Predicting starch content in cassava fresh roots using near-infrared spectroscopy.

ABSTRACT: The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740-1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorith

SUBMITTER: Nkouaya Mbanjo EG 

PROVIDER: S-EPMC9679500 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature