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New data preprocessing trends based on ensemble of multiple preprocessing techniques
Trends in Analytical Chemistry ( IF 13.1 ) Pub Date : 2020-09-19 , DOI: 10.1016/j.trac.2020.116045
Puneet Mishra , Alessandra Biancolillo , Jean Michel Roger , Federico Marini , Douglas N. Rutledge

Data generated by analytical instruments, such as spectrometers, may contain unwanted variation due to measurement mode, sample state and other external physical, chemical and environmental factors. Preprocessing is required so that the property of interest can be predicted correctly. Different correction methods may remove specific types of artefacts while still leaving some effects behind. Using multiple preprocessing in a complementary way can remove the artefacts that would be left behind by using only one technique. This article summarizes the recent developments in new data preprocessing strategies and specifically reviews the emerging ensemble approaches to preprocessing fusion in chemometrics. A demonstration case is also presented. In summary, ensemble preprocessing allows the selection of several techniques and their combinations that, in a complementary way, lead to improved models. Ensemble approaches are not limited to spectral data but can be used in all cases where preprocessing is needed and identification of a single best option is not easily done.



中文翻译:

基于多种预处理技术集合的新数据预处理趋势

由于测量模式,样品状态以及其他外部物理,化学和环境因素,分析仪器(例如光谱仪)生成的数据可能包含不必要的变化。需要进行预处理,以便可以正确预测感兴趣的属性。不同的校正方法可以删除特定类型的伪像,同时仍然留下一些效果。以互补的方式使用多个预处理可以消除仅使用一种技术会留下的伪像。本文总结了新数据预处理策略的最新进展,并特别回顾了化学计量学中预处理融合的新兴集成方法。还演示了一个案例。总而言之,集成预处理允许选择几种技术及其组合,以互补的方式,导致模型的改进。集成方法不仅限于光谱数据,还可以用于需要预处理且难以识别单个最佳选项的所有情况。

更新日期:2020-10-06
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