An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples

Abstract

In infrared spectroscopy of thin film samples, interference introduces distortions in spectra, commonly referred to as fringes. Fringes may alter absorbance peak ratios, which hampers the spectral analysis. We have previously introduced extended multiplicative signal correction (EMSC) for fringes correction. In the current article, we provide a robust open-source algorithm for fringe correction in infrared spectroscopy and propose several improvements to the Fringe EMSC model. The suggested algorithm achieves a more precise fringe frequency estimation by mean centering of the measured spectrum and applying a window function prior to the Fourier transform. It selects two frequencies from a user defined number of maxima in the Fourier domain. The improved Fringe EMSC algorithm is validated on two experimental datasets, one of them being a hyperspectral image. Techniques for separating sample spectra from background spectra in hyperspectral images, and techniques to identify spectra affected by fringes are also provided.

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

“…Several EMSC-based models are built by including spectra as interferent spectra. Among the most known EMSC-based methods we find the Mie extinction EMSC (ME-EMSC) [ 13 ], the fringe EMSC [ 17 ] and the replicate EMSC [ 18 , 19 ]. Throughout the remainder of the paper, we illustrate how analyte spectra can be used to gain additional information about the chemical composition of the samples.…”

“…One example is Mie scattering, which results in scattering features that have been observed in measurements of spherical or approximately spherical samples, such as single cells [ 13 , 28 , 29 ]. Another example is multiple internal reflections in thin film samples, which causes fringes in the infrared spectra [ 17 , 30 ].…”

Diffraction-limited mid-infrared microspectroscopy to reveal a micron-thick interfacial water layer signature

“…Some models allow suppressing the interference fringes in FTIR data. 87,88,94,95 A modified interferogram can suppress interference fringes in the absorption/transmission spectrum by replacing the sinusoidal trend in the interferogram with a horizontal line. 96 Using the central burst to model and eliminate the side bursts is another approach to directly edit the interferogram for rectification.…”

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

“…ATR-IR spectra are recorded by placing a sample in contact with internal reflection elements, such as diamond or zinc selenide crystals. In general, ATR-IR data is devoid of many unwanted spectral variations that are commonly encountered in transmittance IR data, such as scattering of the infrared radiation at a sample surface or interior (for example scattering artifacts in microparticulate samples), and multiple reflections of the infrared radiation within a thin sample (for example interference fringes in film samples) [ 24 , 25 , 26 ]. The datasets analyzed in the study are FTIR spectra of bovine and human cartilage, and the datasets were truncated into the sparse datasets by selecting seven wavenumbers.…”