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ABSTRACT

Background: Kodo millet (Paspalum scrobiculatum L.) is an important small millet recognized for its nutritional richness, tolerance to changing climatic conditions and suitability for cultivation on low-fertility or marginal lands agricultural conditions. Most of the studies largely emphasized on the general nutritional composition and health benefits of millets, while comparative evaluations integrating biochemical characterization with multidimensional statistical approaches through method including principal component analysis (PCA), correlation analysis and hierarchical cluster-based analysis are still scarce.

Methods: The present study was conducted during the Kharif cropping season of 2022-2025 at Department of Botany and Research field of Shri Guru Ram Rai University, Dehradun, Uttarakhand, India. The experimental material comprised eleven genotypes of Kodo millet grown in a randomized complete block design (RCBD) comprising three replications for the evaluation of nutritional composition, identify superior genotypes and assess the relationships among nutritional traits using multidimensional statistical analyses.

Result: Analysis of variance (ANOVA) confirmed statistically significant differences among the kodo millet genotypes for all nutritional traits, indicating the existence of wide genetic variability that can be effectively utilized in crop improvement and breeding programmes. Protein content ranged from 7.8-10.4 g/100 g, carbohydrates from 57.5-64.4 g/100 g, fat from 1.8-3.15 g/100 g, ash content from 2.70-3.45 g/100 g and iron from 0.58-1.20 mg/100 g. Among the studied varieties, RK250-90 exhibited comparatively higher protein and mineral content, indicating superior nutritional potential. Principal component analysis (PCA), correlation analysis, heat map and cluster analysis further confirmed nutritional diversity and relationships among traits. The positive association between protein and mineral content highlights the possibility of simultaneous improvement of nutritional parameters through breeding strategies. The findings identified genotype (RK250-90) as a promising genetic resource for biofortification and breeding programmes aimed at improving the nutritional quality of Kodo millet.

INTRODUCTION

Kodo millet (Paspalum scrobiculatum L.) represents one of the earliest cereal species brought under cultivation and has historically supported subsistence agriculture in several parts of Asia and Africa. Owing to its exceptional ecological adaptability, this minor millet can successfully grow in environments characterized by low precipitation, degraded soils and limited agricultural inputs. Such attributes make it a practical alternative to widely cultivated cereals such as rice, wheat and maize, which are often sensitive to environmental stresses (Issoufou et al., 2013; Singh and Chauhan, 2019). Recent advances in crop genomics have further emphasized its potential as a resilient species for future climate-adaptive agriculture (Devi et al., 2014; Mishra et al., 2024).
       
The agronomic suitability of Kodo millet is particularly evident in rainfed and drought-affected regions (Habiyaremye et al., 2017; Dayal et al., 2023). The crop requires relatively low water input, typically performing well under annual rainfall conditions of approximately 40-50 cm. Its adaptability extends across a range of soil types, from coarse-textured uplands to moderately fertile loamy soils (Anal et al., 2024). In addition, its relatively short growth cycle and inherent tolerance to several pests and diseases make it a reliable crop for cultivation under marginal and risk-prone conditions (Deshpande et al., 2015; Hariprasanna, 2016). Modern research also indicates that improved nutrient management strategies can enhance productivity and resource-use efficiency in millet-based systems (Rangaiah et al., 2024).
       
From a nutritional perspective, Kodo millet is recognized for its balanced composition of macronutrients and micronutrients (Mallikarjun et al., 2013). It contains substantial levels of carbohydrates (~66%), moderate protein content (~11%) and significant dietary fiber (~10%), along with essential minerals such as calcium, iron, phosphorus and magnesium (Gopalan et al., 2004; Deshpande et al., 2015). Beside its basic nutritional profile, the grain is rich in biologically active compounds, including phenolics and antioxidants, which have been associated with reduced oxidative stress and improved metabolic health (Shahidi and Chandrasekhara, 2013). Recent systematic evaluations have also highlighted considerable variation in nutrient composition among millet genotypes, suggesting opportunities for targeted biofortification and crop improvement programs (Anitha et al., 2024; Shobana et al., 2025). These findings assist in the importance of developing transitioning technologies that balance consumer acceptability with nutritional retention (Sharma et al., 2024; Mathur et al., 2025).
       
Despite its multiple advantages, Kodo millet continues to be underexploited in mainstream food systems. Factors such as limited consumer awareness, suboptimal culinary familiarity and the abundance of anti-nutritional constituents (e.g. phytates and tannins) have contributed to its reduced utilization (Roopashree et al., 2014; Puren et al., 2023). However, these compounds are predominantly localized in the outer grain layers and can be effectively minimized through techniques such as soaking, germination and fermentation, which simultaneously improve nutrient bioavailability (Kumar and Sinha, 2010; Kimeera and Sucharitha, 2019; Kumar et al., 2024).
       
