color), have demonstrated a high capacity for accumulating anthocyanin, betacyanin, tannin, and saponin. Anthocyanins are a group of secondary metabolites that are derived from flavonoids. Betacyanins, on the other hand, are nitrogen-containing compounds present in a small number of plant families. Both anthocyanins and betacyanins contribute to the yellow to red colors observed in plants43,44. The rose genotypes in this experiment have demonstrated the presence of betacyanin content, which might potentially be utilized by the food sector as a natural food coloring ingredient. The correlation study has demonstrated a strong positive association between the total anthocyanin and a* value, as indicated by the multivariate analyses as well. Additionally, betacyanin and phytate also showed high correlations with these two variables. Anthocyanin, betacyanin, and phytate have been found to have a negative connection with mineral content. Mineral substances contribute to the development of flower color by combining with flavonoids to create supramolecular pigments known as metal complexes. Our investigation has revealed a weak to negative association between the total flavonoid levels and mineral matters, anthocyanin, a*, and betacyanin. Previous work has indicated that flavonoids possess colorless structures that alter the intensity of yellow pigmentation in plants38. The current study found that the rose genotype R3, which is baby pink in color, has the largest accumulation of total flavonoids. Additionally, it was observed that there is a weakly positive link between the levels of total flavonoids and the L* and b* values.

When considering food, it is important to take into account the antinutritional properties of substances such as tannin, saponin, alkaloid, and phytate. Recent data has demonstrated that the ingestion of these secondary metabolites can have beneficial impacts on human health, contingent upon the dietary pattern and composition. Tannin, saponin, phytate, and alkaloid are polyphenols that possess antioxidant characteristics, which enhance the human body's immune system45–47. According to the author48, tannins in food can create complex compounds with proteins, carbohydrates, and specific minerals. However, the development of these complexes depends on factors such as temperature, pH, and concentration, which must be suitable. Furthermore, the oral administration of tannin resulted in a roughly 50% decrease in toxicity compared to the rectal administration of tannic acid49,50. Phytate is classified as a nutraceutical and is deemed generally recognized as safe (GRAS) by the Food and Drug Administration (FDA)45. Plant-derived alkaloids serve as a valuable source for the development of medicines and pharmaceuticals. It has demonstrated antiviral, antibacterial, antiproliferative, and insecticidal characteristics51. In addition, both secondary metabolites and other compounds such as total carotenoids, total phenols, total flavonoids, and vitamin E play a crucial role as antioxidants52.

This study examined the link between the overall antioxidant activity (measured by the IC50 value) of several rose genotypes and their secondary metabolites. The results showed a positive correlation between the IC50 value and the presence of alkaloids, tannin, saponin, phytate, total phenols, anthocyanin, and betacyanin. In addition, there is a clear negative association between phytate and the levels of calcium (Ca), magnesium (Mg), and iron (Fe), as indicated by the IC50 value. Excessive intake of phytate has been demonstrated to decrease the absorption of minerals, particularly calcium, magnesium, iron, and zinc. Therefore, it is advisable to consume 100–400 mg/day of phytate53. The presence of antinutrients such as tannin, polyphenols, and phytate in plant materials significantly decreases the bioavailability of minerals, including calcium (Ca), iron (Fe)33,54, magnesium (Mg), and potassium (K)55. The molar ratios of [PHT]: [Ca] are less than 0.2456,57, while the ratios of [Ca]: [PHT] are larger than 6.0 which indicate a positive effect on the bioavailability of Ca58. The phytate concentrations in all rose genotypes showed a positive effect on Ca absorption, except for R6. The phytate level in plant-based nutrition is widely recognized as the primary factor that hinders the absorption of iron. In order to mitigate the negative impacts of phytates, it is recommended that the phytate content in the food material be kept below 0.1g per 100g57. Furthermore, a molar ratio of [PHT]: [Fe] < 1 serves as an indication of favorable iron bioavailability59. However, the presence of phytate in R6 and R7 rose genotypes might hinder the absorption of Fe. The solubility of magnesium and phytate complexes is directly correlated with the pH of the solution. The magnesium complex with a molar ratio of 6:1 with [PHT] demonstrated that it is highly soluble at pH levels below 5.0. However, as the pH increases, the solubility of magnesium decreases fast and becomes insoluble at pH levels over 8.060. The cell sap of the ten rose genotypes exhibited an acidic pH ranging from 4.50 to 5.60. This indicates that the presence of phytate concentrations in these rose petals’ products would not hinder the bioavailability of Mg. The impact of tannin on the absorption of iron has been studied in common beans, with the ratios of tannin to iron ([TNN]: [Fe]) ranging from 0 to 65.7, and the ratios of phytate plus tannin to iron ([PHT + TNN]: [Fe]) ranging from 24 to 90.133.

