带有包装多输出回归的人工智能,使用NIR光谱识别新鲜和干燥便样本的化学含量
Kamini G Panchbhai1, Sarvesha S Shetgaonkar2, Pranay P Morajkar2
1Goa College of Pharmacy, Panaji, Goa 403001, India.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|January 12, 2025
概括
近红外 (NIR) 谱学与包装式多输出回归算法 (WMRA) 和k-最近邻居 (KNN) 结合,准确地检测到关键的肥营养素. 这种方法提供了一种快速可靠的方法,用于分析新鲜和干燥肥料中的基本元素.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 精确分析肥料营养素对于优化作物营养,提高农场利能力和最大限度地减少环境污染至关重要.
- 传统的肥营养分析方法可能是耗时和劳动密集的.
研究的目的:
- 评估近红外光谱法 (NIR) 的有效性,以快速量化新鲜和干燥肥料中的关键营养素.
- 为了比较不同预处理技术和机器学习算法的性能,用于营养分析.
主要方法:
- 使用了两个NIR光谱数据集,用于新鲜和干燥的便样本.
- 应用的预处理方法包括萨维茨基-戈莱 (SG),乘法散射校正 (MSC) 和标准正常变量 (SNV),其次是主要成分分析 (PCA).
- 使用包装机多输出回归算法 (WMRA) 进行回归和k-最近邻居 (KNN) 进行分类以识别化学含量.
主要成果:
- WMRA实现了高的确定系数 (R2):新鲜肥的0.983和干肥的0.949.
- KNN分类显示出极好的准确性,在某些情况下达到100.0%,平均5倍准确率为98.1%.
- 该研究确定了关键营养素的最佳光谱签名峰值,如总,干物质和各种氧化物.
结论:
- 当与WMRA和KNN集成时,NIR光谱为快速准确检测肥中的必需营养素提供了一个强大的工具.
- 这种方法促进了农业中有效的营养管理,有助于可持续的做法和提高经济回报.
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