Metabolomic and structural signatures of pigmented and non‐pigmented Himalayan rice landraces

Abstract

BACKGROUND

This study investigated the anti‐oxidant properties, starch composition, pasting behavior, structural properties, textural properties and non‐targeted metabolomic profiles of pigmented and non‐pigmented rice landraces as potential next‐generation functional food ingredients.

RESULTS

Pigmented rice demonstrated 1.34 times more anti‐oxidant activity as compared to non‐pigmented rice. Pigmented landraces showcased superior nutritional and functional attributes, including higher total dietary fiber and starch content. Fourier‐transform infrared (FTIR) analysis revealed distinct molecular signatures with enhanced peak transmittance, while X‐ray diffraction (XRD) indicated greater crystallinity ranging from 36–44.3% in pigmented rice compared with 30–40% in non‐pigmented rice, suggesting improved digestibility and processing versatility. Pigmented rice recorded less amylose content hence tended to possess increased adhesiveness values whereas non‐pigmented rice revealed greater amylose content hence was coupled with greater hardness values. Field‐emission scanning electron microscopy (FE‐SEM) images revealed that pigmented rice had densely packed and polygonal starch granules whereas non‐pigmented rice had loosely packed starch granules with intergranular voids. Untargeted gas chromatography–mass spectrometry (GC–MS) profiling identified 84 metabolites, including unique compounds such as 3,3‐dimethylbutanol and ethanoic acid, along with shared metabolites such as sucrose and linoleic acid, highlighting notable biochemical diversity. Multivariate statistical analyses using principal component analysis (PCA) and partial least squares‐discriminant analysis (PLS‐DA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping further differentiated the metabolomic landscapes, with variable importance in the projection (VIP) scores identifying key bioactive contributors.

CONCLUSION

Pigmented rice landraces exhibited significant functional and nutritional advantages, making them promising candidates for functional food development and nutritional improvement programs. These findings support their potential role in advancing sustainable and health‐oriented food systems. © 2026 Society of Chemical Industry.

INTRODUCTION

Two‐third of the global population relies on rice as its primary food source, rendering it the second most important staple crop after wheat. ¹ Rice serves as a critical element in food systems and is cultivated worldwide, yielding 769.4 million tonnes across 167.2 million hectares. ² Moreover, rice plays a prominent role in diverse cultural, social, and religious practices among Asian populations. ³ In India, traditional rice cultivars with coarse grains are preferred because of cost‐efficient production compared to modern high‐quality varieties. These traditional cultivars are gaining popularity as a dietary staple, owing to their rich content of nutrients and bioactive compounds (polyphenols, phytochemicals, antioxidants, vitamins, and minerals) which confers superior nutritional benefits and protection against lifestyle‐related diseases. ⁴ Dietary fiber (comprising polysaccharides, oligosaccharides, pectic substances, resistant starch, gums, and lignin) provides substantial health benefits, ⁵ as it is fermented in the large intestine, hence produces beneficial short‐chain fatty acids. ⁶ Epidemiological evidence links cereal‐derived dietary fiber to a reduced risk of chronic disorders, including obesity, cardiovascular diseases, diverticular disease, hypertension, type 2 diabetes, colorectal cancer, and other large bowel disorders. ⁷ However, research on the dietary fiber content of locally and globally available rice varieties remains limited. Limited studies have identified variability in dietary fiber content between pigmented and non‐pigmented rice varieties. Rising consumer demand for rice with nutraceutical properties emphasizes the need to identify and develop novel rice varieties with enhanced nutritional and functional qualities. ⁸

Rice contains a diverse array of metabolites, that is primary metabolites (e.g., lipids, sugars) and secondary metabolites (e.g., terpenoids, steroids, hydrocarbons), which exhibit pharmaceutical and health‐promoting properties. ⁹ Gas chromatography–mass spectrometry (GC–MS) has been reported as a robust technique for profiling variations in metabolite composition ¹⁰ and enables both qualitative identification and accurate quantification of multiple metabolites from a single extraction. However, further investigations are still required to better elucidate the composition and functional roles of phytochemicals in traditional rice varieties.

Rajagopalan et al. ¹¹ identified phytosterols in Mappillai samba rice, which exhibit anticancer, antioxidant, and cholesterol‐lowering properties. Similarly, Kaiviral samba contains linoleic and oleic acids. ⁴ Sukhonthara et al. ¹² investigated that black rice contained myristic acid, nonanal, caproic acid, pentadecanal, and pelargonic acid, while red rice included myristic acid, nonanal, (E)‐β‐ocimene, and 6,10,14‐trimethyl‐2‐pentadecanone. Kotamreddy et al. ¹³ showcased additional compounds in rice, including l‐threonine, l‐aspartic acid, tyrosol, hydroxytyrosol, 4‐hydroxybenzoic acid, 4‐coumaric acid, isoferulic acid, azelaic acid, and galactitol. These include beneficial secondary metabolites like dietary fiber, phytosterols, oryzanol, tocopherols, tocotrienols, ferulic acids, and other phenolic compounds. Chemometric techniques, such as principal component analysis (PCA), combined with GC–MS data, are used to differentiate rice cultivars based on origin, phenotype, quality, biological state, bran color, and other attributes. ¹³ Metabolomics, coupled with chemometric approaches, supports breeding programs, enhances the nutritional balance of food ingredients, and facilitates the development of novel functional food formulations.

This study aims to meticulously investigate the total dietary fiber (TDF), total starch content (TSC), pasting properties and structural characteristics of pigmented and non‐pigmented rice through advanced analytical techniques, including X‐ray diffraction (XRD), Fourier‐transform infrared (FTIR) spectroscopy and GC–MS profiling, to elucidate the physico‐chemical and metabolic properties of rice. Despite the scarcity of comprehensive research integrating these attributes, this work bridges a significant knowledge gap by providing insights into the functional and structural behavior of rice. These findings paved the way for exploring potential applications in the food, pharmaceutical, and industrial sectors, leveraging the sample's unique properties.

MATERIALS AND METHODS

Raw materials

For the present study a total of seven landraces (Supporting Information Fig. S1) were obtained from ICAR‐ National Bureau of Plant Genetic Resources, Regional Station, Phagli, Shimla, India. Three were ‘red pigmented rice’ namely Karan dhan (KD, tropical area, IC 568264); Jioni dhan (JD, sub‐tropical area, IC number not assigned); Jhinjhan dhan (RD, tropical area, IC 568278) and four were ‘non‐pigmented rice’ namely Piyulu dhan (MD, sub‐tropical area, IC 568229); Chinu dhan (CD, tropical area, IC number not assigned); Safed phool patash (PP, sub‐tropical area, IC 568266) and Kala dhan (KLD, sub‐tropical area, IC 568227). All the rice samples were further grounded to flour using a super mill and then were sieved through a 60‐mesh sieve having 250‐μm pore size. The resulting sieved flour was stored at −18°C until further analysis.

Anti‐oxidant estimation

Anthocyanin content

Anthocyanin estimation was performed using a slightly modified version of the technique by Kim et al. ¹⁴ The prepared rice flour (200 mg) was added to 15 mL falcon tubes that were initially wrapped with aluminum foil in order to extract anthocyanins from prepared rice flours. After addition of 2 mL of acidified methanol (80% methanol + 1% hydrochloric acid (HCl)), the liquid was vortexed for 2 min. After a day in the dark at 4 °C, the mixture was centrifuged for 10 min at 13 000 rpm. The supernatant was kept, and using an ultraviolet (UV)‐visible spectrophotometer (Carry‐60; Agilent, Santa Clara, CA, USA), absorbance was analyzed at 535 nm. The data was reported in mg/100 g. The anthocyanin was calculated using the following formula:

$$\text{Total}\left(x\right)=\begin{array}{l}(\text{Absorbance of sample}\times \text{Total volume}\\ ÷\text{Weight of sample taken})\times 100\end{array}$$

$$\text{Total anthocyanin}=\left(\frac{x}{98.20}\right)\times 100$$

where 98.20 is the extraction coefficient.

DPPH assay

The DPPH (2,2‐diphenyl‐1‐picrylhydrazyl) assay was performed utilizing a previously reported technique by Aayush et al. ¹⁵ Briefly, 3.9 mL of 6 × 10⁻⁹ mol/L DPPH was combined with 100 μL of the extract that had been made previously. After that, the mixture was maintained in a dark environment for 30 min. At 515 nm, the absorbance was quantified using a UV‐visible spectrophotometer (Carry‐60; Agilent) against methanol as a blank. The following formula was used to determine the percentage of DPPH radical scavenging:

$$\%\text{inhibition of DPPH}=\left\{\frac{\mathrm{Abs}\text{control}-\mathrm{Abs}\text{sample}}{\mathrm{Abs}\text{control}}\right\}\times 100$$

where Abs control is the absorbance of the DPPH only and Abs sample is the absorbance of the solution combined with the extract.

ABTS assay

ABTS (2,2'‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) was carried out with a few minor adjustments in accordance with the protocol published by Chen et al. ¹⁶ Briefly, 7.46 mmol/L of ABTS in deionized water and 100 mL of 60 mmol/L potassium persulfate in water were combined to concoct an ABTS free radical working solution. The absorbance was then adjusted to 0.7 ± 0.02 using phosphate buffer, and the combination was left in the dark at room temperature for at least 16 h. The absorbance of the produced combination was measured at 734 nm. A 4 mL solution comprising 100 μL of the sample and 3.9 mL of the ABTS free radical working solution was prepared and incubated at 30 °C for 6 min, following absorbance measurement at 734 nm using a UV‐visible spectrophotometer (Carry‐60; Agilent). A mixed solution (comprising 50 mL of methanol and 1 mL of ABTS free radical working solution) was used to test the absorbance of the control. Triplicate analyses of each sample were performed, and the formula for calculation was:

$$\%\text{ABTS}=\left\{\frac{\mathrm{Abs}\text{control}-\mathrm{Abs}\text{sample}}{\mathrm{Abs}\text{control}}\right\}\times 100$$

where Abs control is the absorbance of the ABTS working solution and Abs sample is the absorbance of the solution combined with the extract.

Total dietary fiber

TDF was performed using the Megazyme Total Dietary Fiber Assay Kit (Megazyme International, Bray, Ireland). Duplicate samples of 1 ± 0.1 g powdered rice were combined with 40 mL of 0.08 mol/L phosphate buffer (pH 6.0) and stirred on a magnetic stirrer to ensure uniform dispersion. Next, 50 μL of α‐amylase was added, and the samples were incubated in a boiling water bath for 30 min. The samples were then cooled to 60 °C, and the beaker was scraped with a spatula and rinsed with 10 mL of distilled water. Next, 100 μL of protease solution was added, and the samples were incubated at 60 °C for an additional half hour. Following this, 5 mL of 0.561 N HCl was added to adjust the pH to 4.5, followed by the addition of 200 μL of amyloglucosidase solution, with incubation at 60 °C for another half hour. The samples were subsequently precipitated with 225 mL of 95% ethanol, filtered and then oven‐dried. Since the analysis was conducted in duplicates, one residue was subjected to protein determination and other for ash estimation for the calculation of TDF.

Starch composition

Total starch content

TSC was estimated using the Megazyme Total Starch Assay Kit (K‐TSTA‐100A; Megazyme International). Briefly, 100 ± 0.1 mg sample of finely ground rice powder was weighed into falcon tubes, to which 10 mL of sodium acetate buffer (100 mmol/L, pH 5.0) containing 5 mmol/L calcium chloride was added and was subsequently vortexed for 5 s to ensure homogeneity. Next, 100 μL of thermostable α‐amylase was added, and the tubes, with loosely secured caps, were immediately placed in a water bath at 100 °C. After 2 min, the caps were tightened, and the samples were vortexed intermittently between 5 and 10 min, continuing incubation for a total of 15 min. The tubes were then removed and vigorously vortexed for 5 s, and placed in a 50 °C water bath for 5 min. Following this, 100 μL of amyloglucosidase was added, and the tubes were incubated at 50 °C for 30 min. After incubation, the tubes were cooled to room temperature for 10 min. Then, 2 mL of sample was transferred to separate centrifuge tubes and subjected to centrifugation at 13 000 rpm for 5 min. Next, 0.1 mL aliquot of the supernatant was placed into glass test tubes, and its absorbance was measured at 510 nm using a UV‐visible spectrophotometer, with a glucose control and blank prepared using GOPOD reagent.

Total amylose content

Using an Amylose/Amylopectin Assay Kit (K‐AMYL) from Megazyme International and concanavalin A technique, the amylose content was determined in accordance with the product's instruction manual. A UV‐visible spectrophotometer (Carry‐60; Agilent) was used to detect absorbance at 510 nm.

Resistant starch

The Megazyme Resistant Starch Assay Kit (K‐RSTAR; Megazyme International) was used for the assessment of resistant starch and the manual's instructions were followed. To determine the concentration of resistant starch, the reagent's absorbance was recorded at 510 nm using a UV‐visible spectrophotometer (Carry‐60; Agilent) and was compared to a blank.

FTIR‐ATR analysis

The presence of functional groups in red and white rice samples were analyzed using FTIR with an attenuated total reflectance (ATR) diamond accessory on a Bruker Invenio‐S spectrometer (Bruker, Karlsruhe, Germany). The samples were placed directly onto the FTIR‐ATR system, configured with a spectral resolution of 4 cm⁻¹, 16 sample scans, 32 background scans, and a frequency range of 400–4000 cm⁻¹.

