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

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 (r(2) = 0.75), lutein (r(2) = 0.55), zeaxanthin (r(2)= 0.61), beta-carotene (r(2) = 0.22) and beta-cryptoxanthin (BCX) (r(2) = 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.

Automated summary

This study used Bayesian and modified partial least square regression models to predict the content of provitamin A carotenoids in maize genotypes using near-infrared spectroscopy data. Both regression methods showed similar accuracies in predicting carotenoid content, offering opportunities for cost-effective and high-throughput phenotyping in maize breeding.