Comparative analysis of phytochemical traits, proximate composition, and metabolite diversity in Nigella sativa L. genotypes from India

Y Ravi; P I Vethamoni; S N Saxena; N Ashoka; S Choudhary; Kailashpati Tripathi; P Yadav; A K Verma; C B Harisha; P Dhamotharan; R Singh; N K Meena; R S Meena; Vinay Bhardwaj; Manjesh Guligenahalli Narayanappa

doi:10.1038/s41598-026-52441-7

. 2026 Jun 20;16:28317. doi: 10.1038/s41598-026-52441-7

Comparative analysis of phytochemical traits, proximate composition, and metabolite diversity in Nigella sativa L. genotypes from India

Y Ravi ^1,^✉, P I Vethamoni ², S N Saxena ³, N Ashoka ⁴, S Choudhary ¹, Kailashpati Tripathi ¹, P Yadav ⁵, A K Verma ¹, C B Harisha ⁶, P Dhamotharan ⁷, R Singh ¹, N K Meena ¹, R S Meena ¹, Vinay Bhardwaj ¹, Manjesh Guligenahalli Narayanappa ^8,^✉

PMCID: PMC13558749 PMID: 42323380

Abstract

Nigella is a medicinal cum spice plant and is celebrated for its minute, distinctive black seeds and is native to Africa, Asia and the Mediterranean region. The present study aimed to assess variation in oil yield, biochemical composition, phytochemical traits, metabolite diversity, fatty acid profile, and nutritional indices using multivariate analysis. Oil yield displayed a broad range, from 9.96% in NSC‑16 to 29.93% in NSC‑6. GC–MS profiling revealed linoleic acid as the predominant fatty acid, accompanied by notable levels of oleic, palmitic, and stearic acids. Polyunsaturated fatty acids (PUFA) represented the major lipid fraction (57.11–71.92%), yielding favorable nutritional indices (PUFA/SFA:3.52–5.45; MUFA+PUFA/SFA: 4.52–6.95). Thymoquinone content exhibited pronounced chemotypic variation, peaking at 247.6 µg/100 mg seed in AN‑13. Phenolic, Flavonoid density ranged approximately ~ 2.5 to 11 mg g⁻¹ oil and ~ 0.6–2.8 mg g⁻¹ oil respectively and the thymoquinone index ranged ~ 0.3 to ~ 1.6 mg g⁻¹ oil. Distinct patterns in antioxidant activity, carbohydrate allocation, and bioactive metabolite indices highlighted strong metabolite specialization among genotypes. Multivariate analysis pinpointed NSC‑6, NSC‑5, AN‑8, AN‑11, AN‑12, AN‑2, NSC‑1, NSC‑2, AN‑35, and AN‑13 as superior genotypes, integrating high oil yield, PUFA enrichment, robust antioxidant activity, and elevated bioactive compound accumulation. These elite lines represent valuable resources for nutraceutical innovation, functional food development, and targeted breeding strategies aimed at enhancing health‑promoting traits.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-52441-7.

Keywords: Nigella, Fatty acids, Oil profiling, Metabolites, Diversity

Subject terms: Biochemistry, Biotechnology, Plant sciences

Introduction

Kalonji or Nigella (Nigella sativa L.) is an erect, herbaceous annual plant, belonging to the plant family Ranunculaceae¹. This medicinal cum spice plant is celebrated for its minute, distinctive black seeds is native to Africa, Asia and Mediterranean region². These seeds have been valued for their pungent odour and potential health benefits viz., antimicrobial^3,4, anticancer^5,6, antidiabetic^7,8 and have strong antioxidant, galactagogue, carminative, laxative and antiparasitic properties⁹. The seed and its extract have played a significant role in traditional medicine and regional cuisines. As a seed spice and condiment in Indian, Middle Eastern and North African cuisines, they are often sprinkled onto bread or incorporated into spice blends¹⁰. The seed possesses dietary fibre, amino acids, carbohydrates like glucose and xylose, arabinose, and unsaturated fatty acids like oleic and linolenic acids, as well as volatile oils, alkaloids, and saponins¹¹. N. sativa L., seeds contain a variety of bioactive components, including proteins, carbohydrates, fibre, volatile and fixed oils, among others¹². Thymoquinone, an active metabolite which is also found in the seed, is an antioxidant and an excellent anti-inflammatory¹³. It has recently emerged as a potential candidate in the battle against oxidative stress and chronic disorders¹⁴.

The oil content and other biochemical composition are influenced by cultivar and genotype, ripening time, environmental factors, and agronomic practices^15,16. Black cumin is gaining popularity and is marketed as a wholesome, useful, and nutrient-dense food when it first hits the market^17,18. Despite extensive research on the therapeutic qualities of Nigella seeds, there are limited information on proximate and biochemical composition, antioxidant activity, nutritional utility ratios, energy distribution to the level of genotypic heterogeneity among available N. sativa genotypes in India, particularly in response to the growing demand for nutritious and useful food. The current study used multivariate statistical techniques to investigate differences in the total oil yield, biochemical composition, phytochemical traits, proximate composition, metabolite diversity, fatty acid composition and nutritional utility ratios in 38 Indian genotypic populations of N. sativa L. The economic use of the seeds in the food sectors can be contemplated in light of these findings and further implications in producing novel foods with significant nutritional potential can potentially be a lucrative endeavour for manufacturers using plant materials.

Materials and methods

Plant material and chemicals

The seeds of 38 genotypes of Nigella sativa L., utilized in the present study, were collected from different growing locations in farmers’ fields across various states in India. These collections were made from cultivated fields representing diverse agro-climatic regions based on the spatial range and morphological diversity (Supplementary Fig. 1). Further, we confirm that prior permission was obtained from the respective farmers before collecting the seeds from their fields, as per ethical research guidelines. The collected seeds were subsequently maintained and were sown in the research farm of ICAR- National Research Centre on Seed Spices, Ajmer, Rajasthan (26°27′0″N latitude & 74°38′0″E longitude having an altitude of 460 m mean sea level) in a randomized complete block design (RBD) in three replications. The weather at the trial site was semi-arid, with an average relative humidity of 37%. The plot size was 3 × 2m² with an inter and intra row of 15 cm and 30 cm was considered for the sowing. The recommended package of practices was followed to raise the good field crop, including weed control and cultural care and management. Throughout the growing season, need-based irrigations were provided. Further, all experimental research and field studies on cultivated plant materials were conducted in accordance with the institutional guidelines of ICAR and complied with relevant national and international regulations governing plant research and germplasm handling.

The chemicals and standards 2, viz., Gallic acid, 2- Diphenyl-1-picrylhydrazyl radical (DPPH), Folin–Ciocalteu’s phenol reagent, Thymoquinone and Quercetin used for the analysis were purchased from Sigma–Aldrich Chemical Company (St. Louis, USA). The solvents to extract total oil and other chemicals (analytical grade) were acquired from Merck Company.

Proximate composition analysis

The proximate composition from seeds of 38 ecotypes were analysed Association of the official analytical chemicals (AOAC, 2000)¹⁹ methods.

Moisture content

To measure moisture content 5 g of seeds were ground and lay open to for oven drying at 105 °C for 4 h. It was again weighed after cooling in desiccators until it attained a constant weight. The resultant loss in weight was calculated as moisture content²⁰.

Total ash

A precisely weighed seed sample (5 g) was placed in a crucible and combusted at a low flame until the entire material had become smokeless. Then it was placed in a muffle furnace for 5 h at 550 °C, cooled in desiccators, and weighed. This process was continued until two consecutive weights were constant, at which point the percentage ash was computed as suggested by Kaushik et al.²¹. The percentage of ash was obtained by subtracting the initial and final weights.

Fat content

A 5 g of demoisturized powdered seed sample carefully weighed in thimble to measure the fat content and the same was defatted with petroleum ether in a Soxhlet apparatus for 6–8 h at 60 °C. The resulted petroleum ether extract was evaporated, and the fat content was determined by Kaushik et al.²¹.

Crude fibre

The total oil extracted via Soxhlet apparatus was evaporated to remove the residual solvent and the material was boiled for 30 min in 200 ml of sulphuric acid (1.25%), filtered through muslin cloth, and then thoroughly washed in boiling water. The residue was then heated to a boil for 30 min with 200 ml of sodium hydroxide (1.25%), filtered through muslin cloth, and then washed with 25 ml of boiling sulphuric acid (1.25%) and water. The leftovers were put in the weighing (W1) washing dish. Prior to weight measurement, the residue was dried for two hours at 130 ± 2 °C. (W2). It was then reweighed after being ignited for 30 min at 600 ± 15 °C (W3). With the help of the following formula the amount of crude fibre in nigella seed was estimated¹⁹.

Total oil extraction and its yield

To extract total oil from seeds, a Soxhlet apparatus was employed, with powdered dry seed (15 g) immersed in 50 ml of 100% n-hexane at 65 °C for 6 h with slight changes. The extract was treated to rotary flash evaporation under decreased pressure to eliminate solvent residue, and the oily extract was collected and refrigerated for later analysis. Extraction efficiency was evaluated based on extraction yield as a proportion of the raw material using the following formula²¹.

Total phenolic content

The total phenol content was measured using the Folin-Ciocalteu test, as described by Lenny et al.²². In a test tube, a 0.1 ml aliquot of extract was mixed with 700 µl of methanol and 200 µl of Folin-Ciocalteu reagent, which had been diluted 10-fold with distilled water. The mixture was left at room temperature for 5 min. Three milliliters of 10% sodium carbonate were added and shaken thoroughly. The combination was heated in a water bath at 60 °C for 1 min, then brought to room temperature, and the absorbance at 710 nm was measured against a standard curve of gallic acid (Gallic acid was used as the standard, and the calibration curve was prepared over a concentration range of 10–100 µg/mL. The standard curve showed good linearity with the regression equation y = 0.0098x + 0.021 and a coefficient of determination (R² = 0.998). The quantity of phenol was estimated using the gallic acid standard curve and represented as mg Gallic Acid Equivalents per gram (GAE g⁻¹DW). Furthermore, to adjust phenolic content to oil yield, phenolic density was determined as follows:

This metric reflects the concentration of phenolic compounds per gram of oil extracted, allowing genotype level comparisons independent of yield variability.

