Machine learning technique unraveled subspecies‐specific ionomic variation with the preferential mineral enrichment in rice

Abstract

Background and ObjectivesTo enrich micronutrients in rice through breeding, screening a large diversity panel of major sub‐species of rice accessions for their ionomes is necessary.FindingsInductively coupled plasma optical emission spectroscopy (ICP‐OES) was deployed to profile grain ionomes from 1100 rice accessions. Classification models derived for multi‐dimensional ionomics data using random forest model, support vector machine, and artificial neural network predicted three distinct groups differ in ionomic levels with higher prediction accuracy (76.8 – 85.1%). While class A showed inferior lines in mineral content, class B and C represent superior lines with enriched multiple minerals. The identified contrasting lines from the modeling classifications were milled and profiled for minerals and toxic elements using inductively coupled plasma mass spectrometry (ICP‐MS) and revealed donors with an elevated mineral content even higher than the Zn‐biofortified rice lines.ConclusionOverall, the result of this study provided new insights into ionomic variation among subspecies with a preferential enrichment of minerals in temperate japonica and admixed populations coincided with increased grain protein content, while in indica median mineral content is low while selected for increased amylose content.Significance and NoveltyArtificial intelligence models developed to predict grains enriched with multiple minerals.This article is protected by copyright. All rights reserved.

The deterioration of starch physiochemical and minerals in high-quality indica rice under low-temperature stress during grain filling

“…However, our results align with Jiang et al. (2007) and Tiozon et al. (2023) , who reported a significantly negative correlation between amylose content and K, Mg, and Mn levels.…”

“…Although rice is not known to be mineral-rich, it can still serve as an essential source of caloric energy and macro-micro nutrients for those who consume it as a staple food ( Huang et al., 2020 ). Except for genotypic influences, environmental factors have been documented to affect minerals in rice grain ( Du et al., 2013 ; Huang et al., 2016 ; Tiozon et al., 2023 ). Previous studies have shown that atmospheric CO 2 levels and temperature ( Myers et al., 2014 ; Chaturvedi et al., 2017 ), application of nitrogen ( Gu et al., 2015 ; Wang et al., 2018 ) and water management ( Xu et al., 2019 ) could affect the mineral nutrients of rice grains.…”