Abstract
Leaf-vegetable sweet potato is a nutritious food source, with stem tips as the harvest organ. However, the lack of rapid quantitative approaches for quality evaluation of stem tips largely limits the genetic improvement of leaf-vegetable sweet potato. This study aimed to establish a near-infrared spectroscopy (NIRS) methodology for the rapid analysis of both proximate (cellulose, crude protein, and soluble sugar) and functional (chlorogenic acid, total flavonoid content, and total phenolic content) components in the stem tips. Leveraging a dataset of 140 representative germplasm samples, we developed six robust NIRS models through the optimization of calibration sets and variable selection. These models exhibited exceptional accuracy, with high determination coefficients on calibration (R2C = 0.95–0.98), cross-validation (R2CV = 0.93–0.96), external validation (R2V = 0.91–0.95), and the ratio of prediction to deviation (RPD = 6.78–9.72). Overall, the NIRS models developed through this research facilitate high-throughput profiling of nutritional composition in stem tips, thereby enabling the swift identification of superior germplasm suited for leaf-vegetable sweet potato production.
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