Although kodo millet has gained increasing attention as a climate-resilient and nutrient-rich cereal, comprehensive information on the nutritional variability among Indian kodo millet genotypes remains limited. Most of the available literature indicates that have primarily focused on the general nutritional composition and health benefits of millets, while comparative evaluations integrating biochemical characterization with multi-dimensional statistical approaches such as principal component analysis (PCA), correlation analysis and hierarchical cluster analysis (HCA) are still scarce among diverse kodo genotypes in India. To provide a comprehensive assessment, the present study was conducted to analyze the nutritional composition of selected kodo millet genotypes, identify superior genotypes and assess the relationships among nutritional traits using multi-dimensional statistical analyses to provide valuable information for future breeding programs and nutritional improvement of kodo millet.

MATERIALS AND METHODS

Experimental site and climatic condition
 
The present investigation was conducted over four successive Kharif seasons (2022-2025) at the Research Field of Shri Guru Ram Rai University, Patel Nagar, Dehradun, Uttarakhand, India, to evaluate the performance of kodo millet genotypes under subtropical field conditions  (30.32° N, 78.03° E; altitude ~640 m above mean sea level). The study area was characterized by a subtropical climate with most of the annual precipitation occurring during the monsoon months (June-September). The average temperature during the cropping period ranged between 18°C and 38°C, with annual rainfall around 2000 mm. The soil of the experimental field was classified as sandy loam with near-neutral pH (6.5-7.2), moderate organic carbon content (~0.6%) and available nutrient status comprising nitrogen (280-300 kg ha-1), phosphorus (18-22 kg ha-1) and potassium (150-180 kg ha-1). Standard laboratory protocols were followed for all analyses.
 
Experimental material
 
A total of 11 kodo millet varieties (KMV1 to KMV10 and RK 390-25) were evaluated which were collected from different places of India. KMV1 from Gujrat, KMV2, KMV6 and KMV8 from Maharashtra, KMV3 from Uttar Pradesh, KMV4 and KMV5 were from Madhya Pradesh, KMV7 Jharkhand, KMV9 and KMV10 from Punjab and RK250-90 from Uttar Pradesh.  Seeds used for sowing were obtained from authenticated sources and were screened to ensure.
 
Experimental design
 
The experiment was conducted following a Randomized Complete Block Design (RCBD) with three replications following suitable agricultural practices. Each genotype was assigned to individual plots measuring 3 m x 2 m. Spacing between rows and plants was maintained at 25 cm and 10 cm, respectively.
 
Extraction of crude seed extract
 
The harvested grains were first cleaned using a degrader to remove stones and an aspirator to eliminate sand and other dust particles. The cleaned grains were then subjected to dehulling to remove the husk and grind to powder. This millet powder was further cleaned with an aspirator to ensure removal of any remaining impurities. After thorough cleaning, 100 grams of each kodo millet powder were packed in polythene zip lock covers and used for nutritive analysis. The following nutritional parameters were analyzed for each variety: ash, moisture, protein, fat, carbohydrate and iron.
 
Estimation of phyto-constituents
 
Estimation of ash content
 
The samples were analyzed for ash content by using the dry ash method (AOAC, 2005; Thiex et al., 2012). The ash content was calculated by determining the weight of the ash residue (W3 - W1) and expressing it as a percentage of the original sample weight using the following formula:
 
  
 
W1- initial weight of empty crucible.
W2- weight of empty crucible + Sample initial weight.
W3- final weight of the crucible along with the ash residue.
 
Estimation of protein
 
Crude protein content was determined using the standard Micro-Kjeldahl method (AOAC, 2005).
       
The percentage nitrogen content was determined using the subsequent formula:
 
  
 
Where,
T =sample titration value (mL).
B = Blank titration value (mL).
N = Normality of HCl.
W = Weight of the specimen (g) and 14 is the atomic weight of nitrogen.
       
The crude protein content was then estimated by multiplying the nitrogen percentage by the conversion factor 6.25, as shown below:
 
Crude protein (%) = %Nx6.25
 
Estimation of carbohydrate
 
The available carbohydrate mass of millet samples was quantified using the phenol-sulfuric acid method (Dubois et al., 1956).
 
Estimation of lipids
 
The lipid content of millet samples was estimated by employing the Soxhlet extraction method (AOAC, 2005). The lipid content was estimated by using the formula:
  
  
 
Estimation of iron
 
The iron quantity present in millet samples was determined using the o-phenanthroline colorimetric method (Siong et al., 1989; AOAC, 2005). The method is based on the formation of stable orange-red complex between ferrous iron (Fe2+) and o-phenanthroline, the intensity of which is directly proportional to the concentration of iron present in the sample.
 
