Back to Search Start Over

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

Authors :
Valeria Tafintseva
Tiril Aurora Lintvedt
Johanne Heitmann Solheim
Boris Zimmermann
Hafeez Ur Rehman
Vesa Virtanen
Rubina Shaikh
Ervin Nippolainen
Isaac Afara
Simo Saarakkala
Lassi Rieppo
Patrick Krebs
Polina Fomina
Boris Mizaikoff
Achim Kohler
Source :
Molecules, Vol 27, Iss 3, p 873 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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.

Details

Language :
English
ISSN :
27030873 and 14203049
Volume :
27
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Molecules
Publication Type :
Academic Journal
Accession number :
edsdoj.bf64082d8d3a4264ab140f39e02cb289
Document Type :
article
Full Text :
https://doi.org/10.3390/molecules27030873