Total flavonoid content

Total Flavonoid concentration was estimated using the quercetin dehydrate method as described earlier²³. A 1.0 mL of seed extract was mixed with 100 µL aluminium chloride (1 M) and 100 µL potassium acetate, followed by 2.8 mL distilled water to make a final volume of 4 mL. The mixture was incubated at room temperature for 30 min, and absorbance was recorded at 415 nm. Quercetin was used as the standard, and the calibration curve was constructed within the range of 10–100 µg/mL. The regression equation obtained was y = 0.0075x + 0.018, with a high linearity (R² = 0.997). TFC was calculated from a quercetin standard curve and expressed as mg quercetin equivalents g⁻¹ seed (mg QE g⁻¹ DW). The normalization enables direct comparison of flavonoid concentration across genotypes with differing oil content by calculating flavonoid density using:

Antioxidant activity

The extract of the seeds was tested for antioxidant activity by scavenging 1, 1-Diphenyl-2-picrylhydrazyl (DPPH) radicals. The antioxidant activity was determined by its ability to scavenge the stable DPPH radical, using a slightly modified approach reported by Naeem et al.²⁴. In a test tube, 500 µL of seed extract, 1 ml of DPPH, and 2.5 ml of methanol were taken and mixed thoroughly. The mixture was then homogenized and allowed to stand for 30 min in the dark. A spectrophotometer was used to measure the absorbance at 517 nm against a blank sample. The EC₅₀ value represents the concentration of extract that scavenges DPPH radicals by 50%. The obtained results were expressed as the mean standard deviation of three replicate measurements. The percentage scavenging effect was computed as follows:

The IC₅₀ value for each sample defined as the concentration of the test sample leading to 50% reduction of the DPPH concentration was calculated from the non-linear regression curve of the test extract.

Total carbohydrates

The carbohydrate content of nigella genotypes from seed samples were estimated by Anthrone method²⁵. A 0.1 g of seed powder was hydrolysed with 5 mL of 2.5 N HCl in a water bath for 3 h and neutralized with sodium carbonate. The volume was made up to 100 mL, centrifuged at 3000 rpm for 10 min, and the supernatant was collected. Aliquots (0.5–1.0 mL) were used for analysis. A glucose standard curve was prepared using Anthrone reagent (200 mg Anthrone in 100 mL ice-cold 95% H₂SO₄). Samples and standards were heated for 8 min in a boiling water bath, cooled, and absorbance was read at 630 nm.

Nutritional efficiency indices

To assess the comparative efficiency of nutrient accumulation among Nigella genotypes, three nutritional efficiency indices were calculated based on proximate composition and Atwater energy values.

Carbohydrate efficiency index (CEI)

Carbohydrate content was estimated from proximate analysis²⁶. This index normalizes carbohydrate content, allowing direct comparison across genotypes. The CEI was calculated to express the relative efficiency of carbohydrate accumulation in each genotype compared to the highest observed value.

Fat efficiency index (FEI)

This index highlights genotypes with superior lipid accumulation efficiency relative to the maximum observed fat content. The FEI was calculated as:

Energy distribution ratio (EDR)

Energy contributions from fat and carbohydrate were calculated using Atwater conversion factors (9 kcal/g for fat, 4 kcal/g for carbohydrate). The EDR was calculated as:

This ratio reflects the balance between lipid-derived and carbohydrate-derived energy, providing insight into macronutrient energy distribution patterns among genotypes.

Estimation of thymoquinone

The HPTLC analysis was performed on 10 × 20 cm aluminium-backed silica gel 60 F254 plates (0.2 mm, E-Merck). Samples were applied as 6-mm bands using a Camag ATS4 (150 nL s⁻¹). Plates were developed to 80 mm in a Camag ADC2 chamber pre-saturated for 30 min at 22 °C with n-hexane: ethyl acetate (8:2, v/v). After development, plates were scanned at 259 nm in absorbance mode using a Camag TLC scanner (slit 4.0 × 0.45 mm; scanning speed 20 mm s⁻¹)²⁷. To assess thymoquinone efficiency relative to oil yield, the Thymoquinone Index (TQI) was calculated by formula.

FAME analysis

Following the instructions in the AOCS Method, Fatty Acid Methyl Esters (FAME) analysis was used to identify chemical compounds and fatty acids in the seeds of Nigella accessions. The diluted FAME was separated using an Agilent GC-7820 A and an MS-5975 system (Agilent HP-5 MS column, USA, film thickness: 0.25 μm, dimensions: 30 m × 0.325 mm). An autosampler operating in split mode²⁸ was used to inject a 1.0 µL sample at a ratio of 20:1. The helium was a carrier gas and flowed at a rate of one milliliter per minute. The column’s temperature was set to rise between 50 °C and 280 °C for 30 min, followed by a constant period of three minutes. A temperature of 250 °C was chosen as the temperature for the injectors. Retention time comparison was used to identify compounds, and “computer comparison of mass spectrum fragmentation patterns using the updated NIST-MS library and available mass spectra, aided by Chemstation software (Agilent Technologies, USA)” was used to corroborate the results. The precise identification and confirmation of the chemical components found in Nigella seeds are guaranteed by this methodical analytical approach.

Statistical analysis

The experiment was conducted in a randomized complete block design (RCBD) with three replications. Data are presented as mean ± standard deviation (SD). Statistical analysis was performed using two-way analysis of variance (ANOVA) to determine significant differences among genotypes at P < 0.05. When significant differences were observed, mean separation was carried out using Duncan’s Multiple Range Test (DMRT) at the 5% significance level. All biochemical and phytochemical analyses were carried out in triplicate to ensure analytical accuracy. All statistical calculations and graphical representations were performed using Microsoft Excel 2019. For GC–MS metabolite data, multivariate analysis was performed using normalized data classifying AN group, NSC and other groups among the tested genotypes. Heatmap analysis was conducted using MetaboAnalyst 6.0. Principal component analysis (PCA) and biplot visualization were generated using RStudio considering the metabolite profiles of all the genotypes.

Results and discussion

Proximate composition of Nigella genotypes

The Proximate analysis of thirty-eight Nigella genotypes revealed significant differences (P < 0.05) in moisture, total ash, crude fat, and crude fiber (Table 1). This indicates considerable genetic diversity and suggests the potential for nutritional enhancement through selective breeding.

Table 1.

Proximate analysis of Nigella genotypes.

