数据驱动版本的多路软独立建模类类类比 (N-WayDD-SIMCA):理论和应用
Ariana P Pagani1,2, Gonzalo Camargo1,2, Gabriela A Ibañez1,2
1Departamento de Química Analítica, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Suipacha 531, 2000 Rosario, Argentina.
Analytical chemistry
|March 12, 2024
概括
本研究介绍了用于一类分类 (OCC) 的高级多路数据分析方法. 在多块和塔克3格式的数据驱动软独立类类类比建模 (DD-SIMCA) 有效地分类样本,包括真实和伪造的蓝提取物.
科学领域:
- 化学测量 化学测量 化学测量
- 分析化学 分析化学
- 数据科学数据科学数据科学
背景情况:
- 一类分类 (OCC) 对于识别数据集中的异常至关重要.
- 多路数据分析为复杂数据集提供了先进的功能.
- 数据驱动的类类类比的软独立建模 (DD-SIMCA) 是分类任务的强大工具.
研究的目的:
- 应用和比较多块和Tucker 3 N-way DD-SIMCA,以对多路数据进行一类分类.
- 为N-way DD-SIMCA引入一个新的决策规则.
- 评估这些方法对变量和模型复杂性选择的实用性.
主要方法:
- 多块DD-SIMCA用于变量选择的应用.
- 应用Tucker 3 N-way DD-SIMCA用于模型复杂性选择和样本评估.
- 开发基于双向DD-SIMCA的N-way DD-SIMCA的新决策规则.
- 使用流注射分析分析蓝提取物样本的光谱时间数据的分析.
主要成果:
- 多块SIMCA在变量选择方面被证明是有效的.
- 塔克3 SIMCA 促进了最佳模型复杂性选择和样本角色评估.
- 新的N-way DD-SIMCA决策规则表现良好.
- 对比表明,在未折叠的数据上,N-way方法对双向DD-SIMCA的有效性.
结论:
- 多路块和塔克3N路DD-SIMCA是对多路数据的一类分类有价值的工具.
- 当数据缺乏三线性时,塔克3模型是必要的,正如在蓝提取物数据集中观察到的.
- 这些方法提供了可靠的分类和对样本特征和模型参数的洞察.
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