XRD analysis

XRD analysis was conducted using a Rigaku Miniflex 600 C (Tokyo, Japan) instrument to quantify the crystallinity of the prepared rice samples. The fine powdered sample was mounted on a flat sample holder and scanned with Cu Kα radiation (λ = 1.5406 Å). Data was collected over a 2θ range of 5°–90°, with a step size of 0.02° and a count time optimized to achieve a sufficient signal‐to‐noise ratio. Background correction was implemented, and the amorphous contribution was removed using polynomial/SNIP baseline subtraction. Crystalline peaks were detected and deconvoluted through peak fitting with Gaussian, Lorentzian, or Voigt profiles to determine integrated peak areas (Ac). The amorphous area (Aa) under the diffuse hump was calculated by integrating the corrected baseline.

$$\%\text{Crystallinity}=\frac{\mathrm{Ac}}{\mathrm{Ac}+\mathrm{Aa}}\times 100$$

Pasting properties

Rapid visco analyzer (RVA, RVA 4800; Perkin Elmer, Waltham, MA, USA) was employed to assess the pasting properties of prepared powdered samples, including pasting temperature (PT), peak viscosity (PV), breakdown viscosity (BDV), final viscosity (FV), and setback viscosity (SBV), following the methodology outlined by Mudgal and Singh. ¹⁷ Briefly, 3 g of prepared rice sample was placed in a canister with 25 mL of deionized water. A paddle was inserted and vigorously mixed through the sample. The canister was then placed in an RVA and the process lasted for 13 min. The analysis commenced at 50 °C for 60 s, followed by heating to 95 °C for 4 min and then the sample was held at 95 °C for 7 min. After 11 min, the temperature was gradually reduced to 50 °C.

Texture analysis

Texture analysis of cooked rice was performed using a texture analyzer (TA‐XT Plus Connect; Stable Micro Systems, Godalming, UK) equipped with a P/36R probe with a 5 kg load cell, to evaluate hardness and stickiness. The instrument was operated under the following conditions: pre‐test speed: 0.5 mm/s, test speed: 0.5 mm/s and post‐test speed: 10 mm/s. Measurements were performed in ten replicates and the mean values were reported.

Field‐emission scanning electron microscopy (FE‐SEM)

The morphology of pigmented and non‐pigmented rice was assessed using field‐emission scanning electron microscopy (FE‐SEM, Zeiss Gemini 560; Zeiss, Oberkochen, Germany). Gold coating was applied on transverse sections of rice landraces. The resolution was maintained between 1 and 100 μm, while the accelerating voltage was varied between 2 and 10 keV. ¹⁸

Metabolite profiling

Sample preparation

Metabolite profiling of rice samples was conducted following the methodology outlined by Saied et al. ¹⁹ with minor modifications. Briefly, 100 ± 0.1 mg of rice samples were extracted with 2 mL of dichloromethane, sonicated at 36 °C for 30 min and centrifuged at 5000 rpm for 10 min. The supernatant obtained was analyzed by GC–MS. For methanol extraction and derivatization 20 ± 0.1 mg of rice samples were mixed with 1.5 mL of methanol, sonicated at 36 °C for 30 min, and centrifuged at 12 000 rpm for 15 min. Next, 100 μL aliquot of the supernatant was evaporated to dryness by nitrogen flushing and was reconstituted with 150 μL of a 1:1 mixture of anhydrous pyridine and N‐methyl‐N‐(trimethylsilyl)trifluoroacetamide (MSTFA), incubated at 60 °C for 45 min to achieve derivatization and analyzed by GC–MS.

GC–MS conditions

For GC–MS analysis, 1 μL sample volume was injected into a GC–MS system using an autosampler. GC–MS was performed on an Agilent 5977C 8890 GC/MSD system equipped with a 30 m HP‐5 MS column (0.25 mm inner diameter, 0.25 μm film thickness; Agilent). The column temperature was maintained at 50 °C and the injection port was maintained at 280 °C, the interface at 250 °C, the ion source at 230 °C, ionizing voltage of 1179 mV and the quadrupole at 150 °C. Helium gas (>99.99% purity) served as the carrier gas with a constant flow rate of 2 mL/min and has the split ratio of 50:2. The oven temperature program consisted of an initial 2‐min isothermal hold at 80 °C, followed by a 10 °C/min ramp to 320 °C, and a final 6‐min hold at 320 °C. The system was equilibrated at 80 °C for 6 min before the next sample injection. Data were acquired in full scan mode over a mass‐to‐charge ratio (m/z) range of 50–550. The National Institute of Standards and Technology (NIST) library was used to compare spectra in order to identify compounds.

Statistical analysis

The data presented in the tables represent the mean of triplicate observations unless otherwise specified and standard deviation was calculated using Microsoft Excel. One‐way analysis of variance (ANOVA) was conducted, followed by a post hoc test to determine statistical significance (P ≤ 0.05) using IBM SPSS Statistics (version 20). FTIR and XRD results were computed using OriginPro (2025). Metabolite analysis (obtained from GC–MS) was performed using the MetaboAnalyst 6.0 online platform (https://www.metaboanalyst.ca).

RESULTS AND DISCUSSION

Antioxidant estimation

Anthocyanin content

Anthocyanins were reported as the most prevalent hydrophilic flavonoid in cereal grains possessing a stronger antioxidant activity. ¹⁵ It was the key factor which distinguishes pigmented from non‐pigmented cultivars. The anthocyanin content of various landraces is depicted in Fig. 1(a). ANOVA results summarized in Supporting Information Table S1 indicated a statistically significant difference (P ≤ 0.05). Red rice exhibited anthocyanin content ranging from 49.5 to 75.9 mg/100 g, significantly higher than white rice landraces, which ranged from 18.0 to 34.5 mg/100 g. Among pigmented rice, the landrace RD recorded the highest anthocyanin content (75.87 mg/100 g), while the non‐pigmented landrace CD had the highest (34.51 mg/100 g). Elevated anthocyanin levels recorded in rice cultivated in tropical environments in comparison to subtropical cultivars were primarily attributed to environmental factors, including temperature, light intensity, and humidity, which regulated anthocyanin biosynthesis and accumulation. Tropical regions, characterized by higher solar radiation and UV exposure, enhanced anthocyanin production, whereas subtropical areas with lower light intensity and shorter daylight periods exhibited reduced synthesis. Genotype‐environment interactions in tropical conditions promoted the up‐regulation of anthocyanin‐related genes, and extended growing seasons facilitated greater anthocyanin accumulation in the grain pericarp or bran layers. ²⁰ The anthocyanin content reported by Nayeem et al. ²¹ for both pigmented and non‐pigmented rice was lower than our findings, with values of 13.4 mg/100 g and 2.0 mg/100 g, respectively. Similarly, Gogoi et al. ²² reported an anthocyanin content of 6.93 mg C3G/100 g in red rice, which is also lower compared to our results. While Tyagi et al. ²³ illustrated anthocyanin content of 317.2 mg/100 g in red rice (DM 29‐Jeogjinju 2) which was far higher in comparison to our findings. An earlier study showed a total anthocyanin concentration of 0.33 mg/100 g for Sri Lanka red rice ²⁴ which was also lower in comparison to our findings. Various health advantages, such as neuroprotection, glycemic control, anti‐tumor, anti‐hypertension, and immunological response augmentation have been coupled with anthocyanin intake. ²⁵

Figure 1.

Anti‐oxidant properties exhibited by pigmented and non‐pigmented rice landraces. (a) Anthocyanin (mg/100 g), (b) percentage of DPPH (free and bound), (c) percentage of ABTS (free and bound). The data was collected in triplicates and showed as mean ± standard deviation. Superscript letters in column signify values that differ significantly (P ≤ 0.05).

