基于改进的条件相互信息最大化和SHAP分析预测玉米的原油含量
Haichao Zhou1, Xiaodan Ma1, Haiou Guan1
1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Da Qing 163319, China.
Food chemistry
|August 28, 2025
概括
这项研究引入了一种新方法,它结合了有条件的相互信息最大化 (CMIM) 和SHAP,用于使用近红外光谱 (NIR) 准确地预测玉米中的原油脂肪. 这种方法增强了非线性依赖性分析,以改善农业质量检测.
科学领域:
- 农业科学
- 光谱学
- 化学测量
背景情况:
- 传统的条件相互信息最大化 (CMIM) 算法在连续近红外 (NIR) 光谱数据中的分析非线性依赖性方面存在局限性.
- 准确和快速预测作物质量参数,如玉米的原油含量,对于农业管理至关重要.
研究的目的:
- 开发一个新的框架,将改进的CMIM与SHAP (SHapley添加剂解释) 结合起来,以有效地预测玉米的原油含量.
- 克服传统CMIM在NIR光谱分析中处理非线性关系的局限性.
主要方法:
- 实施一个修改后的CMIM算法 (CMIM_KDE),使用连续变量估计而不是离散.
- 采用两阶段过器包装器特征选择策略.
- 与SHAP集成,以便对选定的光谱特征进行解释.
- 使用部分最小平方回归 (PLSR) 和支向量回归 (SVR) 模型进行验证.
主要成果:
- CMIM_KDE方法实现了高预测精度,其R2p值为0.7618 (PLSR) 和0.7531 (SVR).
- 与其他增强策略相比,其平均绩效提高了6.18%和4.42%.
- SHAP 分析确定了与玉米中 CH 组相关的关键波数 (约为 5684 cm-1 和 4312 cm-1),为预测机制提供了洞察力.
- 在外部数据集上验证了该方法的有效性和通用性.
结论:
- 拟议的CMIM_KDE-SHAP框架有效地解决了用于预测玉米原油脂肪含量的NIR光谱分析中的传统CMIM的局限性.
- 这种方法为农产品质量检测提供了可靠的解决方案,在遥感和实时监测方面具有潜在的应用.
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