在新鲜茶叶中-反应和Vis/NIR预测
Qing Luo1, Ting Tang1, Yuxin Duan1
1College of Engineering, South China Agricultural University, Guangzhou 510642, China.
Food chemistry
|February 19, 2025
概括
这项研究分析了茶叶中的和含量,并使用Vis/NIR光谱学开发了预测模型. 这些模型有助于精确的受精和现场检测新鲜茶中的营养水平.
科学领域:
- 农业科学 农业科学
- 植物营养 植物营养
- 频谱学是一种光谱学.
背景情况:
- (N) 和 (P) 对茶叶植物生长至关重要.
- 茶叶植物的不同部分的营养含量有很大差异.
- 新鲜茶叶中的N和P含量存在有限的研究.
研究的目的:
- 在不同的应力下分析新鲜茶叶中的含量 (NC) 和含量 (PC).
- 使用Vis/NIR光谱和智能算法开发定量回归模型来预测NC和PC.
- 调查预处理算法对模型预测性能的影响.
主要方法:
- 使用Vis/NIR光谱法获取光谱数据.
- 各种智能算法被用于定量回归建模.
- 预处理算法对其对模型准确性的影响进行了评估.
- 开发并测试了特定的模型 (NC 的VMDSG-D1-VCPA-IRIV-SVR,PC 的VMDSG-CARS-Stacking).
主要成果:
- 在不同茶叶植物部位中观察到NC和PC的显著差异.
- 预处理算法的选择对模型预测性能产生了重大影响.
- 开发的VMDSG-D1-VCPA-IRIV-SVR和VMDSG-CARS-Stacking模型分别显示了NC和PC的高预测精度.
- 测试组相关系数超过0.85,RPD值大于1.8.
结论:
- 该研究成功地建立了新鲜茶叶中NC和PC的准确预测模型.
- 这些发现为茶叶种植中精确施肥策略提供了基础.
- 开发的光谱方法能够有效地在现场检测茶叶植物中的营养状况.
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