Genotypes	Moisture (%)	Total Ash (%)	Fat (%)	Crude Fiber (%)
AN-1	4.08 ± 0.05^j	4.5 ± 0.17^gh	13 ± 0.41ⁱ⁻ⁿ	2.899 ± 0.02^o
AN-2	5.15 ± 0.06^defg	5.12 ± 0.05^cd	14.249 ± 0.58^b−e	4.25 ± 0.11^hi
AN-4	5.279 ± 0.06^de	4.979 ± 0.04^de	12.559 ± 0.24^l−o	5.6 ± 0.23^a
AN-6	5.359 ± 0.03^d	4.249 ± 0.13^ij	13.65 ± 0.09^e−i	5.239 ± 0.04^bc
AN-7	5.399 ± 0.20^d	5.099 ± 0.06^cd	13.099 ± 0.01^h−m	3.979 ± 0.06^k
AN-8	5.2 ± 0.03^def	4.35 ± 0.06^hi	12.95 ± 0.20^j−n	5.199 ± 0.18^bc
AN-9	5.3 ± 0.09^de	3.65 ± 0.09^m	14.099 ± 0.52^b−f	5.399 ± 0.20^b
AN-10	5.799 ± 0.04^bc	5.1 ± 0.06^cd	14.65 ± 0.05^abc	4.2 ± 0.13^hij
AN-11	6.009 ± 0.10^ab	4.349 ± 0.12^hi	13.95 ± 0.48^def	4.2 ± 0.14^hij
AN-12	6 ± 0.12^ab	4.199 ± 0.16^ij	14.2 ± 0.06^b−e	5.75 ± 0.10^a
AN-13	4.8 ± 0.16^h	4 ± 0.05^kl	13.949 ± 0.45^def	5.649 ± 0.03^a
AN-19	5.299 ± 0.07^de	4.95 ± 0.03^de	12.92 ± 0.08^j−n	4.25 ± 0.09^hi
AN-20	4.25 ± 0.02^j	4.25 ± 0.12^ij	13.25 ± 0.30^g−k	4.56 ± 0.07^f
AN-21	4.2 ± 0.06^j	5.1 ± 0.06^cd	14.02 ± 0.35^c−f	3.98 ± 0.15^k
AN-23	4.799 ± 0.15^h	5.249 ± 0.16^bc	13.65 ± 0.10^e−i	4.01 ± 0.07^jk
AN-24	4.5 ± 0.17ⁱ	4.65 ± 0.19^fg	13.2 ± 0.54^h−l	3.449 ± 0.01^m
AN-27	4.219 ± 0.14^j	4.25 ± 0.14^ij	14.199 ± 0.49^b−e	4.5 ± 0.13^fg
AN-31	4.15 ± 0.00^j	4.299 ± 0.15^hij	13.9 ± 0.24^efg	4.599 ± 0.12^f
AN-32	4.85 ± 0.17^h	5.1 ± 0.04^cd	13.5 ± 0.18^f−j	3.649 ± 0.03^l
AN-35	5.25 ± 0.07^de	5.3 ± 0.13^bc	12.6 ± 0.22^k−n	4.249 ± 0.07^hi
AN-37	3.08 ± 0.08^l	4.249 ± 0.04^ij	13.199 ± 0.04^h−l	4.1 ± 0.04^ijk
NSC-1	3.329 ± 0.13^k	3.979 ± 0.10^kl	14.25 ± 0.60^b−e	4.25 ± 0.11^hi
NSC-2	4.849 ± 0.12^h	4.099 ± 0.09^jk	15.248 ± 0.11^a	5.25 ± 0.18^bc
NSC-5	5.25 ± 0.23^de	5.249 ± 0.02^bc	12.996±0.00^{i-n [}1.3 ± 0.00^p]	4.65 ± 0.11^f
NSC-6	4.979 ± 0.04^fgh	3.85 ± 0.16^l	14.249 ± 0.27^b−e	4.85 ± 0.01^e
NSC-7	6.199 ± 0.03^a	4.25 ± 0.11^ij	13.199 ± 0.45^h−l	3.949 ± 0.15^k
NSC-8	5.25 ± 0.19^de	5.35 ± 0.02^b	11.949 ± 0.53^o	4.099 ± 0.04^ijk
NSC-9	5.649 ± 0.18^c	5.449 ± 0.20^b	12.349 ± 0.06^no	3.749 ± 0.01^l
NSC-10	4.949 ± 0.20^gh	4.249 ± 0.06^ij	14.25 ± 0.14^b−e	2.85 ± 0.04^o
NSC-11	5.099 ± 0.17^efg	4.8 ± 0.20^ef	15.019 ± 0.08^a	3.749 ± 0.05^l
NSC-12	5.25 ± 0.11^de	4.979 ± 0.09^de	14.95 ± 0.11^a	4.249 ± 0.16^hi
NSC-13	5.249 ± 0.01^de	5.399 ± 0.20^b	14.75 ± 0.65^ab	4.079 ± 0.10^ijk
NSC-14	5.75 ± 0.22^c	5.68 ± 0.13^a	13.75 ± 0.10^e−h	4.349 ± 0.06^gh
NSC-15	5.35 ± 0.12^de	5.25 ± 0.13^bc	14.25 ± 0.62^b−e	5.25 ± 0.02^bc
NSC-16	5.85 ± 0.25^bc	4.8 ± 0.07^ef	14.619 ± 0.37^a−d	4.949 ± 0.18^de
NDBC-10	4.249 ± 0.16^j	3.5 ± 0.04^m	12.449 ± 0.36^mno	3.95 ± 0.09^k
Azad Kalonji	4.199 ± 0.00^j	2.8 ± 0.05ⁿ	13.65 ± 0.14^e−i	5.099 ± 0.17^cd
Pant Krishna	5.099 ± 0.01^efg	2.3 ± 0.04^o	12.449 ± 0.49^mno	3.25 ± 0.08ⁿ

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Among the proximate analysis, moisture content varied widely across genotypes, ranging from 3.08 ± 0.08% (AN-37) and 3.329 ± 0.13% (NSC-1) to 6.199 ± 0.03% (NSC-7) and 6.009 ± 0.10% (AN-11). Several accessions, including AN-37, NSC-1, AN-1, AN-20, AN-21, and NDBC-10, exhibited significantly lower moisture levels, reflecting superior post-harvest stability and reduced susceptibility to microbial deterioration. Conversely, genotypes including NSC-7, AN-11, and AN-12 showed comparatively higher moisture levels, likely reflecting genotype specific differences in seed coat structure, hygroscopicity, and water-binding polysaccharide composition. This degree of variation indicates that seed moisture regulation in Nigella is under strong genetic control rather than being environmentally uniform. The literature reported lower moisture content of bean seeds resulted in extended shelf life and ease of transportation and higher moisture of seeds adversely impact the quality of seed, texture, and flavour^29,30. Moisture values reported in other studies on Nigella typically range from 3.8% to 7.0%, indicating that the present range falls within the general variability of this species under different genetic and environmental conditions^28,31. Similar studies in Fenugreek ecotypes resulted in lowest and highest moisture content in the seed samples in Qom (4.95 ± 0.28%) and Nowshahr (7.87 ± 0.55%) ecotypes, respectively³².

Total ash content, representing the mineral fraction, ranged from 2.30 ± 0.04% (Pant Krishna) to 5.68 ± 0.13% (NSC-14), demonstrating more than a two-fold variation among genotypes. However, the reported range of ash content in Nigella seeds is 4.5–7.5%³⁰. In the present work, most accessions ranged 4.0% and 5.3%, indicating generally mineral-rich seed composition. Genotypes such as NSC-14, NSC-9, NSC-13, and NSC-8 exhibited consistently higher ash values, suggesting superior mineral accumulation capacity and enhanced nutritional potential. Though, traditional cultivars, Pant Krishna and Azad Kalonji presented comparatively lower ash contents, reflecting differential nutrient uptake efficiency and seed mineral deposition patterns. This variation highlights strong genotype-dependent regulation of ion transport, storage, and mineral sequestration processes in Nigella seeds.

Seed fat content showed pronounced quantitative variation, ranging from 11.95% (NSC-8) to 15.25% (NSC-2). Most genotypes within the range of 13.0–14.7%, indicating moderate fat accumulation across the population. The present study range for fat contents were lower in comparison to previous reports of Bangladeshi cumin seeds (27.81 to 35.17%), and black cumin of Sudan, Iran, and Turkey seeds^28,33. High-fat genotypes such as NSC-2, NSC-11, NSC-12, NSC-13, AN-10, and NSC-16 represent nutritionally and industrially valuable accessions for edible oil production and nutraceutical applications. In contrast, lower fat content in NSC-8, NSC-9, Pant Krishna, and NDBC-10 suggests metabolite allocation toward non-lipid structural or carbohydrate components. The observed lipid variability confirms that oil biosynthesis in Nigella is a quantitatively inherited trait, controlled by genotype-specific regulation of fatty acid synthesis and carbon partitioning pathways.

Crude fiber content ranged from 2.85% (NSC-10) to 5.75% (AN-12), indicating considerable structural carbohydrate diversity among genotypes. The reported range of crude fiber in black seeds are 5.11–8.60%³⁴. However, the present study had wider variability for crude fiber content. High-fiber genotypes including AN-12, AN-13, AN-4, AN-9, NSC-15, and NSC-2 represent valuable sources for dietary fiber enrichment and functional food development. Conversely, lower fiber levels in AN-1, NSC-10, Pant Krishna, and AN-24 suggest reduced lignocellulosic deposition and thinner seed coat architecture. This wide fiber range reflects genotype specific regulation of cell wall biosynthesis, hemicellulose deposition, and lignification processes. Relatively higher crude fiber (4.7 g/100 g, and 7.11 g/100 g) reported by Ansary et al.³⁵ and Giri et al.³⁶ respectively. Thus inferred, that a high-fiber diet helps fight against several chronic non communicable diseases, coronary heart disease, diabetes, obesity, colon cancer, high blood pressure³⁴.

Total oil yield and bioactive composition of Nigella genotypes

The evaluated Nigella sativa genotypes exhibited pronounced biochemical diversity across total oil yield, total phenols, total flavonoids, total carbohydrate, antioxidant capacity, and thymoquinone accumulation (Table 2). All measured traits differed significantly (P < 0.05), indicating strong genotype-dependent metabolite specialization and biochemical partitioning.

Table 2.

Total oil yield and biochemical composition, antioxidant activity in genotypes of Nigella.