In addition, the principal component analysis (PCA) and K-means cluster analysis were used to categories the 10 various color rose genotypes based on the linked variables. The cluster-I comprises the following rose genotypes: R4 (white color), R3 (baby pink color), R8 (multicolor), and R5 (purple color). Cluster II consists of R10 (yellow color) and R9 (salmon color), which is characterized by high levels of total carotenoids and β-carotene concentration. The cluster III comprises R1 (orange color), R2 (pink color), R6 (red color), and R7 (blackish red color), mostly due to their high levels of anthocyanin, total phenol content, tannin, TSS, phytate and betacyanin. This discovery demonstrates a resemblance to the findings of61, who showed that out of ten different colored roses, the red ones contained the highest amount of anthocyanins and overall phenol levels. The co-pigmentations of anthocyanins in orange color to blackish red color flowers are regulated by the presence of phenolic acids residues and sugar content. These compounds help stabilize the color in various regions of the plants. Based on the present study's results, it is evident that the rose genotypes R9, R10 (cluster II) and R1, R2, R6, R7 (cluster III) are particularly rich in secondary metabolites. These compounds have potential use in the food colors, cosmetics, and pharmaceutical industries. A study conducted on 30 flower species62 found that the rose species had the highest levels of total phenol content, antioxidant activity, and acted as a significant source of bioactive chemicals.

Conclusion

The results indicate significant variation among the ten rose genotypes in terms of the secondary metabolites screened (steroids, coumarins, quinones, anthraquinone, and phlobatanin) as well as the quantified compounds (total carotenoid, β-carotene content, anthocyanin, betacyanin, tocopherol, phenol contents, flavonoid contents, alkaloid, phytate, saponin, and tannin contents) and their antioxidant properties. The R6 rose genotype (red color flower) exhibited the highest levels of anthocyanin, total phenol, phytate, saponin, and colorimetric parameter a*. The R7 genotype (blackish red color) had the highest levels of betacyanin, tannin, and pH. The R10 genotypes (yellow color flower) showed the highest levels of L*, b*, C*, h°, total carotenoid content, and β-carotene. Lastly, the R1 genotype had the highest free radical scavenging potentials and TSS. The findings of principal component analysis (PCA) showed that the total carotenoid, b*, β-carotene content, h°, c*, a*, L*, pH, TSS, Phytate, Betacyanin, and Anthocyanin has strong correlation with each other and influenced on the grouping of the ten examined rose genotypes into three clusters. Therefore, the rose genotypes categorized in cluster III (R1, R2, R6, R7) and cluster II (R9, R10) are suggested as promising genotypes with secondary metabolites for future application in the food sector, cosmetics, fragrance, and pharmaceuticals. These selected rose flowers should be conserved as valuable sources of natural antioxidants for use as functional food components or medications to manage disorders induced by oxidative stress.

Declaration on plant handling with the relevant guidelines and regulations

The present study utilized Rose (Rosa sp.) flowers as the plant material. The cultivated rose genotypes were obtained from the flower garden of Bangabandhu Sheikh Mujibur Rahman Agricultural University, located in Gazipur-1706, Bangladesh. The rose genotypes are cultivated and conserved in the university's flower garden for utilization in research endeavors. The field and laboratory investigation were conducted using established advance protocols and adhering to the scientific ethics rules and regulations for handling plants.

Supplementary Information

Supplementary Figure 1.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72424-w.

Acknowledgements

The authors are highly grateful to the research management wing (RMW), Bangabandhu Sheikh Mujibur Rahman Agricultural University for the financial and logistic supports to carry out this research work under the innovation project (ID: 008). The authors are also extending their gratitude to the Post-Harvest Division of Bangladesh Agricultural Research Institute and Department of Agro-Processing and Soil Science for providing their lab facilities to carry out the analyses. Our sincere appreciation also goes to the Researchers Supporting Project number (RSP2024R219), King Saud University, Riyadh, Saudi Arabia.

Author contributions

J. H., S. R. M. and A. A. H. conceived the idea of the study, design and conduct the experiment. S. R. M. and J. H. wrote the manuscript. S. R. M., J. H., A.A.H, M. A. H., E. K., H. S. and M. A. contributed in sample collection, preparation and laboratory analyses. J.H., S. R. M. and A.A.H analyze the data and made necessary interpretation. M. A. H., E. K., H. S., M. A., M. H. S., A.A.H and Y. O. reviewed and edited the manuscript for further improvement. All authors have read, edited the manuscript and approved it for submission.

Data availability

The data will be made available from the corresponding author J. Hassan on request. From the link below: https://drive.google.com/file/d/12I-1yTNQJ6ZWnXHI7wqRImJK6gKokUHX/view?usp = sharing.

Competing interests

The authors declare no competing interests.

Publisher's note

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