Statistical analysis
 
The experimental data were statistically analyzed using Analysis of Variance (ANOVA)  to determine the significance of differences among the treatments. Mean values and standard error (SE) were computed for all nutritive content traits. The Critical Difference (CD) test at the 5% significance level was used to compare treatment means and determine significant differences among the varieties included in the study (Gomez and Gomez, 1984; Panse and Sukhatme, 1985). Pearson’s correlation coefficients were calculated to assess the degree of association between the studied traits by using R-studio and Jamovi software.

RESULTS AND DISCUSSION

Analysis of variance (ANOVA)
 
Statistical analysis using ANOVA demonstrated significant differences among the Kodo millet genotypes for most nutritional traits except carbohydrate (Table 1). The existence of significant variation among nutritional parameters indicates the possibility of selecting superior genotypes for quality improvement and breeding program.

Table 1: Analysis of variance (ANOVA) for nutritional traits in kodo millet varieties.


       
Significant variation was observed among the eleven kodo millet genotypes for nutritional traits, indicating the existence of valuable genetic diversity for quality improvement (Table 2). Among the evaluated genotypes, RK250-90 recorded the highest protein, iron and ash contents, suggesting its potential as a nutritionally superior genotype. KMV3, KMV4 and KMV9 demonstrated favorable nutrition profiles by combining higher protein with moderate to high carbohydrates and mineral contents. Whereas, lowest nutritional values has been observed in KMV1 genotypes. These variations demonstrates that these genotypes will be used as valuable genetic resources for breeding programs for improving nutritional quality in Kodo millet. Similarly, Upadhyaya et al., (2014) emphasized the importance of exploiting genetic diversity in millet germplasm for nutritional enhancement.

​

Table 2: Nutritional composition among different kodo millet varieties.


 
Principal component analysis (PCA)
 
Principal component analysis is widely recognized as a robust multidimensional statistical tool for assessing genetic diversity and identifying nutritionally superior germplasm based on multiple quantitative traits simultaneously (Upadhyaya et al., 2014; Jolliffe and Cadima, 2016). In this regards, PCA was performed to examine relationships among nutritional traits and to identify patterns of variability among Kodo millet varieties. The PCA plot revealed variation among the genotypes, as evidenced by their wide distribution across the first two principal components (Fig 1). Varieties positioned closer together demonstrated similar nutritional characteristics, while those located farther apart showed greater divergence. Among the evaluated genotypes, RK250-90 separated distinctly from KMV genotypes and occupied the upper left quadrant of the PCA plot. This isolated position indicates that RK250-90 has a unique nutritional profile as it contain comparatively higher protein and mineral values as compared with other genotypes. This diverse position suggest that this genotype may represent an important donor genotype for nutritional improvement programs. Similarly, KMV1 was positioned independently on the extreme positive side of PC1, indicating considerable divergence from other genotypes and may represent another valuable source of genetic variability.

Fig 1: Principal component analysis (PCA) shows the relationship between kodo Millet varieties and nutritional traits. The length and direction of arrows indicate the contribution of each trait to the principal components.


       
KMV2, KMV3 and KMV8 formed a compact cluster in the positive region of the PCA plot indicating close nutritional similarity among these genotypes. KMV4, KMV9 and KMV10 were grouped together on the negative side of PC1 showing their comparable nutritional composition. Similar clustering patterns have been reported in diversity studies of millets and other cereal crops (Upadhyaya et al., 2014; Goron and Raizada, 2015). KMV6 occupied an intermediate position whereas, KMV3 and KMV7 occupied separate position towards the lower region of PC2 indicating moderate divergence from the remaining genotypes (Patil et al., 2019; Porwal et al., 2023). The significant variation in nutritional composition of grains among millet genotypes is governed by genetic factors and can be effectively exploited in breeding programmes for nutritional improvement and biofortification (Upadhyaya et al., 2014; Goron and Raizada, 2015; Nirubana  et al., 2021). Developing cultivars with improved protein and micronutrient contents has become increasingly important in addressing hidden hunger, malnutrition problems and improving nutritional security, particularly in regions where millets constitute an important component of the daily diet (Food and Agriculture Organization, 2023; International Crops Research Institute for the Semi-Arid Tropics, 2023; Thakur and  Saini, 1995; Vishnuprabha and Vanniarajan, 2018).
 