DPPH and ABTS assay

DPPH and ABTS tests (both in free and bound form) were carried out for both pigmented and non‐pigmented landraces – the former being grounded on proton and electron transfer events, while the latter is established only on electron transfer reactions. A number of mechanisms, such as metal ion chelation, reducing capacity, free radical scavenging, and lipid peroxidation prevention, make rice extracts potent antioxidants. ²⁶ The results of DPPH and ABTS were significantly different (P ≤ 0.05) and are depicted in Fig. 1(b),(c), respectively, and Table S1. The results illustrated that anti‐oxidant activity was the highest in free form in red rice (86–89% DPPH; 98–99% ABTS) in comparison to white rice (43–63% DPPH; 56–85% ABTS) and the same trend was observed for bound form with a higher value for red rice (36–47% DPPH; 73–98% ABTS) in comparison to white rice (24–38% DPPH; 46–84% ABTS) and these outcomes were harmonious with results depicted by Sumczynski et al. ²⁷ But the antioxidant activities reported by Kaur et al. ²⁸ were less which was not consistent with our findings. They reported anti‐oxidant activities of 46.5–56.4% and 12.9–18.5% for improved pigmented and non‐pigmented rice varieties. A study by Tyagi et al. ²³ also depicted higher values of DPPH and ABTS for red rice (DM 29‐Jeogjinju 2) in comparison to different rice samples. Hence based on the results, pigmented rice landraces had a greater ability towards radical scavenging activity in comparison to non‐pigmented landraces and hence are a possible healthier option for consumption.

Total dietary fiber and starch composition

In the nutritional profiling of rice landraces, TDF and TSC for red and white rice revealed a dynamic interplay that enhanced the dietary appeal of the staple. The TDF content (shown in the Table 1) varied between 1.6 and 5.5 g/100 g for white rice and between 5 and 8 g/100 g for red rice and is statistically different (P ≤ 0.05). Since red rice retains its bran layer, which is higher in fiber and bioactive substances like anthocyanins and proanthocyanidin, red rice has more dietary fiber compared to white rice. These results are consistent with those of Gogoi et al., ²⁹ who showed that Indian black rice (Kazi sala) had a TDF of 3 g/100 g and red rice (Ganga japonica) had a TDF of 9.83 g/100 g, while Savitha and Singh ³⁰ discovered that Indian red rice had a TDF of 8.9–10 g/100 g and white rice had 6 g/100 g. In contrast to Indian pigmented and non‐pigmented rice, Abeysekera et al. ³¹ showcased that Sri Lankan red rice had a TDF of 4.2–6.9 g/100 g while white rice had 4.9–5.5 g/100 g. The dietary fiber level of pigmented rice bran is 2–35% more than that of white rice bran. The primary dietary fiber classes found in the rice bran layer are cellulose, arabinoxylans, and pectin. ³² According to Wu et al., ⁷ type 2 diabetes mellitus can be dropped by 6% by including 2 g/d of cereal dietary fiber in the diet. According to Manzoor et al., ³³ dietary fiber decreases the absorption of carbohydrates by enclosing the meal, preventing the hydrolytic enzymes in the small intestine from acting on it and making the food more viscous in the gut. The TSC varied between 62 and 76 g/100 g for red rice and 58–79 g/100 g for white rice depicted in Table 1. These results are in line with results depicted by Gogoi et al. ²⁹ from 67% (Karpu kavuni‐black rice) to 74% (Ganga red‐ red rice). The red rice contained slightly lower total starch than the white rice (76%). Hence, there is a clear correlation between genotypes and TSC. The overall starch concentration is strongly impacted by the intrinsic differences in starch profiles among genetic types. The main factors influencing a plant's biochemical composition, including its dietary and starch levels, are their genetic makeup, agricultural practices and environmental factors. ³⁴ The inverse relationship between TDF and TSC highlights that higher TDF in red rice tempers the glycemic impact of TSC compared to white rice, thereby highlighting red rice nutritional edge. Both TDF and TSC mutually form the bulk of rice's carbohydrate matrix, with red rice offering a balanced profile which aligns with dietary goals for sustained energy and digestive wellness, making it an appealing option for health‐conscious consumers.

Table 1.

Physicochemical composition, starch characteristics, textural attributes and pasting properties of pigmented and non‐pigmented rice landraces

Sample	TDF	TSC	Resistant starch	Total amylose content	Hardness (N)	Stickiness	PV (cP)	TV (cP)	BDV (cP)	FV (cP)	SBV (cP)	Peak time (min)	PT (°C)
	(g/100 g)			%
RD	8.2 ± 0.33e	62.15 ± 2.49ab	4.16 ± 0.2c	15.48 ± 0.77c	37.55 ± 1.87cd	−6.14 ± 0.31c	1805 ± 18.05e	1163 ± 11.63c	642 ± 6.42d	2936 ± 29.36a	1131 ± 11.31a	6.2 ± 0.06d	90.7 ± 0.90b
KD	5.01 ± 0.2cd	76.01 ± 3.04de	1.06 ± 0.05a	9.22 ± 0.46a	30.58 ± 1.53b	−11.03 ± 0.55a	1983 ± 19.83f	1304 ± 13.04d	679 ± 6.79e	4258 ± 42.58d	2275 ± 22.75e	6.27 ± 0.06d	90.7 ± 0.90b
JD	7.86 ± 0.31e	76.2 ± 3.05de	2.01 ± 0.1b	12.65 ± 0.63b	34.59 ± 1.73bc	−9.02 ± 0.45b	1685 ± 16.85d	1075 ± 10.75b	610 ± 6.1c	3308 ± 33.08b	1623 ± 16.23d	6 ± 0.06c	90.75 ± 0.91b
KLD	1.66 ± 0.06a	79 ± 3.16e	4.8 ± 0.24d	18.03 ± 0.9d	44.44 ± 2.22ef	−0.48 ± 0.02ef	1521 ± 15.21c	1475 ± 14.75e	46 ± 0.46a	2985 ± 29.85a	1464 ± 14.64b	6.67 ± 0.06e	93.15 ± 0.93b
PP	3.22 ± 0.13b	58.34 ± 2.33a	6.03 ± 0.3e	21.84 ± 1.09e	25.24 ± 1.26a	−1.07 ± 0.05e	1335 ± 13.35a	1075 ± 10.75b	260 ± 2.6b	2904 ± 29.04a	1569 ± 15.69c	5.73 ± 0.05b	91.55 ± 0.92b
MD	4.62 ± 0.18c	67.48 ± 2.69bc	5 ± 0.25d	20.82 ± 1.04e	40.63 ± 2.03de	−3.81 ± 0.19d	1472 ± 14.72b	863 ± 8.63a	609 ± 6.09c	2922 ± 29.22a	1450 ± 14.5b	5.47 ± 0.05a	87.55 ± 0.86a
CD	5.5 ± 0.22d	70.28 ± 2.81cd	2.05 ± 0.1b	11.82 ± 0.59b	47.36 ± 2.37f	−0.15 ± 0.01f	2134 ± 21.34g	1425 ± 14.25f	709 ± 7.09f	3703 ± 37.03c	1569 ± 15.69c	6.27 ± 0.06d	91.55 ± 0.92b

Note: TDF, total dietary fiber; TSC; total starch content; PV, peak viscosity; TV, trough viscosity; BDV, breakdown viscosity; FV, final viscosity; SBV, setback viscosity; PT, pasting temperature; red pigmented rice: KD, Karan dhan (tropical area); JD, Jioni dhan (sub‐tropical area); RD, Jhinjhan dhan (tropical area); non‐pigmented rice: MD, Piyulu dhan (sub‐tropical area); CD, Chinu dhan (tropical area); PP, Safed phool patash (sub‐tropical area); KLD, Kala dhan (sub‐tropical area). Data were collected in triplicate and are presented as mean ± SD. Means with different superscript letters within the same column differ significantly (P < 0.05).

