期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 228, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2022.104617
关键词
High-dimensional data analysis; LASSO regression; Variable selection; Spectroscopy; Oil condition monitoring
类别
资金
- Austrian COMET-Program [872176]
- federal states of Nieder_osterreich and Vorarlberg
- TU Wien
- TU Wien Bibliothek
The aim of this study is to quantify the relationship between different methods of artificial oil alteration and engine oils collected from a passenger car using FTIR spectroscopic data and chemometric methods. The study proposes a comprehensive procedure for the analysis of FTIR spectra and validates its effectiveness on a real-world dataset.
The aim of this work is to quantify the relationship between different methods of artificial oil alteration as well as engine oils collected from a passenger car using FTIR (Fourier-transform infrared) spectroscopic data and chemometric methods. We propose a comprehensive procedure for the analysis of FTIR spectra: First, a reconstruction error based pre-processing to filter non-informative variables is introduced, then simultaneous variable selection and parameter estimation using the (weighted) LASSO is performed. Eventually, post-selection inference is applied to derive confidence intervals for the selected model coefficients. The proposed pre-processing methods do not rely on manual selection of FTIR absorption bands suitable for analysis but perform filtering of non-informative variables objectively. With weighted LASSO, experts' knowledge can be integrated with the model. This pipeline for the analysis of FTIR spectroscopic data is demonstrated and validated on a real-world dataset including series of FTIR spectra of used and artificially altered engine oils.
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