Correlation analysis of nutritional traits
 
The heatmap illustrates the correlation among different nutritional traits of kodo millet varieties, namely protein, carbohydrate, fat, iron and ash content (Fig 2). Protein exhibited a strong positive association with iron (r=0.68) and ash (r=0.76) content, indicating that varieties with higher protein levels also tended to possess greater mineral content. This relationship suggests that simultaneous improvement of protein and iron through selection may be feasible, which is advantageous for breeding nutritionally superior cultivars (Goron and Raizada, 2015). Iron also showed a moderately strong positive correlation with ash (r=0.62), revealed the contribution of mineral accumulation to the total ash content of the grain. Similar positive associations between mineral content and ash have been reported in cereal grains and millets, suggesting ash serve as an indicator of total mineral composition (Saleh et al., 2013).Fat displayed moderate positive correlation with ash content but weak association with iron and carbohydrates. Carbohydrate showed a weak or negative relationship with iron, suggesting that higher carbohydrate concentration may not contribute to increased iron accumulation (Yadav et al., 2020). Overall, the heatmap reveals the interrelationship among nutritional parameters and helps identify traits that can be simultaneously improved in breeding programs for nutritionally superior kodo millet varieties.

Fig 2: Pearson correlation heatmap showing relation among nutritional traits in kodo millet genotypes.


 
Cluster analysis of kodo millet varieties
 
Hierarchical cluster analysis (HCA) was performed using nutritional parameters to assess the similarity among the eleven Kodo millet germplasm. The dendrogram classified the varieties into two clusters represent variability among different kodo millet varieties based on their nutritional characteristics (Fig 3). The varieties were grouped according to their similarity, where closely linked varieties indicate similar nutritional composition. The clustering pattern reflects the degree of similarity among the varieties, where shorter linkage distances indicate greater nutritional resemblance while longer distances represent higher divergence (Mohammadi and Prasanna, 2003).
       
The first cluster (cluster 1) contains KMV1, KMV5, KMV2 and KMV8. Within this cluster, KMV2 and KMV8 were most closely related varieties, joining at the lowest linkage distance, suggesting that these genotypes possess highly similar nutritional composition. KMV5 subsequently clustered with this pair, while KMV1 joined at a comparatively higher linkage distance, indicating that although nutritionally related, it is relatively more distinct than the other genotypes of this cluster. The second cluster (Cluster II) consisted of RK250-90, KMV3, KMV7, KMV4, KMV9, KMV10 and KMV6. In this cluster, KMV3 and KMV7 formed the closest pair indicating a high degree of nutritional similarity. KMV4and KMV9 clustered together at a short linkage distance, suggesting similar nutritional profiles. KMV10 and KMV6 also formed a closely related subgroup, reflecting comparable nutritional characteristics. These three sub-group were subsequently merged to form the larger cluster II.
       
The present investigation highlights the nutritional significance and variability among different Kodo millet genotypes. Future research may focus on molecular characterization of nutritionally superior genotypes to identify genes associated with enhanced protein and mineral accumulation. Such studies can support marker-assisted breeding and genetic improvement programs. Processing techniques including germination, fermentation, malting and extrusion may be optimized to enhance nutritional quality and functional properties of foods based on millets (Dekka et al., 2023). Development of value-added meal products may help increase consumer awareness and commercial utilization of Kodo millet. Shelf-life studies and storage behavior of millet grains and processed products should also be investigated to improve product stability and marketability. Multi-location and multi-season trials involving larger germplasm collections are required to evaluate genotype x environment interactions affecting nutritional traits. Integration of advanced statistical tools, genomics, metabolomics and precision breeding approaches may further accelerate the development of high-yielding and nutritionally superior Kodo millet cultivars suitable for sustainable agriculture and nutritional security.

CONCLUSION

The present study emphasized that Kodo millet (Paspalum scrobiculatum L.) possesses substantial nutritional value and significant variability among different genotypes for important nutritional traits. The analyzed varieties showed appreciable levels of protein, carbohydrates, fat, iron and ash content, confirming the importance of Kodo millet as a nutrient-rich cereal crop. RK 250-90 exhibited superior nutritional characteristics, particularly for protein and mineral content, indicating its potential use in breeding and varietal improvement programs. Promotion of nutritionally superior varieties, along with improved processing and value-addition technologies, may enhance consumer acceptance and utilization of this underexploited millet. Further research involving larger germplasm collections, molecular characterization and evaluation of bioactive compounds may provide additional insights for crop improvement and functional food development.

ACKNOWLEDGEMENT

This work was supported by Shri Guru Ram Rai University through the Seed Money Funded Grant Project. The authors express their sincere gratitude to the Department of Botany, School of Basic and Applied Sciences, Shri Guru Ram Rai University, Patel Nagar Campus, Dehradun, Uttarakhand, for their valuable support and facilities provided during the course of this research.
 
Disclaimers
 
The views and conclusions expressed in this research article are solely those of the authors and do not necessarily reflect the views of their affiliated institution. The authors are responsible for the accuracy and completeness of the information presented, but they do not accept any liability for any direct or indirect losses resulting from the use of this content.

CONFLICT OF INTEREST

Authors have declared that no competing interests exist.