Genotypes	Total oil yield (%)	Total phenols (mg GAE/g seed)	Total Flavonoids (mg QE/mg seed)	Total Carbohydrates (mg/g seed)	Antioxidant activity (%)	Thymoquinone (µg/100 mg seed)
AN-1	14.67 ± 0.48^s	3.552 ± 0.14^hi	6.934 ± 0.21^q	25.27 ± 0.27^de	88.668 ± 0.24^a−h	127.4 ± 5.17^ghi
AN-2	27.763 ± 1.00^bc	2.675 ± 0.10^qr	11.977 ± 0.15^k	26.25 ± 0.76^cd	91.058 ± 2.54^ab	42.88 ± 0.97^v
AN-4	17.967 ± 0.75^q	2.836 ± 0.04^opq	5.144 ± 0.09^r	19.749 ± 0.80^m−p	90.341 ± 1.79^a−d	124.9 ± 0.11^hi
AN-6	15.523 ± 0.04^s	3.433 ± 0.03^ij	5.551 ± 0.09^r	24.25 ± 0.22^efg	86.28 ± 1.87^c−m	42.879 ± 1.78^v
AN-7	21.013 ± 0.02^mn	3.083 ± 0.02^mn	4.085 ± 0.13^s	18.75 ± 0.19^pq	89.147 ± 3.86^a−f	80 ± 1.01^op
AN-8	29.463 ± 0.85^a	2.427 ± 0.02^s	1.32 ± 0.05^u	19.249 ± 0.78^op	88.429 ± 0.56^a−i	145.2 ± 4.32^d
AN-9	21.807 ± 0.48^klm	2.782 ± 0.04^pq	8.153 ± 0.03^op	20.249 ± 0.29^l−o	86.996 ± 3.69^b−m	60.72 ± 2.50^t
AN-10	22.587 ± 0.20^k	2.888 ± 0.04^op	6.527 ± 0.23^q	27.5 ± 0.79^ab	84.13 ± 0.08ⁱ⁻ⁿ	176.3 ± 2.55^b
AN-11	29.393 ± 0.24^a	3.33 ± 0.12^jk	2.947 ± 0.09^t	24.66 ± 0.51^ef	84.846 ± 0.08^f−n	43.95 ± 0.75^v
AN-12	28.383 ± 0.31^b	4.275 ± 0.15^e	26.54 ± 0.22^e	28.5 ± 1.05^a	86.04 ± 1.09^d−m	87.72 ± 2.77^mn
AN-13	23.973 ± 0.61^hi	2.058 ± 0.03^t	19.38 ± 0.24^f	24.25 ± 0.81^efg	82.936 ± 0.30^mno	247.6 ± 1.34^a
AN-19	24.387 ± 0.42^ghi	1.503 ± 0.02^u	5.551 ± 0.19^r	23.649 ± 0.62^fg	84.607 ± 3.13^g−n	244.5 ± 10.36^a
AN-20	21.403 ± 0.91^lm	3.731 ± 0.11^g	4.819 ± 0.20^r	27.249 ± 1.06^bc	90.579 ± 3.24^abc	141.7 ± 2.45^de
AN-21	20.387 ± 0.74^no	2.882 ± 0.10^op	13.28 ± 0.20ⁱ	24.2 ± 0.09^efg	88.19 ± 1.51^a−j	0 ± 0.00
AN-23	19.85 ± 0.25^op	2.527 ± 0.06^rs	32.722 ± 0.74^c	26.24 ± 0.92^cd	87.474 ± 0.71^a−l	155.2 ± 4.06^c
AN-24	24.683 ± 0.29^fgh	3.149 ± 0.08^lmn	34.92 ± 1.35^a	19.5 ± 0.54^nop	86.041 ± 2.25^d−m	69.21 ± 1.56^rs
AN-27	18.983 ± 0.36^p	1.911 ± 0.02^t	29.631 ± 0.13^d	22.399 ± 0.06^hi	84.369 ± 0.46^h−n	0 ± 0.00
AN-31	26.433 ± 0.53^e	2.774 ± 0.03^pq	9.375 ± 0.08^mn	24.349 ± 0.86^efg	88.908 ± 1.92^a−g	55.32 ± 2.19^u
AN-32	23.563 ± 0.28^ij	3.216 ± 0.11^klm	9.862 ± 0.22^m	22.099 ± 0.92^ij	86.757 ± 2.58^b−m	75.61 ± 2.17^pq
AN-35	27.483 ± 0.38^bcd	2.815 ± 0.11^pq	11.245 ± 0.48^l	20.749 ± 0.13^klm	87.952 ± 3.88^a−k	140.7 ± 3.42^de
AN-37	27.357 ± 0.59^cde	2.715 ± 0.00^pq	13.442 ± 0.00ⁱ	18.749 ± 0.37^pq	83.653 ± 1.81^k−o	129.1 ± 1.63^fgh
NSC-2	26.763 ± 0.77^de	2.841 ± 0.07^opq	12.952 ± 0.07ⁱ	20.5 ± 0.06^k−n	89.862 ± 3.97^a−e	131.7 ± 4.04^fg
NSC-1	27.873 ± 0.78^bc	2.755 ± 0.05^pq	12.221 ± 0.43^jk	21.2 ± 0.52^jkl	91.774 ± 0.08^a	127.1 ± 4.47^ghi
NSC-5	29.397 ± 1.22^a	3.003 ± 0.10^no	9.699 ± 0.41^m	24.2 ± 0.50^efg	85.561 ± 0.77^e−m	133 ± 1.92^f
NSC-6	29.93 ± 0.00^a	3.792 ± 0.06^g	9.537 ± 0.25^m	22.1 ± 0.22^ij	87.475 ± 2.52^a−l	96.64 ± 1.48^jk
NSC-7	27.44 ± 0.30^bcd	4.615 ± 0.13^cd	8.723 ± 0.27^no	19.849 ± 0.29^m−p	79.831 ± 0.58^o	90.64 ± 3.76^lm
NSC-8	22.387 ± 0.71^k	4.789 ± 0.14^bc	6.608 ± 0.20^q	17.819 ± 0.24^q	83.652 ± 1.58^k−o	74.179 ± 1.14^qr
NSC-9	22.733 ± 0.35^jk	4.643 ± 0.11^cd	12.709 ± 0.38^ij	23.25 ± 0.42^gh	76.246 ± 3.02^p	144.4 ± 1.30^de
NSC-10	24.957 ± 0.20^fg	4.569 ± 0.17^d	10.676 ± 0.18^l	21.499 ± 0.67^ijk	85.086 ± 3.46^f−n	125.8 ± 2.43^hi
NSC-11	18.987 ± 0.22^p	4.72 ± 0.07^bcd	33.21 ± 0.75^c	24.199 ± 1.07^efg	83.891 ± 1.59^j−o	139.1 ± 2.01^e
NSC-12	16.98 ± 0.35^r	4.556 ± 0.19^d	34.024 ± 1.32^b	22.501 ± 0.24^hi	81.025 ± 2.48^no	124.2 ± 5.15^hi
NSC-13	25.46 ± 1.01^f	5.051 ± 0.21^a	14.987 ± 0.31^h	23.75 ± 0.75^fg	84.13 ± 1.29ⁱ⁻ⁿ	75.379 ± 3.13^pq
NSC-14	10.487 ± 0.05^t	4.053 ± 0.16^f	4.981 ± 0.16^r	24.25 ± 0.26^efg	83.414 ± 1.50^l−o	93.3 ± 3.11^kl
NSC-15	9.98 ± 0.21^t	4.582 ± 0.04^d	17.672 ± 0.62^g	23.679 ± 0.85^fg	85.799 ± 1.93^e−m	74.219 ± 1.74^qr
NSC-16	9.96 ± 0.11^t	4.836 ± 0.15^b	11.164 ± 0.24^l	24.78 ± 0.38^ef	83.653 ± 2.26^k−o	99.769 ± 2.16^j
NDBC-10	20.91 ± 0.43^mn	3.699 ± 0.03^gh	8.725 ± 0.07^no	19.85 ± 0.11^m−p	88.19 ± 1.03^a−j	122.8 ± 2.32ⁱ
Azad Kalonji	22.27 ± 0.18^kl	3.271 ± 0.07^jkl	7.666 ± 0.32^p	21.449 ± 0.72^ijk	91.058 ± 0.41^ab	83.13 ± 0.82^no
Pant Krishna	18.017 ± 0.27^q	3.705 ± 0.02^gh	6.689 ± 0.16^q	26.318 ± 1.07^cd	90.819 ± 3.36^ab	64.31 ± 0.23st

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Values are reported as means ± standard deviation (SD) of three replicate analyses (n = 3), values with the same alphabet (column) is not significantly different at P < 0.05 according to DMRT analysis.

Total oil yield varied widely, ranging from 9.96% (NSC-16) to 29.93% (NSC-6), reflecting nearly a three-fold variation among genotypes. With fat levels exceeding those of corn (10%)³⁷ and soybean (20%), thus, black seed emerges as a promising candidate for oil production. High-oil genotypes such as NSC-6, NSC-5, AN-8, AN-11, AN-12, AN-2, NSC-1, NSC-2, and AN-35 consistently exhibited oil yields above 27%, identifying them as elite oil-bearing accessions. In contrast, genotypes including NSC-14, NSC-15, NSC-16, and AN-1 showed comparatively low oil accumulation (< 15%), indicating divergent metabolite allocation towards non-lipid biochemical pools. Similar study in Bangladesh accessions reported higher crude oil yield (35.17%) for genotype BSK-2074²⁸. In agreement with the present study, significant variation in oil content, among G. platycarpum populations offers strong potential for selection and use in breeding programs. Eghlima et al.¹⁶. As observed in earlier studies substantial variability for phytochemical composition, and nutritional traits among plant genotypes of Vitis viniferaL¹⁵. and in Alcea species³⁸ has been shown to enhance their potential as valuable sources of bioactive and nutritional components for applications in food and related industries. The present study demonstrates a wide range and strong genetic regulation of lipid biosynthesis pathways and carbon partitioning efficiency in Nigella seeds. Such variability reflects differential enzymatic flux through fatty acid biosynthesis, seed storage metabolism, and genotype-specific assimilate allocation strategies.

Total phenolic content

Phenolic compounds are key bioactive constituents of N. sativa seeds and are central to their antioxidant and therapeutic properties. These metabolites, derived from the phenylpropanoid pathway, act as efficient free-radical scavengers and redox regulators, contributing to protection against oxidative stress and associated chronic disorders. Phenolic-rich Nigella extracts have been widely reported for their antioxidant, anti-inflammatory, antimicrobial, antidiabetic, and hepatoprotective activities, highlighting their importance in functional foods and nutraceutical formulations³⁹. As seen from Table 2 the total phenolic content among the Nigella genotypes exhibited substantial quantitative variation, ranging from 1.503 mg GAE/g seed (AN-19) to 5.051 mg GAE/g seed (NSC-13), indicating strong genotype-dependent regulation of phenylpropanoid metabolism. The studies including genotypes of black cumin reported by Saxena et al.⁴⁰ reported total phenolic content ranged from a minimum of 129mgGAE/ml (AN-21) to a maximum of 212mgGAE/ml (AN-3). In the present study most, genotypes grouped within the intermediate range of 2.5–4.5 mg GAE/seed, reflecting moderate phenolic biosynthetic capacity across the germplasm. In contrast, as shown in Table − 2 the released commercial varieties Azad Kalonji and Pant Krishna displayed distinct phenolic profiles relative to the experimental genotypes. Azad Kalonji recorded a phenolic content of 3.271 mg GAE/g seed, positioning it within the mid-to-high phenolic category, whereas Pant Krishna exhibited 3.705 mg GAE/g seed, representing a comparatively elevated phenolic accumulation among released cultivars. Particularly, Pant Krishna surpassed several high-oil experimental genotypes in phenolic concentration, indicating that phenolic biosynthesis in this cultivar is not metabolite compromised by lipid accumulation.

