Near-Infrared Spectroscopy to Predict Provitamin A Carotenoids Content in Maize

Abstract

Vitamin A deficiency (VAD) is a public health issue worldwide. Provitamin A (PVA) biofortified maize serves as an alternative to help combat VAD. Breeding efforts to develop maize varieties with high PVA carotenoid content combine molecular and phenotypic selection strategies. The phenotypic assessment of carotenoids is currently done using liquid chromatography, a precise but time- and resource-consuming methodology. Using near-infrared spectroscopy (NIRS) could increase the breeding efficiency. This study used ultra-performance liquid chromatography (UPLC) data from 1857 tropical maize genotypes as a training set and NIRS data to do an independent test of a set of 650 genotypes to predict PVA carotenoids using Bayesian and modified partial least square (MPLS) regression models. Both regression methods produced similar prediction accuracies for the total carotenoids (r2 = 0.75), lutein (r2 = 0.55), zeaxanthin (r2= 0.61), β-carotene (r2 = 0.22) and β-cryptoxanthin (BCX) (r2 = 0.57). These results demonstrate that Bayesian and MPLS regression of BCX on NIRS data can be used to predict BCX content, the current focus on PVA enhancement, and thus offers opportunities for high-throughput phenotyping at a low cost, especially in the early stages of PVA maize breeding pipeline when many genotypes must be screened.

Plant phenotyping relevance

NIRSと回帰モデルを用いてトウモロコシ種子のプロビタミンAカロテノイド含量を高スループット推定する手法が研究の中心であり、育種用フェノタイピングへの応用を技術的に評価している。

Code and data availability

The paper's maize carotenoid/NIRS datasets are publicly deposited in a CIMMYT repository with an explicit download link; the analysis R code is only in the article appendix, not a separate public deposit.