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    ABSTRACT

    Background: Kodo millet (Paspalum scrobiculatum L.) is an important small millet recognized for its nutritional richness, tolerance to changing climatic conditions and suitability for cultivation on low-fertility or marginal lands agricultural conditions. Most of the studies largely emphasized on the general nutritional composition and health benefits of millets, while comparative evaluations integrating biochemical characterization with multidimensional statistical approaches through method including principal component analysis (PCA), correlation analysis and hierarchical cluster-based analysis are still scarce.

    Methods: The present study was conducted during the Kharif cropping season of 2022-2025 at Department of Botany and Research field of Shri Guru Ram Rai University, Dehradun, Uttarakhand, India. The experimental material comprised eleven genotypes of Kodo millet grown in a randomized complete block design (RCBD) comprising three replications for the evaluation of nutritional composition, identify superior genotypes and assess the relationships among nutritional traits using multidimensional statistical analyses.

    Result: Analysis of variance (ANOVA) confirmed statistically significant differences among the kodo millet genotypes for all nutritional traits, indicating the existence of wide genetic variability that can be effectively utilized in crop improvement and breeding programmes. Protein content ranged from 7.8-10.4 g/100 g, carbohydrates from 57.5-64.4 g/100 g, fat from 1.8-3.15 g/100 g, ash content from 2.70-3.45 g/100 g and iron from 0.58-1.20 mg/100 g. Among the studied varieties, RK250-90 exhibited comparatively higher protein and mineral content, indicating superior nutritional potential. Principal component analysis (PCA), correlation analysis, heat map and cluster analysis further confirmed nutritional diversity and relationships among traits. The positive association between protein and mineral content highlights the possibility of simultaneous improvement of nutritional parameters through breeding strategies. The findings identified genotype (RK250-90) as a promising genetic resource for biofortification and breeding programmes aimed at improving the nutritional quality of Kodo millet.

    INTRODUCTION

    Kodo millet (Paspalum scrobiculatum L.) represents one of the earliest cereal species brought under cultivation and has historically supported subsistence agriculture in several parts of Asia and Africa. Owing to its exceptional ecological adaptability, this minor millet can successfully grow in environments characterized by low precipitation, degraded soils and limited agricultural inputs. Such attributes make it a practical alternative to widely cultivated cereals such as rice, wheat and maize, which are often sensitive to environmental stresses (Issoufou et al., 2013; Singh and Chauhan, 2019). Recent advances in crop genomics have further emphasized its potential as a resilient species for future climate-adaptive agriculture (Devi et al., 2014; Mishra et al., 2024).
           
    The agronomic suitability of Kodo millet is particularly evident in rainfed and drought-affected regions (Habiyaremye et al., 2017; Dayal et al., 2023). The crop requires relatively low water input, typically performing well under annual rainfall conditions of approximately 40-50 cm. Its adaptability extends across a range of soil types, from coarse-textured uplands to moderately fertile loamy soils (Anal et al., 2024). In addition, its relatively short growth cycle and inherent tolerance to several pests and diseases make it a reliable crop for cultivation under marginal and risk-prone conditions (Deshpande et al., 2015; Hariprasanna, 2016). Modern research also indicates that improved nutrient management strategies can enhance productivity and resource-use efficiency in millet-based systems (Rangaiah et al., 2024).
           
    From a nutritional perspective, Kodo millet is recognized for its balanced composition of macronutrients and micronutrients (Mallikarjun et al., 2013). It contains substantial levels of carbohydrates (~66%), moderate protein content (~11%) and significant dietary fiber (~10%), along with essential minerals such as calcium, iron, phosphorus and magnesium (Gopalan et al., 2004; Deshpande et al., 2015). Beside its basic nutritional profile, the grain is rich in biologically active compounds, including phenolics and antioxidants, which have been associated with reduced oxidative stress and improved metabolic health (Shahidi and Chandrasekhara, 2013). Recent systematic evaluations have also highlighted considerable variation in nutrient composition among millet genotypes, suggesting opportunities for targeted biofortification and crop improvement programs (Anitha et al., 2024; Shobana et al., 2025). These findings assist in the importance of developing transitioning technologies that balance consumer acceptability with nutritional retention (Sharma et al., 2024; Mathur et al., 2025).
           
    Despite its multiple advantages, Kodo millet continues to be underexploited in mainstream food systems. Factors such as limited consumer awareness, suboptimal culinary familiarity and the abundance of anti-nutritional constituents (e.g. phytates and tannins) have contributed to its reduced utilization (Roopashree et al., 2014; Puren et al., 2023). However, these compounds are predominantly localized in the outer grain layers and can be effectively minimized through techniques such as soaking, germination and fermentation, which simultaneously improve nutrient bioavailability (Kumar and Sinha, 2010; Kimeera and Sucharitha, 2019; Kumar et al., 2024).
           
