作物学报 ›› 2026, Vol. 52 ›› Issue (4): 1088-1102.doi: 10.3724/SP.J.1006.2026.54108
蒋嘉卉1,2(
), 江炳志1, 刘冠明2, 王章英1,*(
), 唐朝臣1,*(
)
Jiang Jia-Hui1,2(
), Jiang Bing-Zhi1, Liu Guan-Ming2, Wang Zhang-Ying1,*(
), Tang Chao-Chen1,*(
)
摘要:
紫肉甘薯在品质评价上缺乏精确高效的方法, 已成为其种质资源精准鉴定与遗传改良的关键瓶颈。为此, 本研究基于近红外光谱(NIRS)技术, 针对紫肉甘薯的主要品质性状(包括总淀粉、粗蛋白、还原糖、总黄酮、总酚和花青素), 开展了高通量预测模型的构建与优化。本研究选取150份代表性样品, 结合双重优化策略与机器学习算法, 成功建立并优化了6个高性能预测模型。结果显示, 校正集决定系数(R2C)为0.936~0.992, 交叉验证决定系数(R2CV)为0.918~0.987, 验证集决定系数(R2V)为0.903~0.987, 预测偏差比(RPD)为6.55~19.80, 范围误差比(RER)为21.9~63.4, 这些结果表明, 所构建的模型具有良好的稳定性与可靠的预测能力。这些预测模型为紫肉甘薯品质的高通量评价提供了高效、可行的技术路径, 能够促进营养成分的定量评估和优质种质的高效筛选, 从而为提升紫肉甘薯品质和推动产业可持续发展提供核心驱动力。
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