This suggests a balanced carbon allocation strategy, where both primary and secondary metabolic pathways are simultaneously maintained. When compared with elite experimental genotypes such as NSC-13 (5.051 mg GAE/g seed), NSC-8 (4.789 mg GAE/g seed), and NSC-7 (4.615 mg GAE/g seed), the released varieties exhibit moderate phenolic biosynthetic capacity, indicating that current commercial cultivars may not represent the upper genetic potential for phenolic enrichment present within the germplasm pool. Moreover, the TPC of the black cumin was reported higher by Hossain et al.²⁸ than the present study for genotype BSK-2074 (478.07 ± 1.83 mg GAE/100 g) and similar reports by previous researchers^36,41. This variation in black seeds depends on several extraction techniques and solvents, such as 80% methanol with Soxhlet extraction⁴². On the other hand, the ethanol 95% extraction method resulted in the highest contents of TPC, as reported by Al-Bishri⁴³. Further a wide range of phenolic compounds, including gallic acid, rutin, quercetin, kaempferol, and chlorogenic acid, have been reported in other plant species such as Tribulus terrestris, indicating the diverse nature of secondary metabolites in plant populations⁴⁴. Such findings support the variability in phenolic profiles observed among Nigella sativa genotypes in the present study. This differential accumulation pattern reflects genotype-specific regulation of the phenylpropanoid pathway. The higher phenolic levels observed in genotypes suggest stronger constitutive or inducible activation of enzymatic pathways. However, the superior phenolic performance of experimental genotypes such as NSC-13 and NSC-8 highlights the untapped genetic potential within breeding populations for developing next-generation high-phenolic cultivars.

Total flavonoid content

Flavonoids comprise a structurally diverse class of plant secondary metabolites that play essential roles in growth regulation, pigmentation processes, and ecological functions, including plant defense and microbial signaling. Relative to simple phenolic acids, flavonoids generally exhibit stronger antioxidant efficiency due to their complex molecular structures and multiple reactive functional groups^45,46. Compounds such as quercetin, which contain several hydroxyl moieties, display enhanced free-radical scavenging capacity compared to less substituted phenolics. In a broader biological context, polyphenolic compounds including both flavonoids and phenolic acids are recognized as powerful antioxidants that contribute to oxidative stress control, suppression of lipid peroxidation, and modulation of inflammatory responses, reinforcing their significance in plant physiology and human health^47,48. Total flavonoid content among the N. sativa genotypes varied significantly (P < 0.05), ranging from 1.32 mg QE/mg seed (AN-8) to 34.92 mg QE/mg seed (AN-24). High-flavonoid genotypes (29 mg QE/mg seed) included AN-24, NSC-12, NSC-11, AN-23, AN-27, and AN-12. Intermediate levels (10–20 mg QE/mg seed) were observed in AN-21, AN-37, NSC-2, NSC-1, NSC-9, NSC-10, NSC-13, NSC-15, and AN-35. In contrast, AN-8, AN-7, AN-11, AN-4, AN-20, NSC-14, and Pant Krishna showed low flavonoid accumulation (< 7 mg QE/mg seed), forming a low-flavonoid group. Similar variability studies of Bangladeshi ecotypes for flavonoid were reported in Nigella genotype BSK-2074 contained significantly higher TFC (284.34 ± 2.08 mg QE/100 g) than the local Kalozira²⁸, 378 ± 4.20 mg QE/100 g reported by Thomas et al.⁴⁹.

Antioxidant activity of Nigella sativa genotypes

The free-radical scavenging potential of Nigella genotypes was assessed using the DPPH (2,2-diphenyl-1-picrylhydrazyl) assay, and the results are summarized in Table 2. Antioxidant activity differed significantly among genotypes (P < 0.05), with values ranging from 76.246 ± 3.02% (NSC-9) to 91.774 ± 0.08% (NSC-1), indicating substantial genetic variability in radical-quenching efficiency. Genotypes such as NSC-1, AN-2, Azad Kalonji, Pant Krishna, and AN-20 exhibited comparatively higher inhibition activity (90%), forming a high-antioxidant group, whereas NSC-9, NSC-7, and NSC-12 recorded relatively lower scavenging capacity. Similar studies on Nigella genotype BKS-2074 had significantly highest inhibition activity compared to the activities of the local Kalozira and BSK-2081²⁸. Antioxidant assay in black cumin genotypes reported total antioxidant content of maximum in AN-4 (14.8mgBHTE/ml) and minimum in AN-6 (2.4mgBHTE/ml) with an average of 7.5mgBHT E/ml⁴⁰. The DPPH assay is based on the reduction of a stable nitrogen-centered free radical, which undergoes a visible color transition upon interaction with antioxidant compounds. The antioxidant action of plant-derived phytochemicals primarily occurs through hydrogen atom transfer (HAT) and single electron transfer (SET) mechanisms, enabling the neutralization of free radicals either by hydrogen donation or electron transfer, respectively^50,51. The observed genotype-dependent variation in antioxidant activity reflects differences in the biochemical composition of the seeds, particularly the combined and synergistic effects of phenolics, flavonoids, and other redox-active metabolites. Similar variability in antioxidant potential among Nigella genotypes and comparatively lower radical-scavenging values in certain black cumin accessions have also been reported previously⁵².

Total carbohydrates of Nigella sativa genotypes

Total carbohydrate content of the Nigella sativa genotypes showed significant variation (P < 0.05), as presented in Table 2, indicating pronounced genotype-dependent differences in carbon storage and metabolite allocation. Carbohydrate levels ranged from 17.819 ± 0.24 mg/g (NSC-8) to 28.5 ± 1.05 mg/g (AN-12), reflecting substantial diversity in reserve carbohydrate accumulation among the evaluated genotypes. Similar results were reported with total carbohydrates ranged from 25.32 to 35.23% in black cumin^53,54. Higher carbohydrate concentrations were observed in AN-12, AN-10, AN-20, Pant Krishna, and AN-2, forming a high-carbohydrate group, whereas relatively lower levels were recorded in NSC-8, AN-7, AN-37, AN-24, and NSC-7.

Energy distribution and nutritional efficiency of Nigella genotypes

As observed in Fig. 1, limited variation in total energy values was recorded across Nigella genotypes, indicating a relatively conserved caloric density despite biochemical diversity. In contrast, the carbohydrate/fat (CHO/Fat) ratio exhibited moderate genotypic variation, reflecting differences in metabolite allocation between lipid and carbohydrate pools. Most genotypes were lipid-dominant (low CHO/Fat), consistent with the oil-rich nature of Nigella sativa. However, variations in CHO/Fat ratio among genotypes remained within a biologically acceptable range and did not indicate a pronounced shift toward carbohydrate-dominated energy storage. Released varieties (Azad Kalonji and Pant Krishna) displayed intermediate values, suggesting a relatively balanced nutritional composition. Overall, the results indicate that Nigella genotypes maintain stable energy content while exhibiting variation in macronutrient partitioning, likely governed by genetic regulation of carbon flux between fatty acid biosynthesis and carbohydrate metabolism^55,56. Lipid-dominant genotypes may be advantageous for high-energy and nutraceutical applications, whereas genotypes with relatively higher carbohydrate proportions could be useful in functional food formulations, highlighting the breeding potential of this metabolite diversity.

Thymoquinone content

Thymoquinone, an active metabolite in the seed, is an antioxidant and an excellent anti-inflammatory. It has recently emerged as a potential candidate in the battle against oxidative stress and chronic disorders¹⁴. Thymoquinone accumulation among the N. sativa genotypes exhibited pronounced quantitative variation and statistically significant differences (P < 0.05), as presented in Table 2, indicating strong genotype-dependent regulation of quinone biosynthesis. Thymoquinone concentration among the studied germplasm varied from undetectable levels in AN-21 and AN-27 to 247.6 ± 1.34 µg/100 mg seed in AN-13, reflecting pronounced chemotypic diversity. These quantitative profiles were previously documented in our earlier study Ravi et al.²⁷ and are referenced here to facilitate comparison with the additional biochemical analyses presented in the current manuscript. High-thymoquinone genotypes, including AN-13, AN-19, AN-10, AN-23, AN-8, NSC-9, and NSC-5, formed a distinct elite medicinal group characterized by enhanced quinone biosynthetic capacity. In contrast, genotypes with negligible or undetectable thymoquinone levels represent low-thymoquinone chemotypes. Similar studies on quantification of the thymoquinone content in the samples varied between 0.5% and 4.9% in Nigella genotypes as reported by Telci et al.⁵⁷. The thymoquinone was found between 0.45% and 4.57% in the cold-pressed seed oil of the N. sativa genotypes with thymoquinone yields per hectare were between 1.24 and 18.41 kg/ha.

Phenolic, flavonoid, and thymoquinone index

The phenolic density, flavonoid density, and thymoquinone index varied markedly among Nigella genotypes (Fig. 2), indicating strong genetic regulation of secondary metabolite biosynthesis. Phenolic density ranged approximately from ~ 2.5 to 11 mg g⁻¹ oil, with several AN and NSC genotypes forming a high-phenolic group, while others showed consistently low accumulation. Flavonoid density showed narrower variation (~ 0.6–2.8 mg g⁻¹ oil), with specific genotypes (e.g., selected AN and NSC lines) exhibiting distinct enrichment. The thymoquinone index ranged from ~ 0.3 to ~ 1.6 mg g⁻¹ oil, with a few genotypes showing clear supremacy, reflecting specialized quinone biosynthesis. Released varieties showed intermediate, stable values across all three traits, suggesting balanced metabolite profiles rather than extreme accumulation. The coordinated elevation of phenolics, flavonoids, and thymoquinone in select genotypes indicates convergence of antioxidant and bioactive pathways, consistent with earlier reports on genetically driven variation in polyphenol–thymoquinone biosynthesis in N. sativa^58,59.