    Although kodo millet has gained increasing attention as a climate-resilient and nutrient-rich cereal, comprehensive information on the nutritional variability among Indian kodo millet genotypes remains limited. Most of the available literature indicates that have primarily focused on the general nutritional composition and health benefits of millets, while comparative evaluations integrating biochemical characterization with multi-dimensional statistical approaches such as principal component analysis (PCA), correlation analysis and hierarchical cluster analysis (HCA) are still scarce among diverse kodo genotypes in India. To provide a comprehensive assessment, the present study was conducted to analyze the nutritional composition of selected kodo millet genotypes, identify superior genotypes and assess the relationships among nutritional traits using multi-dimensional statistical analyses to provide valuable information for future breeding programs and nutritional improvement of kodo millet.

    MATERIALS AND METHODS

    Experimental site and climatic condition
     
    The present investigation was conducted over four successive Kharif seasons (2022-2025) at the Research Field of Shri Guru Ram Rai University, Patel Nagar, Dehradun, Uttarakhand, India, to evaluate the performance of kodo millet genotypes under subtropical field conditions  (30.32° N, 78.03° E; altitude ~640 m above mean sea level). The study area was characterized by a subtropical climate with most of the annual precipitation occurring during the monsoon months (June-September). The average temperature during the cropping period ranged between 18°C and 38°C, with annual rainfall around 2000 mm. The soil of the experimental field was classified as sandy loam with near-neutral pH (6.5-7.2), moderate organic carbon content (~0.6%) and available nutrient status comprising nitrogen (280-300 kg ha-1), phosphorus (18-22 kg ha-1) and potassium (150-180 kg ha-1). Standard laboratory protocols were followed for all analyses.
     
    Experimental material
     
    A total of 11 kodo millet varieties (KMV1 to KMV10 and RK 390-25) were evaluated which were collected from different places of India. KMV1 from Gujrat, KMV2, KMV6 and KMV8 from Maharashtra, KMV3 from Uttar Pradesh, KMV4 and KMV5 were from Madhya Pradesh, KMV7 Jharkhand, KMV9 and KMV10 from Punjab and RK250-90 from Uttar Pradesh.  Seeds used for sowing were obtained from authenticated sources and were screened to ensure.
     
    Experimental design
     
    The experiment was conducted following a Randomized Complete Block Design (RCBD) with three replications following suitable agricultural practices. Each genotype was assigned to individual plots measuring 3 m x 2 m. Spacing between rows and plants was maintained at 25 cm and 10 cm, respectively.
     
    Extraction of crude seed extract
     
    The harvested grains were first cleaned using a degrader to remove stones and an aspirator to eliminate sand and other dust particles. The cleaned grains were then subjected to dehulling to remove the husk and grind to powder. This millet powder was further cleaned with an aspirator to ensure removal of any remaining impurities. After thorough cleaning, 100 grams of each kodo millet powder were packed in polythene zip lock covers and used for nutritive analysis. The following nutritional parameters were analyzed for each variety: ash, moisture, protein, fat, carbohydrate and iron.
     
    Estimation of phyto-constituents
     
    Estimation of ash content
     
    The samples were analyzed for ash content by using the dry ash method (AOAC, 2005; Thiex et al., 2012). The ash content was calculated by determining the weight of the ash residue (W3 - W1) and expressing it as a percentage of the original sample weight using the following formula:
     
      
     
    W1- initial weight of empty crucible.
    W2- weight of empty crucible + Sample initial weight.
    W3- final weight of the crucible along with the ash residue.
     
    Estimation of protein
     
    Crude protein content was determined using the standard Micro-Kjeldahl method (AOAC, 2005).
           
    The percentage nitrogen content was determined using the subsequent formula:
     
      
     
    Where,
    T =sample titration value (mL).
    B = Blank titration value (mL).
    N = Normality of HCl.
    W = Weight of the specimen (g) and 14 is the atomic weight of nitrogen.
           
    The crude protein content was then estimated by multiplying the nitrogen percentage by the conversion factor 6.25, as shown below:
     
    Crude protein (%) = %Nx6.25
     
    Estimation of carbohydrate
     
    The available carbohydrate mass of millet samples was quantified using the phenol-sulfuric acid method (Dubois et al., 1956).
     
    Estimation of lipids
     
    The lipid content of millet samples was estimated by employing the Soxhlet extraction method (AOAC, 2005). The lipid content was estimated by using the formula:
      
      
     
    Estimation of iron
     
    The iron quantity present in millet samples was determined using the o-phenanthroline colorimetric method (Siong et al., 1989; AOAC, 2005). The method is based on the formation of stable orange-red complex between ferrous iron (Fe2+) and o-phenanthroline, the intensity of which is directly proportional to the concentration of iron present in the sample.
     
