实验室内精度的统计建模使用分层贝叶斯式方法.
Daisuke Miyake1, Shigehiko Kanaya2, Naoaki Ono2
1Department of Management-Planning, Japan Food Research Laboratories, Motoyoyogi-cho 52-1, Shibuya-ku, Tokyo 151-0062, Japan.
Journal of AOAC International
|August 27, 2024
概括
本研究引入了一种使用层次贝叶斯模型的回归方法,用于从重复测量中预测实验室内标准偏差 (SD). 该模型准确地估计了各种分析物的精度,有助于内部质量控制和不确定性评估.
科学领域:
- 分析化学 分析化学
- 统计建模 统计建模
背景情况:
- 食品分析中的可复制性得到了很好的研究,通常遵循霍维茨曲线.
- 对可重复性和中间精度的系统预测仍未得到充分研究.
研究的目的:
- 开发一种回归方法,用于估计实验室内部标准偏差 (SD).
- 使用具有重复测量数据的层次贝叶斯模型.
主要方法:
- 采用哈密尔顿蒙特卡洛 (HMC) 方法,使用R与Stan.
- 在统计模型中假设了基平方分布.
- 嵌入的非线性固定效应和lognormal随机效应在一个层次的先前结构.
主要成果:
- 分析了300多个实例,显示出很好的模型匹配,除了水分 (一种方法定义的分析物).
- 该方法适用于通过光谱,GC和HPLC分析的各种分析物.
- 估计的精度通常符合霍维茨比率标准,一些高灵敏度探测器产生较低的SDs.
结论:
- 建议使用预测的实验室内精度用于内部质量控制和测量不确定性估计,独立于样本矩阵.
- 对重复分析数据的统计建模简化了实验室分析系统的精度估计.
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