使用波波变换与可解释的机器学习模型相结合,近红外预测核桃核中的素含量
Qiuhao Xia1,2,3, Langqin Luo2,3,4, Yerhazi Yerzati1,2,3
1College of Horticulture and Forestry, Tarim University, Alar, China.
Frontiers in plant science
|February 23, 2026
概括
近红外光谱学与机器学习相结合,准确地预测了核桃素含量. 这种方法提高了核桃的质量控制,通过快速,非破坏性的素量化.
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:
- 素含量对于核桃的味道和质量评估至关重要.
- 精确的素检测对于核桃质量管理至关重要.
研究的目的:
- 开发一种有效的方法来预测核桃核的素含量.
- 使用近红外 (NIR) 光谱和机器学习来定量色素.
主要方法:
- 从180个核桃核样本中收集了NIR光谱 (4000-10000 cm-1).
- 使用数学转换和连续波束转换 (CWT) 处理的光谱数据.
- 构建了一个随机森林 (RF) 模型,使用SHAP解释来预测色.
主要成果:
- 红外线反射率与素含量正相关.
- 结合的光谱变换 (第一阶差异和CWT) 提高了预测的准确性.
- 最佳射频模型在验证集上实现了R2 = 0.831.
结论:
- 结合光谱变换和波波分析,提高了核桃中色素预测的准确性.
- 射频模型为快速,非破坏性的素量化提供了一个潜在的解决方案.
- SHAP算法提高了核桃质量控制预测模型的可解释性.
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