来自拉曼光谱的土壤有机碳的校准不确定性估计
Jeffrey K Wiens1, Natalia Solomatova1, Sadegh Shokatian1
1Miraterra Technologies Corporation, 199 W Sixth Avenue, British Columbia, Vancouver V5Y 1K3, Canada.
Analytical chemistry
|December 11, 2025
概括
本研究引入了一个使用符合性预测的框架,以从拉曼光谱中量化土壤有机碳估计的不确定性. 该方法提供可靠,校准的估计,增强关键应用程序的决策.
科学领域:
- 化学测量 化学测量 化学测量
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 模型分析拉曼光谱以估计土壤有机碳 (SOC).
- 目前用于SOC的ML模型缺乏可靠的不确定性估计,限制了它们的实际使用.
研究的目的:
- 开发一个框架,用转移刺激拉曼差异光谱法 (SERDS) 来量化 SOC 估计中的预测不确定性.
- 整合合规预测 (CP) 与各种不确定性量化 (UQ) 方法用于统计学上有效的预测间隔.
主要方法:
- 使用符合性预测 (CP) 与校准数据集来生成预测间隔.
- 集成CP与多个UQ策略 (深层合奏,贝叶斯神经网络等) 对于 SOC 估计.
- 在一个与现场相关的背景下,解决了 Aleatoric 和 Epistemic 不确定性来源.
主要成果:
- 合规的UQ方法产生了精确校准的不确定性估计,具有狭窄的预测间隔.
- 在各种信任级别实现了可靠的经验覆盖.
- 证明不确定性主要是随机的,强调光谱质量而不是模型复杂性.
结论:
- 拟议的框架为基于拉曼的化学测量的可靠,样本特定的不确定性估计提供了一个实用和可通用的解决方案.
- 在SOC估计中,符合性对于准确的不确定性校准至关重要.
- 提高光谱数据质量和预处理是提高预测性能的关键.
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