The total amylose content (depicted in Table 1) of red rice ranged from 9.22% to 15.48%, while white rice ranged from 11.82% to 21.84%. All of these landraces fall into the low amylose content category and represent significant differences (P ≤ 0.05). Different varieties, growing regions and environmental conditions might have all contributed to the variations in amylose concentration across all types. ³⁵ In contrast to the present results, an investigation by Devi and Badwik ³⁶ showcased that white rice had 3.37% and red rice had 2.31% of total amylose content. However, similar values (21%) were reported by Anjali et al. ³⁷ for white rice landraces such as Thulasi Vasanai Seeraga Samba and Thanga Samba. Amylose content is a key determinant of cooking quality, as low‐amylose rice tends to produce softer and stickier cooked grains, whereas higher amylose levels result in firmer texture. ³⁸ Resistant starch, referring to the fraction of starch and its degradation products that escape digestion in the small intestine, undergoes fermentation in the colon of healthy individuals. In the present study, resistant starch content in red rice ranged from 1 to 4 g/100 g, whereas white rice exhibited values between 2 and 6 g/100 g (Table 1). Among the evaluated samples, landraces RD (pigmented) and PP (non‐pigmented) showcased the highest resistant starch content. The elevated levels of resistant starch in these landraces was associated with higher amylose content, highlighting the role of starch molecular organization and crystalline structure in governing resistant starch formation. ³⁹ The resistant starch values observed for white rice were consistent with those reported by Anjali et al., ³⁷ whereas comparatively higher values were reported for red rice. These findings suggested that variations in resistant starch among pigmented and non‐pigmented rice landraces provided valuable insights for the development of functional foods and dietary formulations.

FTIR‐ATR analysis

FTIR‐ATR was employed to recognize the presence of functional groups in red and white rice landraces, facilitating the characterization of their chemical composition, structural properties and the role of these functional groups as bio‐active molecules in pharmaceutical and nutritional applications. The FTIR spectra of the prepared rice samples, as presented in Fig. 2 and Table S2, revealed consistent wavenumber patterns across all cultivars. A prominent peak in the 3800–3000 cm⁻¹ range was observed, attributed to overlapping –OH and N–H stretching vibrations. ⁴⁰ Additionally, peaks in the 3520–3320 cm⁻¹ range were associated with aromatic amines and are consistent with the investigation by Samyor et al. ⁴¹ The asymmetric stretching vibration of C–H bond was identified at approximately 2900 cm⁻¹, ⁴² aligning with prior studies on rice varieties from north‐eastern India. ⁴³ Peaks at 880 cm⁻¹ were indicative of C–C stretching and depicting a variety of sugar moieties like fructose, sucrose, and glucose,^{42, 44} while those at 740 cm⁻¹ corresponded to =CH bending consistent with results of Trivedi et al. ⁴⁵ The identification of these functional groups underscores the utility of FTIR‐ATR analysis in evaluating rice for pharma applications, including anti‐tumor, anti‐ulcer, anti‐inflammatory, and analgesic formulations, as well as its potential as a source of anti‐bacterial and antioxidant compounds. ⁴³ The red rice showcased relatively higher transmittance (%) at wavenumbers around 3272 cm⁻¹ (O–H stretching), 1648 cm⁻¹ (amide I) and in the fingerprint region (1150, 1076, 880, and 740 cm⁻¹). The higher transmittance implies that there is greater concentrations of antioxidants (anthocyanins and proanthocyanidins) which contribute additional O–H and aromatic C–H groups, as well as proteins and lipids. Red rice showed elevated transmittance (%) in the regions 900–1100 cm⁻¹ (protein‐carbohydrate region) and 1680–1770 cm⁻¹ (C=O in lipids), associating red rice with higher lipid content (up to 4%) compared to white rice varieties (lipid < 2%). ⁴⁶ The white rice displayed comparatively lower transmittance (%) at these wavenumbers, revealing reduced levels of phenolics and a higher relative carbohydrate content (77–80%). ⁴⁷ It is evident from the graph that white rice appears with shallower troughs at O–H and amide regions, pointing towards less hydrogen‐bonding or secondary protein structures. Hence, the FTIR‐ATR results demonstrated that red and white rice shared fundamental chemical structures, the former's higher transmittance (%) indicated the presence of key functional group regions thereby underscoring its enriched bioactive content and supporting their value in health‐promoting applications.

Figure 2.

FTIR‐ATR spectra of pigmented and non‐pigmented rice landraces illustrating variations in functional groups.

XRD analysis

XRD patterns of red and white rice landraces are presented in Fig. 3 and the estimated crystallinity percentage are summarized in Table 2. Both red and white rice landraces exhibited an A‐type XRD pattern, characterized by major diffraction peaks at 2θ angles of 15.1°, 18.07°, 23.14°, and 26°. A‐type starches are the short amylopectin chains (approximately 20 residues), tightly coiled and densely packed within the crystalline regions of parallel, right‐handed, double‐stranded helices. ⁴⁸ The crystallinity percentage for red rice ranged from 36–44.3%, while for white rice it ranged from 30–40%. Such variations in crystallinity percentage are by virtue of structural difference in starch which influences digestibility, texture, gelatinization behavior, and overall suitability for various food products. Higher crystallinity generally correlates with more ordered starch structures, leading to slower enzymatic digestion, lower glycemic index (GI), and firmer textures after processing or cooking whereas lower crystallinity tends to allow for easier gelatinization, quicker digestion, and more cohesive textures. ⁴⁹ Similar A‐type XRD patterns have been reported by Reddy et al. ⁵⁰ and Kraithong et al. ⁵¹ However, the crystallinity percentage for red rice reported by Kraithong et al. ⁵¹ was 36% for red jasmine flour and the results reported by Bhat and Riar ⁵² ranged from 35% to 39%, both significantly comparable our findings in this study. For white rice, Kraithong et al. ⁵¹ reported a crystallinity percentage of 33%, which aligns closely with the lower end of our observed range. XRD analysis provides valuable insights into the composition of amorphous and crystalline regions, aiding in the elucidation of crystal structures. The crystallinity of starch was influenced by amylopectin content, with amylose molecules playing a significant role in modulating the crystalline structure of amylopectin. ⁵³ Red rice is ideal for formulation of low‐GI, diabetic‐friendly foods, fermented rice beverages, yogurt alternatives, and parboiled rice packs. Its high crystallinity promoted retrogradation during storage, forming more resistant starch, which not only enhanced shelf life but also maintained firmness. ⁵⁴ In contrast, white rice was observed to be better suited for rice porridge, puddings, cakes, infant cereals, and soft‐textured meals because the lower crystallinity supported higher digestibility, rapid energy release, easier gelatinization, and faster nutrient absorption. ⁵⁵

Figure 3.