GC–MS profiling and fatty acid composition

GC–MS analysis revealed a complex lipid and volatile profile across N. sativa genotypes, with clear dominance of nutritionally important fatty acids (Table 3). The major constituents across genotypes were linoleic acid (C18:2, n-6), oleic acid (C18:1, n-9), palmitic acid (C16:0), and stearic acid (C18:0), confirming that Nigella seed oil is characteristically rich in unsaturated fatty acids. This compositional pattern is in strong agreement with earlier reports on black cumin oil by previous researches^55,60. Among these, linoleic acid was the dominant fatty acid in all genotypes, reaching very high proportions in several accessions (Table 3), confirming its status as the principal polyunsaturated fatty acid in black cumin oil. Similar variability in major fatty acids, including linoleic acid, has been reported in Rosa canina L.^61. supporting the diversity in fatty acid profiles observed in the present study. The prevalence of linoleic acid highlights the essential fatty acid (EFA) value of Nigella seeds, since linoleic acid cannot be synthesized by humans and must be supplied through the diet⁶². Oleic acid, the principal MUFA, was the second major unsaturated fatty acid across genotypes (Table 3). Oleic acid is widely recognized for its cardioprotective role through lowering LDL cholesterol and improving lipid metabolism, and its presence as a dominant MUFA in Nigella oil has been consistently reported Iqbal et al.⁵⁶. In contrast, saturated fatty acids (SFA) such as palmitic acid and stearic acid were present at lower proportions. Palmitic acid, although the major SFA, remained relatively moderate across genotypes (Table 3), which is nutritionally favorable, as excessive palmitic acid intake is associated with increased LDL cholesterol and cardiovascular risk. Similar palmitic and stearic acid ranges in Nigella oil were reported by Gharby et al.⁶³. Several minor fatty acids, including myristic acid (C14:0), arachidic acid (C20:0), palmitoleic acid (C16:1), eicosadienoic acid (C20:2, n-6), and t-vaccenic acid, were detected at low concentrations (Table 3). The presence of these minor components is consistent with earlier compositional studies of Nigella seed oil^60,64, reflecting genetic and metabolite diversity among genotypes. Additionally, volatile compounds such as p-cymene and longifolene were identified, contributing to the characteristic aroma profile and potential bioactivity of Nigella seeds, as also reported in earlier GC–MS studies.

Table 3.

GC-MS composition (%) of Nigella genotypes.

Genotypes	Arachidic acid	Linoleic acid	Longifolene	Myristic acid	Oleic acid	Palmitic acid	Palmitoleic acid	Stearic acid	Eicosadienoic acid	p-Cymene	t-Vaccenic acid
AN-1	0.115^lm	2.314^g−k	59.43^i−l	0.059^hi	0.167^ab	2.324^a	0.054^pq	12.349^efg	0.181^cde	2.189^ijk	20.817^g
AN-2	0.114^lm	1.928st	69.71^a	0.056^ij	0.12^nop	0.508^m	0.334^d	11.076^klm	0.138^l	2.182^i−l	13.834^q
AN-4	0.111^mn	2.135^n−q	67.095^abc	0.071^f	0.135^f−j	1.202^e	0.038^t	10.601^mno	0.138^l	2.015^nop	16.459^mn
AN-6	0.131^fgh	2.297^h−k	62.189^e−j	0.046^mn	0.133^g−k	0.451^no	0.046^rs	12.494^ef	0.186^bcd	2.325^def	19.702^h−k
AN-7	0.128^f−i	2.416^d−h	62.807^d−g	0.055^jk	0.133^g−k	0.286^s	0.078ⁿ	12.77^cde	0.191^b	2.3^e−h	18.836^kl
AN-8	0.133^f	2.273^i−l	63.03^d−g	0.14^b	0.138^e−h	0.449^no	0.216ⁱ	11.268^jkl	0.168^g	2.014^nop	20.17^ghi
AN-9	0.153^b	2.303^h−k	62.72^d−h	0.042^o	0.145^cd	0.308^rs	0.11^l	12.759^cde	0.171^fg	2.42^cd	18.87^kl
AN-10	0.127^ghi	2.46^c−f	56.627^lm	0.068^fg	0.163^b	2.195^b	0.206^j	12.56^def	0.204^a	2.202^hij	23.186^cd
AN-11	0.159^a	2.589^b	59.057^jkl	0.036^qr	0.131^i−l	0.348^qr	0.117^l	12.562^def	0.185^bcd	2.363^de	22.452^def
AN-12	0.14^e	2.376^f−i	58.5^kl	0.119^c	0.133^g−k	0.966^g	0.357^c	12.432^ef	0.207^a	2.214^g−j	22.555^def
AN-13	0.13^fgh	2.39^e−i	59.554^h−l	0.181^a	0.137^e−i	1.029^f	0.312^ef	11.541^ijk	0.185^bcd	2.303^e−h	22.237^def
AN-19	0.133^f	2.531^bcd	54.585^m	0.052^kl	0.145^cd	0.961^g	0.195^k	12.532^ef	0.188^bc	2.252^f−j	26.426^a
AN-20	0.098^o	1.982^rs	58.875^jkl	0.055^jk	0.071^s	0.307^rs	0.115^l	10.374^no	0.058ⁿ	2.149^j−m	25.916^ab
AN-21	0.127^ghi	2.273^i−l	64.657^c−f	0.082^e	0.126^lm	0.933^g	0.233^h	10.229^o	0.165^gh	2.042^nop	19.133^jkl
AN-23	0.126^hij	2.15^m−q	69.321^a	0.034^rs	0.128^klm	0.515^lm	0.064^o	10.89^lmn	0.158ⁱ	2.007^nop	14.606^pq
AN-24	0.099^o	2.211^k−o	62.096^e−j	0.103^d	0.169^a	2.169^b	0.307^f	11.272^jkl	0.169^g	2.018^nop	19.388^ijk
AN-27	0.118^kl	2.101^opq	65.821^bcd	0.046^mn	0.124^mn	0.634ⁱ	0.005^w	10.831^lmn	0.168^g	1.875^r	18.278^l
AN-31	0.117^kl	2.107^n−q	64.812^cde	0.059^hi	0.118^opq	0.563^kl	0.022^u	11.267^jkl	0.167^g	1.902^qr	18.865^kl
AN-32	0.143^de	2.562^bc	58.991^jkl	0.068^fg	0.141^c−f	1.896^c	0.218ⁱ	11.781^hij	0.169^g	2.186^i−l	21.844^f
AN-35	0.145^cde	2.498^b−e	58.253^kl	0.044^no	0.106^r	0.147^u	0.073ⁿ	13.567^b	0.15^jk	2.313^efg	22.702^def
AN-37	0.128^f−i	2.152^l−q	65.849^bcd	0.032st	0.107^r	0.094^vw	0.055^pq	10.641^mno	0.16^hi	1.982^n−q	18.799^kl
NSC-1	0.149^bc	2.369^f−i	59.376^i−l	0.038^pq	0.139^d−g	0.121^uv	0.048^qr	12.225^e−h	0.188^bc	2.421^cd	22.925^de
NSC-2	0.152^b	2.355^f−i	57.049^lm	0.049^lm	0.143^cde	0.138^uv	0.316^e	13.154^bc	0.19^b	2.519^b	23.936^c
NSC-5	0.142^de	2.733^a	60.989^g−k	0.019^u	0.141^c−f	0.615^ij	0.016^uv	15.172^a	0.203^a	2.655^a	17.315^m
NSC-6	0.132^fg	2.176^l−o	68.426^ab	0.033^rst	0.132^h−l	0.459ⁿ	0.065^o	11.13^klm	0.17^g	2.025^nop	15.251^op
NSC-7	0.124^ij	2.161^l−p	69.759^a	0.034^rs	0.106^r	0.391^pq	0.011^vw	10.882^lmn	0.145^k	2.084^l−o	14.304^q
NSC-8	0.143^de	2.457^c−f	62.887^d−g	0.046^mn	0.145^cd	0.21^t	0.039st	12.227^e−h	0.185^bcd	2.275^e−i	19.385^ijk
NSC-9	0.121^jk	2.428^d−g	59.466^i−l	0.067^g	0.147^c	1.33^d	0.057^op	11.801^g−j	0.188^bc	2.089^k−n	22.307^def
NSC-10	0.121^jk	2.269^i−m	62.452^e−i	0.062^h	0.137^e−i	1.167^e	0.032^t	11.515^jk	0.179^de	1.977^opq	20.088^g−j
NSC-11	0.127^ghi	2.38^e−i	59.118^jkl	0.059^hi	0.13^jkl	0.844^h	0.406^a	12.097^f−i	0.177^ef	2.057^mno	22.605^def
NSC-12	0.124^ij	2.122^n−q	66.918^abc	0.044^no	0.117^pq	0.557^kl	0.297^g	10.751^l−o	0.156^ij	1.942^pqr	16.972^m
NSC-13	0.113^lm	2.049^pqr	68.027^ab	0.03^t	0.104^r	0.405^op	0.291^g	10.713^l−o	0.129^m	2.168^jkl	15.971^no
NSC-14	0.146^cd	2.366^f−i	59.411^i−l	0.084^e	0.162^b	0.066^w	0.199^jk	13.123^bcd	0.185^bcd	2.322^def	21.937^ef
NSC-15	0.128^f−i	2.338^f−j	60.885^g−k	0.069^fg	0.136^f−j	0.479^mn	0.379^b	12.66^c−f	0.181^cde	2.252^f−j	20.493^gh
NSC-16	0.117^kl	2.037^qr	67.479^abc	0.049^lm	0.123^mno	0.578^jk	0.339^d	11.114^klm	0.156^ij	2.021^nop	15.988^no
NDBC-10	0.131^fgh	2.226^j−n	61.503^f−k	0.041^op	0.135^f−j	0.089^vw	0.059^op	12.333^e−h	0.166^gh	2.466^bc	20.851^g
Azad Kalonji	0.114^lm	2.202^k−o	57.071^lm	0.061^h	0.135^f−j	0.573^jk	0.094^m	12.165^fgh	0.168^g	2.314^efg	25.103^b
Pant Krishna	0.107ⁿ	1.863^t	62.134^e−j	0.053^jk	0.113^q	0.199^t	0.087^m	10.593^mno	0.135^lm	2.012^nop	22.705^def

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Values are reported as mean of three replicate analysis (n = 3); values with the same alphabet (column) are not significantly different at P < 0.05 according to DMRT analysis.

