从根到结果:基于便携式NIRS的麻豆质量特征的非破坏性预测
Paulo Henrique Ramos Guimarães1, Cinara Fernanda Garcia Morales1, Tamires Sousa Cerqueira1
1Embrapa Mandioca e Fruticultura, Cruz das Almas, Brazil.
PloS one
|December 3, 2025
概括
便携式近红外光谱 (NIRS) 能够快速,非破坏性地表型化木麻的质量特征,如干物质含量. 这项技术,特别是粉碎的根样,通过提高选效率,显著加快了子的育种速度.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 频谱学是一种光谱学.
背景情况:
- 大麻是一种重要的主食作物,但传统的质量特征表型是缓慢和劳动密集的.
- 对干物质含量 (DMC) 和粉含量 (StC) 等特征的准确和快速评估对于麻养殖计划至关重要.
研究的目的:
- 评估手持近红外光谱仪 (NIRS) 对于快速,非破坏性预测麻豆质量特征的有效性.
- 用新鲜与粉碎根样本以及各种光谱预处理和机器学习算法来比较预测模型性能.
主要方法:
- 使用手持式NIRS设备收集了2,236个麻豆克隆的光谱数据.
- 在新鲜和粉碎的根样本上测试了六种光谱预处理方法和三种机器学习算法 (PLS,SVM,XGB).
- 使用R2,RMSE和Kappa指数评估模型性能,以获得克隆选择的一致性.
主要成果:
- 在所有测试模型中,与新鲜样品相比,粉碎的根样始终提供了优异的预测性能.
- 带有特定预处理 (例如,SG+SNV) 的部分最小平方 (PLS) 算法实现了高精度 (R2 > 0.96) 的DMC预测.
- 基于NIRS的选择显示出与传统方法的强烈一致,卡帕指数值接近1.0.0.
结论:
- 便携式NIRS是一个可靠和高效的工具,用于高吞吐量表型化在子育种.
- 最佳的样本准备 (粉碎根) 和强大的建模 (PLS) 是准确预测的关键.
- 这项技术通过实现快速的早期选择来加速繁殖周期,从而增强遗传改进.
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