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Issue 2, 2021
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Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry

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Abstract

Infrared (IR) imaging can be used for fast, accurate and non-destructive pathology recognition of biopsies when supported by machine learning algorithms. Transflection mode of measurements has the potential to be translated into the clinic due to economic reasons of large-scale imaging with the need for inexpensive substrates. Unfortunately, in this mode spectral distortions originating from light interference appear. Due to this fact transmission measurement mode is more frequently used in pathology recognition. Nevertheless, this measurement mode also is not devoid of spectral distortion effects like scattering. However, this effect is better understood and there are preprocessing algorithms to minimize it. In this work, we investigated the influence of interference effects on spectral quality of pancreatic tissues measured in transmission and transflection mode with Fourier tranform IR (FT-IR) microscopy using samples embedded with and without paraffin. The removal of paraffin leads to an altered magnitude of interference in transflection and provides a platform for a detailed analysis of its effect on the spectra of biological material, since the same sample is measured with different interference conditions. Moreover, the potential of transflection mode measurements in histological classification of analyzed samples was investigated and compared with classification results for transmission mode.

Graphical abstract: Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry

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Article information


Submitted
05 Aug 2020
Accepted
07 Nov 2020
First published
09 Nov 2020

Analyst, 2021,146, 646-654
Article type
Paper

Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry

D. Liberda, P. Koziol, M. K. Raczkowska, W. M. Kwiatek and T. P. Wrobel, Analyst, 2021, 146, 646
DOI: 10.1039/D0AN01565B

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