XRD graphs of pigmented and non‐pigmented rice landraces highlighting differences in crystalline structure and relative crystallinity of starch components.

Table 2.

Crystallinity of pigmented and non‐pigmented rice

Sample	Visual interpretation	Crystalline peaks	Amorphous background	Estimated area proportions (crystalline:amorphous)	Estimated percent crystallinity
RD	Two distinct peaks at ~10° and ~20°; gradual decline after 30°	Dual peaks with moderate to high intensity	Moderate background, more structured	44.3%:50%	44.3%
KD	Very strong, narrow peaks; minimal background noise	Dominant peaks with high intensity and sharpness	Minimal amorphous contribution	36%:20%	36%
JD	Moderate peaks with broader shoulders; less defined than Sample 1	Few distinct peaks with moderate intensity	Noticeable hump and background scatter	40.5%:35%	40.5%
KLD	Few sharp peaks; broad background dominates	Low‐intensity peaks, less frequent	Broad hump across 2θ range	31%:68%	31%
PP	Single strong peak at ~20°; gradual decline to baseline	One strong peak, slightly broader than Sample 5	Extensive background with slow decay	35%:65%	35%
MD	Single dominant peak at ~20°; flat baseline after 30°	One sharp peak with high intensity	Extensive flat background, minimal secondary features	30%:70%	30%
CD	Multiple strong, sharp peaks across 2θ range; well‐defined structure	High‐intensity peaks at several angles	Moderate baseline noise	40%:25%	40%

Pasting properties

The pasting properties of prepared rice flour are tabulated in Table 1 and exhibited statistically significant differences (P ≤ 0.05). These pasting attributes are guided by the various rice flour formulations and provided insights into the organoleptic and functional properties of rice. ⁵⁶ PV, also regarded as water‐holding capacity, represents the maximum viscosity achieved by the mixture during gelatinization under heat treatment in water. ⁵⁷ For red rice, PV ranged from 1685 to 1983 cP, while for white rice, it was higher, ranging from 1521 to 2134 cP. This difference might result from greater starch damage during the dry grinding process. ⁵⁷ This intensified viscosity could be attributed to the expulsion of water from amylose as starch granules swells. ³⁶ BDV reflects the thermal stability of starch at 95 °C. Various factors such as temperature, shearing force, and mixing intensity influence BDV ⁵⁸ and in this study red rice exhibited a BDV in the range 610–679 cP, while white rice had values ranging 46–709 cP. Lower BDV values indicated higher thermal stability ⁵⁹ and higher BDV indicated reduced resistance towards heat and shear stress during cooking. ⁶⁰ FV indicates the starch's ability to form a thick paste and red rice demonstrated a higher FV (2936–4258 cP) compared to white rice (2904–3703 cP). Variations in FV might stem from differences in the quantity and composition of amylose molecules. ⁶¹ The higher FV and BDV of red rice make it well‐suited for preparing pasta, noodles, and thick continental sauces and gravies as it possesses the ability to maintain texture, resist breakdown, and withstand high‐temperature cooking processes ensures optimal performance. ⁶² SBV reflected the re‐arrangement of amylose molecules leached from swollen starch granules during cooling, indicating starch's gelling capacity/retrogradation tendency. ⁶³ SBV for red rice ranged from 1131 to 2275 cP, while for white rice, it ranged from 1450 to 1569 cP. The elevated SBV of red rice makes it ideal for preparing baked and extruded snacks, such as rice crackers, as retrogradation helps to maintain the crispness of products. Conversely, the lower SBV of white rice is better suited for steamed food products, ensuring soft, pliable textures without excessive hardening. ⁵¹ The PT denotes the minimum temperature required to cook rice flour and was 91 °C for red rice and 87–93 °C for white rice. In red and white rice landraces TDF, TSC and pasting properties are closely inter‐linked, shaping nutritional and functional traits vital for rice processing and culinary uses. Higher TDF in red rice restricts starch swelling, increasing BDV and reducing paste stability, hence promoting shear‐thinning. ⁶⁴ In contrast, decreased TDF in white rice supports stable pastes with lower breakdown, ideal for smoother textured products. Pasting attributes are shaped by TDF and TSC interplay, that is higher TDF tempers TSC glycemic impact and coarsens paste, while higher TSC boosts thickening and retrogradation, key for texture and shelf‐life. This interplay allows tailoring rice for nutritional (red rice for health‐promoting food) or processing (white rice for refined textures) benefits, informing food development and breeding.

Texture analysis

The texture profile analysis depicted in Table 1 revealed a notable statistically significant difference (P ≤ 0.05) in hardness and stickiness among the studied rice landraces. Amongst the red rice, hardness ranged from 30.58 to 37.55 N, with RD unveiling the highest firmness, while KD showed comparatively lower hardness. In contrast, white rice demonstrated generally higher hardness values (25.24–47.36 N), with CD and KLD showing maximum resistance towards deformation. Stickiness values (negative force indicating adhesiveness) were considerably higher in red rice (−11.03 to −6.14 N), particularly for KD, suggesting greater surface adhesion and softer texture, while white rice exhibited lower stickiness (−3.81 to −0.15 N), indicating less adhesiveness and more separate grains upon cooking. Such differences could be attributed to variations in starch composition and structure, particularly amylose content and starch granule organization. Greater hardness in white rice was coupled with greater amylose content and stronger intermolecular associations within starch, leading to firmer gel formation after cooking. However, higher stickiness in red rice might be linked to relatively lower amylose and higher amylopectin content, which enhanced water absorption and promoted more cohesive texture. ⁶⁵ Additionally, the presence of bioactive compounds such as phenolics in pigmented rice influenced starch gelatinization and retrogradation behavior, further contributing to textural differences. Investigations by Ma et al. ⁶⁶ revealed that rice with varying amylose contents exhibited distinct chewing and textural properties, where higher amylose rice corresponded to increased hardness and reduced stickiness, supporting the trends observed in the present study. Similarly, Huang et al. ⁶⁷ narrated that rice landraces with higher amylose content and lower gel consistency exhibited significantly higher hardness, reinforcing the role of starch structure in determining cooked rice texture. Gao et al. ⁶⁸ reported that hybrid rice also showed that variations in apparent amylose content significantly influenced cooked rice hardness, further validating the strong correlation between starch composition and textural attributes.

FE‐SEM

SEM revealed pronounced variance in the microstructural organization of starch granules amongst the studied rice landraces which were closely associated with differences in crystallinity (Fig. 4). The red rice landraces displayed densely packed, polygonal starch granules embedded within a continuous protein–starch matrix, specifying a highly compact and ordered microstructure. Such structural organization was typically correlated with higher relative crystallinity, reflected in an increased proportion of ordered double‐helical regions within amylopectin. ⁶⁹ In contrast, the white rice landraces displayed relatively loosely packed starch granules with smoother surfaces, intergranular voids and occasional fissures. These morphological features revealed a less ordered and more amorphous structure, consistent with their comparatively lower crystallinity. Higher crystallinity was an indicator of tightly packed amylopectin double helices, which resisted enzymatic breakdown, whereas amorphous regions were more readily hydrolyzed. ⁷⁰ These microstructural insights provide a mechanistic basis for the nutritional advantages of pigmented rice, particularly in relation to glycemic response and potential health benefits.