Fatty acid classes and nutritional utility ratios

Class-wise fatty acid analysis (Table 4) showed that PUFA were the predominant fraction in all genotypes, followed by MUFA and SFA, confirming the highly unsaturated nature of Nigella seed oil. Similar PUFA-dominant lipid patterns have been consistently reported in Nigella oils. The PUFA/SFA ratio ranged from 3.52 to 5.45 (Table 4), indicating a highly favorable lipid quality profile. According to nutritional lipid quality standards, higher PUFA/SFA ratios are associated with reduced risk of cardiovascular disease and improved lipid metabolism. Comparable PUFA/SFA ratios in Nigella oils have been reported by Iqbal et al.⁵⁷ and Saxena et al.⁴⁰, supporting the health relevance of these values. Further, Palmitic acid (34.12–49.15%) and linoleic acid (18.4–29.12%) were the dominant fatty acids in T. erecta and T. petula⁶⁵. The MUFA/SFA (M/S) and PUFA/SFA (P/S) ratios, along with the combined (MUFA+PUFA)/SFA (M + P/S) index (Table 4), further demonstrated the dominance of unsaturated over saturated fatty acids across genotypes. High M + P/S values (4.52–6.95) reflect a nutritionally superior lipid composition, as such ratios are widely used indicators of dietary fat quality and cardiovascular safety.

Table 4.

Fatty acid composition (%) and nutritional utility ratios in Nigella genotypes.

Genotypes	SFA	MUFA	PUFA	PUFA/SFA	M/S	P/S	M + P/S
AN-1	14.82^e−h	23.32^cd	61.74^hij	4.16^d−g	1.57^f−j	4.16^d−g	5.74^i−l
AN-2	13.49^m−p	14.48^o	71.63^a	5.31^a	1.07^tu	5.31^a	6.38^d−g
AN-4	12.86^pq	17.79^l	69.23^abc	5.38^a	1.38^op	5.38^a	6.77^abc
AN-6	15.08^efg	20.33^hi	64.48^e−h	4.27^def	1.34^p	4.27^def	5.62^klm
AN-7	15.33^def	19.31^jk	65.22^def	4.25^def	1.26^qr	4.25^def	5.51^l−o
AN-8	13.55^l−o	20.78^gh	65.30^def	4.81^c	1.53^h−l	4.81^c	6.35^efg
AN-9	15.47^cde	19.34^jk	65.02^d−g	4.20^d−g	1.25^r	4.20^d−g	5.45^l−o
AN-10	15.05^efg	25.58^b	59.08^jk	3.92^g−j	1.70^cd	3.92^g−j	5.62^klm
AN-11	15.21^def	22.98^de	61.64^hij	4.05^f−i	1.51^i−m	4.05^f−i	5.56^k−n
AN-12	14.91^efg	23.72^cd	60.87^ij	4.08^e−h	1.59^e−i	4.08^e−h	5.67^j−m
AN-13	14.11^j−m	23.45^cd	61.94^g−j	4.39^de	1.66^def	4.39^de	6.05^ghi
AN-19	15.06^efg	27.57^a	57.11^k	3.79^h−k	1.83^b	3.79^h−k	5.62^klm
AN-20	12.69^q	26.28^b	60.85^ij	4.79^c	2.07^a	4.79^c	6.87^ab
AN-21	12.52^q	20.23^hij	66.93^cde	5.34^a	1.61^d−h	5.34^a	6.95^a
AN-23	13.15^n−q	15.27^no	71.47^a	5.44^a	1.16^s	5.44^a	6.60^a−f
AN-24	13.55^l−o	21.72^fg	64.30^e−h	4.74^c	1.60^e−i	4.74^c	6.35^efg
AN-27	12.94^opq	19.08^k	67.92^bcd	5.24^ab	1.47^k−o	5.24^ab	6.72^a−d
AN-31	13.40^nop	19.59^ijk	66.91^cde	4.99^bc	1.46^l−o	4.99^bc	6.45^c−f
AN-32	14.25^h−k	23.90^cd	61.55^hij	4.31^def	1.67^cde	4.31^def	5.99^hij
AN-35	16.13^b	22.99^de	60.75^ij	3.76^ijk	1.42^m−p	3.76^ijk	5.19^o
AN-37	12.85^pq	19.05^k	68.00^bcd	5.29^a	1.48^j−n	5.29^a	6.77^abc
NSC-1	14.93^efg	23.23^d	61.74^hij	4.13^d−g	1.55^g−k	4.13^d−g	5.69^i−m
NSC-2	15.96^bc	24.26^c	59.40^jk	3.72^jk	1.51^i−l	3.72^jk	5.23^no
NSC-5	18.11^a	18.13^l	63.72^e−i	3.52^k	1.00^u	3.52^k	4.52^p
NSC-6	13.41^nop	15.88^mn	70.60^ab	5.26^ab	1.18^rs	5.26^ab	6.44^c−f
NSC-7	13.19^n−q	14.84^o	71.92^a	5.45^a	1.12st	5.45^a	6.58^b−f
NSC-8	14.79^e−i	19.78^ijk	65.34^def	4.42^d	1.33^pq	4.42^d	5.76^i−l
NSC-9	14.15^i−l	23.82^cd	61.89^g−j	4.37^de	1.68^cde	4.37^de	6.05^ghi
NSC-10	13.75^k−n	21.43^fg	64.72^e−h	4.70^c	1.55^g−k	4.70^c	6.26^fgh
NSC-11	14.41^g−j	23.62^cd	61.49^hij	4.26^def	1.63^d−g	4.26^def	5.90^ijk
NSC-12	12.93^opq	17.68^l	69.04^abc	5.33^a	1.36^p	5.33^a	6.70^a−e
NSC-13	13.09^n−q	16.50^m	70.07^ab	5.35^a	1.26^qr	5.35^a	6.61^a−f
NSC-14	15.75^bcd	22.18^ef	61.77^g−j	3.92^g−j	1.40^nop	3.92^g−j	5.32^mno
NSC-15	15.17^def	21.15^gh	63.22^f−i	4.16^d−g	1.39^nop	4.16^d−g	5.56^k−n
NSC-16	13.37^nop	16.72^m	69.51^abc	5.19^ab	1.25^r	5.19^ab	6.44^c−f
NDBC-10	15.06^efg	21.10^gh	63.72^e−i	4.23^d−g	1.40^nop	4.23^d−g	5.63^klm
Azad Kalonji	14.72^f−j	25.84^b	59.27^jk	4.02^f−i	1.75^bc	4.02^f−i	5.78^i−l
Pant Krishna	12.82^pq	23.03^de	63.99^e−i	4.99^bc	1.79^b	4.99^bc	6.78^abc

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Values are reported as mean of three replicate analysis (n = 3); values with the same alphabet (column) are not significantly different at P < 0.05 according to DMRT analysis. NOTE: SFA: Saturated fatty acids; MUFA: Monounsaturated fatty acids; PUFA: Polyunsaturated fatty acids; PUFA/SFA: Ratio of polyunsaturated to saturated fatty acids; M/S: Ratio of monounsaturated to saturated fatty acids; P/S: Ratio of polyunsaturated to saturated fatty acids; M+P/S: Ratio of (monounsaturated + polyunsaturated fatty acids) to saturated fatty acids.

Biological and nutritional significance revealed the dominance of PUFA (especially linoleic acid) and substantial MUFA (oleic acid) content, together with low SFA levels, indicates N. sativa genotypes as a nutritionally valuable oilseed crop. Linoleic acid plays a critical role in membrane structure, eicosanoid synthesis, and metabolic regulation, while oleic acid contributes to lipid stability and cardio protection. The fatty acid patterns and nutritional indices observed in this study (Tables 3 and 4) are highly consistent with earlier comprehensive studies on Nigella seed oil composition^39,63,64. Minor variations among genotypes can be attributed to genetic diversity, environmental conditions, seed maturity, and analytical extraction methods, as widely recognized in oilseed lipid research.

Heatmap cluster analysis of fatty acid profiles of Nigella genotypes

The hierarchical heatmap analysis of GC-MS metabolites were studied among the genotypes of Nigella and revealed clear biochemical stratification of Nigella genotypes based on fatty acid methyl esters (FAMEs) and selected bioactive/volatile metabolites, forming distinct chemotypic groups (Fig. 3a, b). Genotypes were primarily separated into AN, NSC, and other classes, indicating strong genotype-dependent metabolite structuring. In Fig. 3a, the class-level heatmap shows that the AN group is characterized by higher standardized intensities of nutritionally important unsaturated fatty acids, particularly linoleic acid, oleic acid, and cis-eicosadienoic acid, along with moderate contributions of palmitic and stearic acids, reflecting a PUFA-enriched oil profile. The NSC group exhibits comparatively lower signals of saturated fatty acids (myristic, palmitic, stearic and arachidic acids) but balanced MUFA and PUFA levels, suggesting a more favorable unsaturation pattern from a nutritional perspective. In contrast, the “Other” group is distinguished by relatively higher contributions of secondary/volatile metabolites such as p-cymene and longifolene, indicating a chemotype oriented more towards aromatic and bioactive constituents than lipid dominance. Earlier report of El Caid et al.⁶⁶ have shown with multivariate analyses identifying superior genotype–environment combinations. In saffron significant ecotype and environment-driven variation has been reported, highlighting strong genotype–environment interactions. This supports the variability observed among Nigella sativa genotypes in the present study.

