Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

Molecules

Rubina Shaikh¹  Ervin Nippolainen¹  Isaac Afara¹  Boris Zimmermann²  Achim Kohler²  Johanne Heitmann Solheim²  Tiril Aurora Lintvedt²  Valeria Tafintseva²  Hafeez Ur Rehman²  Patrick Krebs³  Boris Mizaikoff³  Polina Fomina³  Vesa Virtanen⁴  Lassi Rieppo⁴  Simo Saarakkala⁴ 

[1] Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland;Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway;Institute of Analytical and Bioanalytical Chemistry, Ulm University, 89081 Ulm, Germany;Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, 90220 Oulu, Finland;

关键词: preprocessing;    sparse spectra;    multiplicative signal correction;    quantum cascade lasers;   

DOI  :  10.3390/molecules27030873

来源: DOAJ

【 摘 要 】

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.

【 授权许可】

Unknown