Multivariate analysis of seed mineral composition in lablab bean (Lablab purpureus L.) germplasm

This study assessed seed mineral composition in lablab bean ( Lablab purpureus L.), an underutilized legume with potential to combat micronutrient malnutrition, focusing on key micronutrients (Fe, Zn, Cu, Mn) and macronutrients (P, K). Seed samples were oven-dried, finely ground and subjected to di-acid (HNO₃:HClO₄, 9:4) wet digestion following standard protocols to ensure complete oxidation of organic matter and release of mineral elements. The digested extracts were diluted to a known volume and used for analysis. Micronutrients were determined using an Atomic Absorption Spectrophotometer (AAS), while phosphorus and potassium were estimated using a spectrophotometer and flame photometer, respectively. Wide differences were observed, with Fe ranging from 71.95 mg/kg (IC-285148) to 212 mg/kg (IC-556845) and Zn from 34.3 mg/kg (IC-413709) to 76.5 mg/kg (EC-454159). Copper content varied between 8.6 mg/kg (IC-285148) and 35.3 mg/kg (EC-454159), while Mn ranged from 14.25 to 28.5 mg/kg. Among macronutrients, P content spanned 0.352% (IC-556765) to 0.505% (IC-586963) and K from 0.942% (IC-285115) to 1.761% (Pusa Garima). High coefficients of variation for Cu (37.79%) and Fe (28.87%) reflected considerable diversity. Correlation analysis showed significant positive associations of Fe with P (r = 0.465) and Zn with Cu (r = 0.550, p < 0.01), while negative associations such as K with Zn (r = -0.250) suggested nutrient trade-offs. Principal component analysis (PCA) explained 79.29% of the total variance, with PC1 (34.55%) capturing co-variation of Fe, Zn, Cu, and P, and PC2 (26.57%) defined by Mn and K. Hierarchical cluster analysis (HCA) classified the accessions into five groups, highlighting IC-556845, EC-454159, IC-856772, RCPD-16, IC-285115, and IC-586963 as mineral-rich lines. These results are important because they provide a scientific basis for utilizing lablab bean as a sustainable source of essential dietary minerals. The identified lines represent valuable resources for breeding and biofortification programs aimed at addressing widespread Fe- and Zn-deficiency malnutrition in developing regions. • Substantial genetic diversity recorded in lablab bean seed mineral content. • Fe ranged 71.95–212 ppm and Zn 34.3–76.5 ppm across evaluated genotypes. • IC-556845 (Fe-rich) and EC-454159 (Zn-rich) emerged as key donor lines. • PCA revealed Fe–Zn–Cu–P as major enrichment axis, distinct from Mn–K. • Mineral-rich genotypes identified for targeted biofortification breeding.

Authors

Institutions

Publication Details

Journal
Journal of Food Composition and Analysis
Published
2026-05-17
DOI
https://doi.org/10.1016/j.jfca.2026.109253
Primary Topic
Agricultural pest management studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multivariate analysis of seed mineral composition in lablab bean (Lablab purpureus L.) germplasm

Arbind Kumar Choudhary, Kamal Prasad Mahapatra, Kumari Shubha, S Naresh Kumar et al.
Journal of Food Composition and Analysis
Agricultural pest management studies
article

Multivariate analysis of seed mineral composition in lablab bean (Lablab purpureus L.) germplasm

Arbind Kumar Choudhary, Kamal Prasad Mahapatra, Kumari Shubha, S Naresh Kumar, Anup Das, Kirti Saurabh, Anirban Mukherjee
article en

Abstract

This study assessed seed mineral composition in lablab bean ( Lablab purpureus L.), an underutilized legume with potential to combat micronutrient malnutrition, focusing on key micronutrients (Fe, Zn, Cu, Mn) and macronutrients (P, K). Seed samples were oven-dried, finely ground and subjected to di-acid (HNO₃:HClO₄, 9:4) wet digestion following standard protocols to ensure complete oxidation of organic matter and release of mineral elements. The digested extracts were diluted to a known volume and used for analysis. Micronutrients were determined using an Atomic Absorption Spectrophotometer (AAS), while phosphorus and potassium were estimated using a spectrophotometer and flame photometer, respectively. Wide differences were observed, with Fe ranging from 71.95 mg/kg (IC-285148) to 212 mg/kg (IC-556845) and Zn from 34.3 mg/kg (IC-413709) to 76.5 mg/kg (EC-454159). Copper content varied between 8.6 mg/kg (IC-285148) and 35.3 mg/kg (EC-454159), while Mn ranged from 14.25 to 28.5 mg/kg. Among macronutrients, P content spanned 0.352% (IC-556765) to 0.505% (IC-586963) and K from 0.942% (IC-285115) to 1.761% (Pusa Garima). High coefficients of variation for Cu (37.79%) and Fe (28.87%) reflected considerable diversity. Correlation analysis showed significant positive associations of Fe with P (r = 0.465) and Zn with Cu (r = 0.550, p < 0.01), while negative associations such as K with Zn (r = -0.250) suggested nutrient trade-offs. Principal component analysis (PCA) explained 79.29% of the total variance, with PC1 (34.55%) capturing co-variation of Fe, Zn, Cu, and P, and PC2 (26.57%) defined by Mn and K. Hierarchical cluster analysis (HCA) classified the accessions into five groups, highlighting IC-556845, EC-454159, IC-856772, RCPD-16, IC-285115, and IC-586963 as mineral-rich lines. These results are important because they provide a scientific basis for utilizing lablab bean as a sustainable source of essential dietary minerals. The identified lines represent valuable resources for breeding and biofortification programs aimed at addressing widespread Fe- and Zn-deficiency malnutrition in developing regions. • Substantial genetic diversity recorded in lablab bean seed mineral content. • Fe ranged 71.95–212 ppm and Zn 34.3–76.5 ppm across evaluated genotypes. • IC-556845 (Fe-rich) and EC-454159 (Zn-rich) emerged as key donor lines. • PCA revealed Fe–Zn–Cu–P as major enrichment axis, distinct from Mn–K. • Mineral-rich genotypes identified for targeted biofortification breeding.

Journal of Food Composition and AnalysisVol. 155
ICAR-National Bureau Of Plant Genetic Resources (IN), ICAR Research Complex for NEH Region (IN)
Zero hunger
Openalex Percentile: Top 7%
Agricultural pest management studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.