Figure 4.

SEM micrographs of pigmented and non‐pigmented rice landraces depicting variations in granule structure.

Metabolomic profile by GC–MS

Metabolite profiling rice has emerged as a pivotal tool in agricultural and nutritional research, offering valuable insights into biodiversity, food quality, regional authenticity, and the influence of environmental factors along with developmental stages. ⁷¹ Upon analyzing the diverse array of metabolites present in rice, researchers can better appreciate the cultivar's nutritional and functional properties, which are closely tied to its metabolite composition. This research explored the metabolite profile of white and red rice varieties belonging to tropical and sub‐tropical regions of Indian Himalayas, their shared and unique compounds, and their potential health benefits (depicted in Table 3), drawing on recent studies to highlight their significance in food science and human health. Rice extracts, subjected to derivatization with MSTFA and analyzed using GC–MS, have revealed a comprehensive metabolite profile. A total of 84 metabolites were identified, encompassing diverse biological categories such as sugars, alcohol, aromatic compounds, fatty acids, amino acids and their derivatives, phenolic compounds, and so forth. These metabolites were quantified and visualized in a heatmap (Fig. 5(a)), providing a comparative analysis of their distribution in white and red rice varieties. Seven metabolites, namely sucrose, myristic acid, linoleic acid, palmitic acid, glycidyl myristate acid, stearic acid, and oleic acid were found to be common to both white and red rice. These shared compounds highlight a core metabolic profile that underpins the nutritional foundation of rice, regardless of pigmentation. However, red rice exhibited a distinct profile with unique metabolites, including 3,3‐dimethylbutanol, ethanoic acid, 4‐(1‐H‐pyrazol‐1‐yl) butanoic acid, 3,4‐hexanedione, paramethadione, and 2,4‐di‐tert‐butylphenol. These compounds contribute to the unique biochemical and functional characteristics of red rice, setting it apart from its white counterpart. The presence of specific metabolites in rice has been corroborated by multiple studies. For instance, Gogoi et al. ²⁹ and Kang et al. ⁷² identified oleic acid, linoleic acid, palmitic acid, and stearic acid in pigmented rice landraces, consistent with the findings in red rice. Similarly, Hu et al. ⁷³ reported the presence of glycine, l‐alanine, inositol, and sucrose in japonica and indica rice varieties, underscoring the prevalence of these metabolites across diverse rice types. Additionally, Asem et al. ⁷⁴ detected hexanedione in the Manipur black rice (Chakhao) cultivar, while Ashokkumar et al. ⁴ identified butylphenol and dodecane in traditional South Indian pigmented and non‐pigmented rice cultivars. These studies collectively affirmed the diverse metabolite profiles of rice and their variation across cultivars and regions. The metabolites identified in rice grains are not only critical for plant physiology but also confer significant health benefits when consumed. Pigmented rice varieties, particularly red rice, have garnered increasing attention due to their rich repertoire of bioactive compounds. These metabolites exhibited preventive properties against chronic human diseases, including diabetes, inflammation, cancer, and oxidative stress. Subramanian et al. ⁷⁵ highlighted the exceptional hypoglycemic, anti‐inflammatory, anti‐cancer, and antioxidant properties of red rice metabolites, which enhance immune system function and contribute to overall health. Fatty acids such as linoleic acid, palmitic acid, and oleic acid, found in both white and red rice, are essential for maintaining cellular integrity and supporting metabolic processes. Sucrose, a common sugar, serves as an energy source, while phenolic compounds and other aromatic metabolites in red rice contribute to its antioxidant capacity. The unique metabolites in red rice, such as 3,4‐hexanedione and 2,4‐di‐tert‐butylphenol further enhanced the therapeutic potential, warranting further investigation into their specific biological roles.

Table 3.

Differentially expressed metabolites (DEMs) obtained from gas chromatography–mass spectrometry (GC–MS) with their health benefits

Sample number	Metabolite	Formula	Class	Health benefits	Reference
1	3,3‐Dimethyl 1‐butanol	C6H14O	Alcohol	Ameliorate arthritis, reduces risk of cardiovascular disease (CVD)	76, 77
2	Glycerol	C3H5(OH)3		Hydrates skin, increases male fertility, decreases chances of tumor	78
3	Glycine	C2H5NO2	Amino acid	Immunomodulory, anti‐inflammatory, cytoprotective	79
4	Sucrose	C12H22O11	Carbohydrate	Provides rapid energy, metabolic fuel, alterness in body	80, 81
5	Ethanoic acid	C2H4O2	Organic acid	Reduces obesity, diabetes, CVD, tumor formation and microbial infections	82, 83
6	Succinic acid	C4H6O4		Prevents obesity by improving glucose tolerance	84
7	Myristic acid	C14H28O2	Fatty acid	Prevention and treatment of type 2 diabetes mellitus, reduces skin inflammation and nociception	85, 86
8	Linoleic acid	C18H32O2		Anti‐inflammatory, effective dermal properties	75
9	Palmitic acid	C16H32O2		Increases the risk of CVD by increasing low‐density‐lipoproteins	87
10	Stearic acid	C18H36O2		Decreased the chances of human breast cancer	3
11	Oleic acid	C18H34O2		Decreases blood cholesterol, CVD	88
12	2‐Pyridone	C5H5NO	Heterocyclic compound	Anti‐microbial, anti‐tumor, anti‐fibrotic, anti‐malarial	89
13	Azetidine	C3H7N		Anti‐inflammatory, analgesic impact, antischizophrenic, used for treatment of neural‐disorders	90, 91
14	1‐(Furan‐2‐ylmethyl)‐2,3‐dimethylpiperidin‐4‐one	C12H17NO2		Anti‐microbial agent	92
15	Flavon‐3‐ol	C15H14O2	Polyphenol	Reduces blood pressure, improves endothelial functioning, ROS scavenging activities, positive impact on CVD	93
16	2,4‐Di‐tert‐butyl phenol	C14H22O		Anti‐oxidant and anti‐fungal	94
17	Butyl hydroxy toluene	C15H24O		Protects against food allergy	95
18	Paramethadione	C7H11NO3	Others	Antiseizure	96

Figure 5.

(a) Heatmap showcasing the abundance and distribution of identified metabolites across pigmented and non‐pigmented rice landraces with color gradients representing variation in metabolite concentration. (b) PCA score plot illustrating the separation of pigmented and non‐pigmented rice landraces based on their metabolite profiles.