    Statistical analysis
     
    The experimental data were statistically analyzed using Analysis of Variance (ANOVA)  to determine the significance of differences among the treatments. Mean values and standard error (SE) were computed for all nutritive content traits. The Critical Difference (CD) test at the 5% significance level was used to compare treatment means and determine significant differences among the varieties included in the study (Gomez and Gomez, 1984; Panse and Sukhatme, 1985). Pearson’s correlation coefficients were calculated to assess the degree of association between the studied traits by using R-studio and Jamovi software.

    RESULTS AND DISCUSSION

    Analysis of variance (ANOVA)
     
    Statistical analysis using ANOVA demonstrated significant differences among the Kodo millet genotypes for most nutritional traits except carbohydrate (Table 1). The existence of significant variation among nutritional parameters indicates the possibility of selecting superior genotypes for quality improvement and breeding program.

    Table 1: Analysis of variance (ANOVA) for nutritional traits in kodo millet varieties.


           
    Significant variation was observed among the eleven kodo millet genotypes for nutritional traits, indicating the existence of valuable genetic diversity for quality improvement (Table 2). Among the evaluated genotypes, RK250-90 recorded the highest protein, iron and ash contents, suggesting its potential as a nutritionally superior genotype. KMV3, KMV4 and KMV9 demonstrated favorable nutrition profiles by combining higher protein with moderate to high carbohydrates and mineral contents. Whereas, lowest nutritional values has been observed in KMV1 genotypes. These variations demonstrates that these genotypes will be used as valuable genetic resources for breeding programs for improving nutritional quality in Kodo millet. Similarly, Upadhyaya et al., (2014) emphasized the importance of exploiting genetic diversity in millet germplasm for nutritional enhancement.

    ​

    Table 2: Nutritional composition among different kodo millet varieties.


     
    Principal component analysis (PCA)
     
    Principal component analysis is widely recognized as a robust multidimensional statistical tool for assessing genetic diversity and identifying nutritionally superior germplasm based on multiple quantitative traits simultaneously (Upadhyaya et al., 2014; Jolliffe and Cadima, 2016). In this regards, PCA was performed to examine relationships among nutritional traits and to identify patterns of variability among Kodo millet varieties. The PCA plot revealed variation among the genotypes, as evidenced by their wide distribution across the first two principal components (Fig 1). Varieties positioned closer together demonstrated similar nutritional characteristics, while those located farther apart showed greater divergence. Among the evaluated genotypes, RK250-90 separated distinctly from KMV genotypes and occupied the upper left quadrant of the PCA plot. This isolated position indicates that RK250-90 has a unique nutritional profile as it contain comparatively higher protein and mineral values as compared with other genotypes. This diverse position suggest that this genotype may represent an important donor genotype for nutritional improvement programs. Similarly, KMV1 was positioned independently on the extreme positive side of PC1, indicating considerable divergence from other genotypes and may represent another valuable source of genetic variability.

    Fig 1: Principal component analysis (PCA) shows the relationship between kodo Millet varieties and nutritional traits. The length and direction of arrows indicate the contribution of each trait to the principal components.


           
    KMV2, KMV3 and KMV8 formed a compact cluster in the positive region of the PCA plot indicating close nutritional similarity among these genotypes. KMV4, KMV9 and KMV10 were grouped together on the negative side of PC1 showing their comparable nutritional composition. Similar clustering patterns have been reported in diversity studies of millets and other cereal crops (Upadhyaya et al., 2014; Goron and Raizada, 2015). KMV6 occupied an intermediate position whereas, KMV3 and KMV7 occupied separate position towards the lower region of PC2 indicating moderate divergence from the remaining genotypes (Patil et al., 2019; Porwal et al., 2023). The significant variation in nutritional composition of grains among millet genotypes is governed by genetic factors and can be effectively exploited in breeding programmes for nutritional improvement and biofortification (Upadhyaya et al., 2014; Goron and Raizada, 2015; Nirubana  et al., 2021). Developing cultivars with improved protein and micronutrient contents has become increasingly important in addressing hidden hunger, malnutrition problems and improving nutritional security, particularly in regions where millets constitute an important component of the daily diet (Food and Agriculture Organization, 2023; International Crops Research Institute for the Semi-Arid Tropics, 2023; Thakur and  Saini, 1995; Vishnuprabha and Vanniarajan, 2018).
     
    Correlation analysis of nutritional traits
     
    The heatmap illustrates the correlation among different nutritional traits of kodo millet varieties, namely protein, carbohydrate, fat, iron and ash content (Fig 2). Protein exhibited a strong positive association with iron (r=0.68) and ash (r=0.76) content, indicating that varieties with higher protein levels also tended to possess greater mineral content. This relationship suggests that simultaneous improvement of protein and iron through selection may be feasible, which is advantageous for breeding nutritionally superior cultivars (Goron and Raizada, 2015). Iron also showed a moderately strong positive correlation with ash (r=0.62), revealed the contribution of mineral accumulation to the total ash content of the grain. Similar positive associations between mineral content and ash have been reported in cereal grains and millets, suggesting ash serve as an indicator of total mineral composition (Saleh et al., 2013).Fat displayed moderate positive correlation with ash content but weak association with iron and carbohydrates. Carbohydrate showed a weak or negative relationship with iron, suggesting that higher carbohydrate concentration may not contribute to increased iron accumulation (Yadav et al., 2020). Overall, the heatmap reveals the interrelationship among nutritional parameters and helps identify traits that can be simultaneously improved in breeding programs for nutritionally superior kodo millet varieties.

    Fig 2: Pearson correlation heatmap showing relation among nutritional traits in kodo millet genotypes.


     
    Cluster analysis of kodo millet varieties
     
    Hierarchical cluster analysis (HCA) was performed using nutritional parameters to assess the similarity among the eleven Kodo millet germplasm. The dendrogram classified the varieties into two clusters represent variability among different kodo millet varieties based on their nutritional characteristics (Fig 3). The varieties were grouped according to their similarity, where closely linked varieties indicate similar nutritional composition. The clustering pattern reflects the degree of similarity among the varieties, where shorter linkage distances indicate greater nutritional resemblance while longer distances represent higher divergence (Mohammadi and Prasanna, 2003).
           
    The first cluster (cluster 1) contains KMV1, KMV5, KMV2 and KMV8. Within this cluster, KMV2 and KMV8 were most closely related varieties, joining at the lowest linkage distance, suggesting that these genotypes possess highly similar nutritional composition. KMV5 subsequently clustered with this pair, while KMV1 joined at a comparatively higher linkage distance, indicating that although nutritionally related, it is relatively more distinct than the other genotypes of this cluster. The second cluster (Cluster II) consisted of RK250-90, KMV3, KMV7, KMV4, KMV9, KMV10 and KMV6. In this cluster, KMV3 and KMV7 formed the closest pair indicating a high degree of nutritional similarity. KMV4and KMV9 clustered together at a short linkage distance, suggesting similar nutritional profiles. KMV10 and KMV6 also formed a closely related subgroup, reflecting comparable nutritional characteristics. These three sub-group were subsequently merged to form the larger cluster II.
           
    The present investigation highlights the nutritional significance and variability among different Kodo millet genotypes. Future research may focus on molecular characterization of nutritionally superior genotypes to identify genes associated with enhanced protein and mineral accumulation. Such studies can support marker-assisted breeding and genetic improvement programs. Processing techniques including germination, fermentation, malting and extrusion may be optimized to enhance nutritional quality and functional properties of foods based on millets (Dekka et al., 2023). Development of value-added meal products may help increase consumer awareness and commercial utilization of Kodo millet. Shelf-life studies and storage behavior of millet grains and processed products should also be investigated to improve product stability and marketability. Multi-location and multi-season trials involving larger germplasm collections are required to evaluate genotype x environment interactions affecting nutritional traits. Integration of advanced statistical tools, genomics, metabolomics and precision breeding approaches may further accelerate the development of high-yielding and nutritionally superior Kodo millet cultivars suitable for sustainable agriculture and nutritional security.

    CONCLUSION

    The present study emphasized that Kodo millet (Paspalum scrobiculatum L.) possesses substantial nutritional value and significant variability among different genotypes for important nutritional traits. The analyzed varieties showed appreciable levels of protein, carbohydrates, fat, iron and ash content, confirming the importance of Kodo millet as a nutrient-rich cereal crop. RK 250-90 exhibited superior nutritional characteristics, particularly for protein and mineral content, indicating its potential use in breeding and varietal improvement programs. Promotion of nutritionally superior varieties, along with improved processing and value-addition technologies, may enhance consumer acceptance and utilization of this underexploited millet. Further research involving larger germplasm collections, molecular characterization and evaluation of bioactive compounds may provide additional insights for crop improvement and functional food development.

    ACKNOWLEDGEMENT

    This work was supported by Shri Guru Ram Rai University through the Seed Money Funded Grant Project. The authors express their sincere gratitude to the Department of Botany, School of Basic and Applied Sciences, Shri Guru Ram Rai University, Patel Nagar Campus, Dehradun, Uttarakhand, for their valuable support and facilities provided during the course of this research.
     
    Disclaimers
     
    The views and conclusions expressed in this research article are solely those of the authors and do not necessarily reflect the views of their affiliated institution. The authors are responsible for the accuracy and completeness of the information presented, but they do not accept any liability for any direct or indirect losses resulting from the use of this content.

    CONFLICT OF INTEREST

    Authors have declared that no competing interests exist.

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