The genotype-wise clustering in Fig. 3b further resolves clear sub-clusters within AN and NSC groups, demonstrating substantial intra-group biochemical heterogeneity. Several AN genotypes cluster tightly due to consistently higher linoleic and oleic acid levels, whereas subsets of NSC genotypes group based on moderate MUFA dominance and relatively lower SFA content. This confirms that metabolite differentiation is not only class-dependent but also strongly genotype-specific, producing distinct biochemical fingerprints among accessions. Biologically, the clustering patterns reflect genetically regulated lipid metabolism, particularly variation in fatty acid desaturation and elongation pathways controlling SFA→MUFA→PUFA conversion. The PUFA-rich AN chemotype suggests enhanced oleate and linoleate biosynthesis, consistent with previous reports on Nigella seed oil where linoleic and oleic acids dominate the fatty acid profile⁶⁰. The separation of volatile-rich genotypes in the other group further highlights the dual metabolite nature of Nigella, where fatty acid biosynthesis and secondary metabolite/terpenoid pathways operate under partially independent genetic control⁵⁵. Overall, this biochemical structuring is highly relevant for nutritional quality improvement, functional food development, and breeding programs, enabling targeted selection of genotypes with superior fatty acid composition and/or enhanced bioactive metabolite profiles.

Principal component analysis

Principal Component Analysis (PCA) was applied to integrate GC–MS fatty acid composition, volatile constituents, phenolic density, flavonoid density, thymoquinone index, antioxidant traits, and nutritional lipid ratios across Nigella sativa genotypes. PCA efficiently reduced data dimensionality while preserving biologically meaningful variance, enabling discrimination of genotypes based on metabolite and functional attributes.

PCA score plot (individuals PCA)

The PCA score plot revealed distinct clustering of genotypes, reflecting strong biochemical differentiation driven by lipid composition and bioactive metabolites (Fig. 4). The first two principal components (PC1 and PC2) explained the majority of variance, indicating that a limited number of biochemical traits account for most metabolite diversity. Genotypes positioned at the extremes of PC1 and PC2 represented chemically specialized profiles, particularly those enriched in polyunsaturated fatty acids (PUFA), volatile bioactives (p-cymene, longifolene), and antioxidant-related metabolites. Similar genotype-level biochemical segregation in Nigella sativa based on fatty acid and phytochemical composition has been reported earlier^56,64. Variable PCA indicated that linoleic acid, oleic acid, trans-vaccenic acid, p-cymene, longifolene, thymoquinone index, phenolic density, flavonoid density, and PUFA/SFA ratios contributed most moderately to PC1, demonstrating that lipid unsaturation and antioxidant phytochemicals are the dominant drivers of metabolite variation. Saturated fatty acids (palmitic, stearic, myristic, arachidic acids) loaded mainly on PC2, representing a secondary axis of structural lipid variation. This pattern agrees with previous reports showing that unsaturated fatty acids and bioactive volatiles are the principal discriminating metabolites in Nigella oil genotypes.

Fig. 4 — PCA analysis in oils of *Nigella* genotypes: (1) Scree plot, (2) variable, (3) individual PCA, and (4) biplot analysis.

Genotype distribution and PCA biplot

Individual PCA confirmed the presence of genotypes with superior metabolites, characterized by high PUFA content, elevated phenolic and flavonoid density, and increased thymoquinone index. These genotypes represent nutraceutical-rich chemotypes, while centrally clustered genotypes showed balanced metabolite profiles. This distribution reflects genetically controlled biochemical specialization, which has been widely documented in N. sativa germplasm⁴⁰. The PCA biplot demonstrated strong positive correlations between unsaturated fatty acids (linoleic and oleic acids), antioxidant phytochemicals (phenolics, flavonoids, thymoquinone), and volatile compounds (p-cymene, longifolene). Genotypes aligned with these vectors exhibited enhanced nutritional quality, antioxidant potential, and therapeutic relevance. Conversely, genotypes associated with saturated fatty acid vectors showed comparatively lower nutraceutical value. This integrative metabolite association supports the concept that lipid unsaturation, antioxidant systems, and volatile bioactives act synergistically in defining the functional quality of Nigella seeds⁶³.

Overall, PCA reveals that the biochemical diversity of Nigella sativa is structured primarily by fatty acid unsaturation profiles, antioxidant phytochemicals, and volatile bioactive compounds, rather than by metabolites. The strong agreement between PCA patterns and clustering analysis (Fig. 3a, b) validates the robustness of metabolite differentiation. These findings indicate the existence of genotype-specific metabolite architectures integrating nutritional quality (high PUFA/SFA and MUFA/SFA ratios), functional lipids, and pharmacologically active compounds, confirming the potential of selected genotypes for nutraceutical breeding, functional food development, and medicinal applications. Similar integrative metabolite structuring has been reported in previous Nigella chemo profiling and FAME studies⁵⁵. This study was conducted under specific agro-climatic (semi-arid eastern plains of India) conditions. Metabolite accumulation in seed spices is influenced by environmental factors, genotype × environment interactions and seasonal variation. Therefore, multi-location and multi-season evaluation may require confirming the stability of metabolite profiles.

Conclusion

The present study demonstrates substantial variation among N. sativa genotypes in terms of nutrient composition, oil quality, and accumulation of bioactive compounds. The predominance of unsaturated fatty acids, along with high antioxidant potential and favourable nutritional characteristics, highlights the value of black cumin as a functional oilseed crop. The integrated analysis of chemical composition and metabolite diversity revealed distinct genotype-specific profiles, enabling the identification of promising genotypes with superior nutritional and bioactive attributes. These findings provide a strong foundation for the selection of elite genotypes and support their potential use in nutraceutical development, functional food applications, and crop improvement programs.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(143.4KB, docx)}

Acknowledgements

The authors gratefully acknowledge HC&RI, TNAU, Coimbatore, Director, ICAR- NRCSS, Ajmer, for providing research facilities, and support to conduct the study.

Author contributions

**RY, PIV & SNS: ** Conceptualized the study and conducted the experiments, data curation, formal analysis, methodology and original draft writing and editing. **NA, SC & KT: ** Helped in conducting the experiment and data collection. **GNM, PY, AKV, CBH & RS: ** writing, review, data analysis and editing of manuscript. **PD, NKM, RSM: ** Writing, review and editing of manuscript. **PD, VB & GNM: ** Statistical analysis of data and draft editing.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-from-profit sectors.

Data availability

The article and its supplementary materials contain all the data supporting the findings of this study. Data can be made available upon reasonable request to the first author.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

There is no use of any animals or human beings in the present research work.

Consent for publication

All authors have approved for publication.

Footnotes

Publisher’s note

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

Contributor Information

Y. Ravi, Email: ry.davanagere@gmail.com

Manjesh Guligenahalli Narayanappa, Email: gn.manjesh5@gmail.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(143.4KB, docx)}

Data Availability Statement

The article and its supplementary materials contain all the data supporting the findings of this study. Data can be made available upon reasonable request to the first author.

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[CR61] 61.Bakhtiar, Z. et al. Quantification of fatty acids in seed oil and important bioactive compounds in Iranian Rosa canina L. ecotypes for potential cosmetic and medicinal uses. Sci. Rep.13, 22721. 10.1038/s41598-023-50135-y (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Comparative analysis of phytochemical traits, proximate composition, and metabolite diversity in Nigella sativa L. genotypes from India

Y Ravi

P I Vethamoni

S N Saxena

N Ashoka

S Choudhary

Kailashpati Tripathi

P Yadav

A K Verma

C B Harisha

P Dhamotharan

R Singh

N K Meena

R S Meena

Vinay Bhardwaj

Manjesh Guligenahalli Narayanappa

Abstract

Supplementary Information

Introduction

Materials and methods

Plant material and chemicals

Proximate composition analysis

Moisture content

Total ash

Fat content

Crude fibre

Total oil extraction and its yield

Total phenolic content

Total flavonoid content

Antioxidant activity

Total carbohydrates

Nutritional efficiency indices

Carbohydrate efficiency index (CEI)

Fat efficiency index (FEI)

Energy distribution ratio (EDR)

Estimation of thymoquinone

FAME analysis

Statistical analysis

Results and discussion

Proximate composition of Nigella genotypes

Table 1.

Total oil yield and bioactive composition of Nigella genotypes

Table 2.

Total phenolic content

Total flavonoid content

Antioxidant activity of Nigella sativa genotypes

Total carbohydrates of Nigella sativa genotypes

Energy distribution and nutritional efficiency of Nigella genotypes

Fig. 1.

Thymoquinone content

Phenolic, flavonoid, and thymoquinone index

Fig. 2.

GC–MS profiling and fatty acid composition

Table 3.

Fatty acid classes and nutritional utility ratios

Table 4.

Heatmap cluster analysis of fatty acid profiles of Nigella genotypes

Fig. 3.

Principal component analysis

PCA score plot (individuals PCA)

Fig. 4.

Genotype distribution and PCA biplot

Conclusion

Supplementary Information

Acknowledgements

Author contributions

Funding

Data availability

Declarations

Competing interests

Ethics approval and consent to participate

Consent for publication

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

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