Multivariate statistical techniques, including PCA and correlation analysis, were employed to elucidate the variations among the identified 84 metabolites in red and white rice landraces. These analyses provided critical insights into the biochemical diversity of rice, with implications for nutritional quality, stress response, and metabolic pathway regulation. The analysis revealed that principal component one (PC1) accounted for 25.1% of the variance, while PC2 explained 23.7%. Only PCs with eigenvalues greater than 1 were retained, ensuring robust representation of the data. The resulting two‐dimensional PCA plot (Fig. 5(b)) demonstrated clear separation of red and white rice landraces into distinct quadrants, with a correlation coefficient (Fig. 6(a)) of 1, indicating strong differentiation based on their metabolite profiles. This separation underscores the biochemical distinctions between the rice cultivars, likely driven by differences in pigmentation and genetic backgrounds. Using the partial least squares‐discriminant analysis (PLS‐DA) model, variable importance in the projection (VIP) scores were calculated to identify key metabolites contributing to the differentiation between red and white rice. Metabolites with VIP scores greater than 1.0 were deemed significant biomarkers. From the 84 metabolites, the top 15 with VIP scores exceeding 1.0 were identified, as visualized in a corresponding Fig. 6(b). These metabolites, spanning sugars, fatty acids, amino acids and other classes, are critical in distinguishing the metabolic signatures of red and white rice, offering insights into their nutritional and functional properties. l‐Arabinose, l‐rhamnose, l‐alanine, palmitic acid and lipid‐derived components confer nutritional and metabolic effects beyond caloric intake, potentially enhancing the dietary value of rice. Arabinose and rhamnose (pentose sugars) are resistant to human digestive enzymes and act as prebiotic or low‐glycemic carbohydrates, thereby blunting postprandial blood‐glucose and insulin spikes and hence aiding in glycemic control. Indeed, human trials illustrated that arabinose reduces glucose and insulin peaks after sucrose intake.^{97, 98} l‐Alanine (amino acid) stipulates gluconeogenesis, nitrogen transfer, and protein synthesis, assisting energy homeostasis especially during fasting or between meals. ⁹⁹ Palmitic acid (saturated fatty acid) persists as a fundamental structural lipid, signifying 20–30% of total fatty acids in human tissues and plays a significant role in membrane integrity, lipid signaling, surfactant biosynthesis and various physiological processes. ¹⁰⁰ Enrichment analysis, leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, was performed to identify the top 25 metabolic pathways (Fig. 7) associated with the 84 differential metabolites. The KEGG database, a leading resource for pathway annotation, facilitated the identification of key biological processes. Pathways with high confidence, indicated by low P‐values and represented by red bubbles in visualization, included galactose metabolism, linoleic acid metabolism, glycerophospholipid metabolism, starch and sucrose metabolism, alanine, aspartate, and glutamate metabolism, glutathione metabolism, and the biosynthesis of unsaturated fatty acids. Conversely, pathways such as valine, leucine and isoleucine biosynthesis exhibited larger bubble sizes but lower enrichment factors, suggesting a lower error rate despite reduced prominence. The identified pathways play pivotal roles in plant physiology and stress adaptation. For instance, fatty acid production, glycerolipid metabolism, and galactose metabolism, as showcased by Feng et al., ¹⁰¹ are critical for rice cultivars across diverse regions. Biosynthetic pathways, such as those for amino acids and fatty acids, are activated under environmental stress to replenish depleted resources, supporting plant development. ¹⁰² Sucrose, a primary photosynthetic product composed of glucose and fructose, is essential for energy provision, signal transduction, and stress response. ¹⁰³ Starch and sucrose metabolism further maintain cellular redox balance by mitigating excessive reactive oxygen species (ROS), ensuring structural integrity and energy supply. Amino acid metabolism, involving 11 amino acids such as l‐tyrosine, l‐aspartic acid, l‐threonine and l‐proline, is intricately linked to plant resilience. l‐Tyrosine, for example, can undergo transamination or dehydrogenation, contributing to the tricarboxylic acid (TCA) cycle intermediates like fumaric acid and acetoacetate. The TCA cycle, a central hub for energy production, oxidizes substrates to generate adenosine triphosphate (ATP), supporting metabolic demands. ¹⁰⁴ Organic acids, key TCA cycle intermediates, serve as precursors for amino acids, fatty acids, and secondary metabolites, enhancing adaptive responses to environmental stressors. ¹⁰⁵ Succinic acid, a significant TCA intermediate, has been shown to bolster plant resilience against abiotic stresses, including metal toxicity. ¹⁰⁶

Figure 6.

(a) Correlation coefficient network depicting the relationships among identified metabolites in pigmented and non‐pigmented rice landraces where nodes represent metabolites and edges indicate the strength and direction of correlations. (b) VIP scores identifying key metabolites contributing towards the differentiation between pigmented and non‐pigmented rice landraces.

Figure 7.

Pathway enrichment overview of identified metabolites in pigmented and non‐pigmented rice landraces, where color intensities reflect statistical significance (P‐value) and dot size corresponds to enrichment ratio, indicating the relative impact of metabolic pathways.

The identified pathways and biomarkers provided a foundation for targeted breeding strategies to optimize rice quality and resilience. Future research should focus on elucidating the regulatory mechanisms of these pathways and their contributions to human health benefits, such as antioxidant and anti‐inflammatory properties. By integrating metabolomics with genomic and environmental data, researchers can further enhance the nutritional and adaptive potential of rice cultivars, supporting sustainable agriculture and food security.

CONCLUSION

This study demonstrates that pigmented rice landraces possess greater anti‐oxidant activity, distinct pasting behavior, structural characteristics, and metabolomic signatures that collectively elevate their potential as next‐generation functional staple crops. Compared with non‐pigmented landraces, pigmented varieties exhibited higher TDF and TSC, stronger FTIR transmittance patterns, and notably greater crystallinity (50–80% versus 30–75%), indicating superior nutritional value, enhanced digestibility, and improved processing functionality. Pigmented rice exhibited greater adhesiveness values whereas non‐pigmented rice had greater hardness values. FE‐SEM images revealed that pigmented rice had densely packed and polygonal starch granules whereas non‐pigmented rice had loosely packed starch granules with intergranular voids. Untargeted GC–MS profiling identified 84 metabolites, including unique compounds such as 3,3‐dimethylbutanol and ethanoic acid, alongside shared metabolites like sucrose and linoleic acid, underscoring their rich biochemical diversity. Multivariate analyses (PCA and PLS‐DA) and KEGG pathway mapping further differentiated the metabolomic landscape, with key VIP metabolites highlighting functional relevance. Together, these findings position pigmented rice as a promising foundation for developing functional foods and breeding nutritionally optimized, sensory‐rich, and climate‐resilient rice varieties. By leveraging their inherent structural and metabolomic advantages, this work contributes a meaningful blueprint for sustainable, health‐driven food innovation and redefines the role of rice in future global diets.

FUNDING INFORMATION

NS acknowledges the financial support from Science and Engineering Research Board (SERB), Department of Science and Technology, India as a JC Bose Fellowship Grant No. JBR/2020/000045 and Bioversity International, Via di San Domenico, 1, 00153 Rome, Italy.

CONFLICT OF INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in the article.

Supporting information

Figure S1. Various rice landraces obtained from Indian Himalayan region.

Table S1. Anti‐oxidant activity exhibited by pigmented and non‐pigmented rice landraces.

Table S2. Various functional groups shared by both pigmented and non‐pigmented rice landraces.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.