Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

ABSTRACT: Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassi

SUBMITTER: Rehman HU 

PROVIDER: S-EPMC9000438 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature