Ionomic variation characterization in African leafy vegetables for micronutrients using xrf and hplc

Abstract

Beside the phytochemical, ionomic fingerprinting represents the inorganic trace element composition of the cellular and organismal component. High-throughput elemental analysis technologies, such as X-Ray Fluorescence (XRF), are applied to ionomic analysis whereas phyto-chemical analyses tend to be in vitro. Both could contribute to: (a) insights on ionome-phytochem micronutrient composition; (b) genetic diversity variant discrimination among accessions to allow simple grouping; (c) core and/or reserve collections rationalization; (d) integration of bioinformatic and genetic tools; and (e) micronutrient-dense varietal improvement and/or cropping decisions. First, variant discrimination was appraised on individual element criterion (e.g. K or Ca or Fe or Zn). Second, on a multiple element (K & Ca & Fe & Zn) condition upon which nutrahealth-implied ionomic variant conditions were rationalized as: (a) Core ‘exceptional’ grade collections scored between E8 and E10; (b) Core ‘Medium’ grades (M5-M7); and (c) ‘Least’ exceptional (Ll-L4) scored and regarded as reserve collections. Objectives were to: (1) Investigate a phyotchem background among the selected African Leafy Vegetables (ALVs) for coupling with ionome grading; (2) Assess variation among accessions based on single element criterion; (3) Characterize cumulative nutrahealth-implied ionomic variation among ALVs. Eleven ALV accessions were raised from seed at the University of Nairobi Greenhouse (2006) and leafy parts harvested. XRF was carried out at the Institute of Nuclear Science and Technology, University of Nairobi. High Performance Liquid Chromatographic (HPLC) analyses were done at the Tanzania Food and Nutritional Centre. Highly significant density variation (p< 0.001) among accessions due to Lutein and â-carotene suggested the phytochem effectiveness for variant discrimination accounting for 0.79 and 0.87 of (R2) variation. The single element K discriminator basis was highly significant (p<.001) relative to the other elements but shyly corrected with only Fe. Latter’s discrimination activity, however, correlated with 3 elements as thus: Mn’s (r=0.64; p< .001); Ca’s (r=0.51; p< .003); and leaf K’s (r= 0.35 at p< 0.05). Leaf Ca’s also correlated with three: with Mn’s (0.52; p< .003); Fe’s (already shown); and Zn (r=.39; p<.03). Data suggest that ionomic variation discriminator ability on Fe & Ca single element selection basis may ‘walk’ with others. Conclusions are: (1) that for primary data mining, XRF can be utilized as the first course of action for large-sized ionomic screening which can be rationalized into Core and Reserve collections to precede phytochem screening for utilization and/or conservation.