在PLS回归中使用梯度增强机来优化特征选择,通过NIR光谱学预测多国玉米核中的水分和蛋白质
Runyu Zheng1, Yuyao Jia1, Chidanand Ullagaddi2
1Department of Agricultural and Biological Engineering, University of Illinois at Urbana- Champaign, Urbana, IL, 61801, USA.
Food chemistry
|June 14, 2024
概括
渐变增强机器有效地使用近红外光谱识别了关键波长,以准确预测在全球不同环境中的玉米核中的水分和蛋白质. 这增强了农业和食品分析能力.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
背景情况:
- 玉米核的组成 (水分,蛋白质) 影响营养价值和加工.
- 近红外 (NIR) 光谱是一种用于估计玉米组成的工具.
- 在有限的环境中训练的模型可能低估了错误和偏见.
研究的目的:
- 评估特征选择方法,以改善基于NIR的玉米成分预测.
- 为玉米水分和蛋白质含量开发强大的全球校准模型.
- 评估梯度增强机 (GBM) 用于农业应用中的特征工程.
主要方法:
- 使用近红外 (NIR) 光谱与化学测量和部分最小平方回归 (PLSR).
- 组装了各种各样的国际玉米样本.
- 应用了五种特征选择方法,专注于像CatBoost和LightGBM这样的梯度提升机 (GBM),以识别关键波长.
主要成果:
- GBM有效地选择了水分 (例如1409,1900 nm) 和蛋白质 (例如887,1212 nm) 的关键波长.
- SHAP图形证实了所选波长对模型预测的显著贡献.
- 证明了开发多国全球校准模型的潜力.
结论:
- 梯度增强机对于农业和食品分析中的特征工程非常有效.
- 精确预测玉米水分和蛋白质可以在使用NIR和GBMs的各种环境中实现.
- 这种方法可以改善作物质量基本参数的全球校准模型.
关键词:
组件预测预测的组成部分玉米的谷粒是玉米的核心.功能选择 功能选择梯度增强机器 (GBM) 是一个近红外 (NIR) 光谱学近红外 (NIR) 光谱学部分最小平方回归 (PLSR)沙普利的添加式扩展 (SHAP)更多相关视频
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