High-throughput phenotyping of nutritional quality components in sweet potato roots by near-infrared spectroscopy and chemometrics methods

Abstract

The lack of an efficient approach for quality evaluation of sweet potatoes significantly hinders progress in quality breeding. Therefore, this study aimed to establish a near-infrared spectroscopy (NIRS) assay for high-throughput analysis of sweet potato root quality, including total starch, amylose, amylopectin, the ratio of amylopectin to amylose, soluble sugar, crude protein, total flavonoid content, and total phenolic content. A total of 125 representative samples were utilized and a dual-optimized strategy (optimization of sample subset partitioning and variable selection) was applied to NIRS modeling. Eight optimal equations were developed with an excellent coefficient of determination for the calibration (R2C) at 0.95–0.99, cross-validation (R2CV) at 0.93–0.98, external validation (R2V) at 0.89–0.96, and the ratio of prediction to deviation (RPD) at 6.33–11.35. Overall, these NIRS models provide a feasible approach for high-throughput analysis of root quality and permit large-scale screening of elite germplasm in future sweet potato breeding.