通过融合太赫兹时域光谱学和可解释的机器学习来准确预测跨物种木材密度
Min Yu1, Jia Yan1, Jiawei Chu1
1Key Laboratory of National Forestry and Grassland Administration "Wood Quality Improvement & Efficient Utilization", School of Materials and Chemistry, Anhui Agricultural University, Hefei 230036, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|December 17, 2025
概括
太赫兹时域光谱 (THz-TDS) 与机器学习相结合,可以准确预测木材密度. 这种方法为各种物种木材密度检测提供了快速而精确的解决方案.
科学领域:
- 材料科学 材料科学 材料科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 精确的木材密度测定对于材料科学应用至关重要.
- 传统的木材密度评估方法可能耗时且具有破坏性.
- 开发用于木材密度预测的非破坏性,快速和准确的方法是非常可取的.
研究的目的:
- 调查太赫兹时域光谱学 (THz-TDS) 用于木材密度预测的应用.
- 开发和比较机器学习模型,以基于THz光谱数据预测木材密度.
- 分析特征的重要性,并使用SHAP值来解释预测模型.
主要方法:
- 使用太赫兹时域光谱法 (THz-TDS) 来获得木材样本的折射率.
- 机器学习算法包括弹性净回归 (ENR),随机森林 (RF) 和极端梯度提升 (XGBoost) 用于模型开发.
- 用于特征选择的非信息变量消除 (UVE) 和用于超参数调整的粒子群集优化 (PSO) 得到了利用.
- 沙普利增量解释 (SHAP) 方法用于模型的解释性.
主要成果:
- 在木材密度和THz折射率之间观察到正相关性.
- 对于快速生长的树种,XGBoost模型实现了高预测精度 (R2 = 0.9594).
- 将数据集扩展到包括Pterocarpus木种,提高了准确性 (R2 = 0.9846).
- 使用UVE的特征选择进一步提高了预测准确性 (R2 = 0.9862) 并降低了计算复杂性.
- SHAP分析确定了0.2-0.3 THz频段的关键预测频率,其中0.216 THz是最重要的.
结论:
- THz-TDS与机器学习相结合,为跨物种木材密度预测提供了一种有效和通用的方法.
- 综合方法可以快速,精确和非破坏性地检测木材密度.
- 该研究强调了THz光谱在木材科学中的定量分析潜力.
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