Development of prediction models for high throughput phenotyping of protein and essential amino acids content in rice grain using the near infrared reflectance spectroscopy

Abstract

Due to polygenic nature of grain protein and essential amino acids, a high throughput methodology is required for identification of desired segregants and improvement of rice simultaneously for protein quality and quantity in a cost-effective way. Data from chemical analysis of samples of 150 rice genotypes with a substantially wide range were used here to develop prediction models for high-throughput estimation of grain protein content (GPC) and essential amino acids (EAA) content using near-infrared spectroscopy (NIRS). Various mathematical pretreatments were employed under modified partial least squares(mPLS) models to ascertain the optimal mathematical equation for prediction based on the lowest standard error of cross-validation, the highest 1-VER (1minus variance ratio), the highest coefficient of determination (RSQ) and the lowest standard error of calibration (SEC). The optimal pretreatment for GPC and EAA were 1,6,6,1 and 2,8,8,1, respectively. These models were validated through paired t-tests. Association (R²) between the predicted and reference values varied from 0.909 to 0.967, revealing prediction models' higher accuracy and effectiveness. This study was further extended by applying those prediction models for estimating GPC and EAA in a mapping population to identify genomic regions for qualitative and quantitative improvement of grain protein.

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.