Extended multiplicative signal correction for FTIR spectral quality test and pre‐processing of infrared imaging data

Tafintseva, Valeria; Shapaval, Volha; Smirnova, Margarita; Köhler, Achim

doi:10.1002/jbio.201960112

Cited by 23 publications

(24 citation statements)

References 48 publications

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“…When the protein region was used for the analysis, one spectral replicate for the isolate Leifsonia rubra G.S.16 cultivated on BHI agar appeared to be significantly different from other replicates and could be considered an outlier. This can be due to the errors happening during the preprocessing (Kohler et al, 2005; Tafintseva et al, 2020). After this outlier was removed from the dataset, the PCA scatterplot of the protein region did not show any significant change in the sample distribution; therefore, we showed a scatter plot of the protein region without the outlier being removed (Figure 4c and Figure A5C).…”

Section: Resultsmentioning

confidence: 99%

Isolation and characterization of fast‐growing green snow bacteria from coastal East Antarctica

et al. 2020

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Snow microorganisms play a significant role in climate change and affecting the snow melting rate in the Arctic and Antarctic regions. While research on algae inhabiting green and red snow has been performed extensively, bacteria dwelling in this biotope have been studied to a much lesser extent. In this study, we performed 16S rRNA gene amplicon sequencing of two green snow samples collected from the coastal area of the eastern part of Antarctica and conducted genotypic and phenotypic profiling of 45 fast‐growing bacteria isolated from these samples. 16S rRNA gene amplicon sequencing of two green snow samples showed that bacteria inhabiting these samples are mostly represented by families Burkholderiaceae (46.31%), Flavobacteriaceae (22.98%), and Pseudomonadaceae (17.66%). Identification of 45 fast‐growing bacteria isolated from green snow was performed using 16S rRNA gene sequencing. We demonstrated that they belong to the phyla Actinobacteria and Proteobacteria, and are represented by the genera Arthrobacter, Cryobacterium, Leifsonia, Salinibacterium, Paeniglutamicibacter, Rhodococcus, Polaromonas, Pseudomonas, and Psychrobacter. Nearly all bacterial isolates exhibited various growth temperatures from 4°C to 25°C, and some isolates were characterized by a high level of enzymatic activity. Phenotyping using Fourier transform infrared (FTIR) spectroscopy revealed a possible accumulation of intracellular polymer polyhydroxyalkanoates (PHA) or lipids in some isolates. The bacteria showed different lipids/PHA and protein profiles. It was shown that lipid/PHA and protein spectral regions are the most discriminative for differentiating the isolates.

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Section: Resultsmentioning

confidence: 99%

Isolation and characterization of fast‐growing green snow bacteria from coastal East Antarctica

et al. 2020

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“…This difference should for the large majority of the pixels in the image be very small, since the chemical signals in the spectra mostly does not vary much between neighboring pixels. We calculate this difference for all pixels of the image after removing empty pixels with EMSC b‐parameter quality test [34] and use the median value within images of the samples as a measure of noise. We found the median difference to be about 0.003 for the DSAE corrected spectra and 0.013 for the ME‐EMSC corrected spectra.…”

Section: Resultsmentioning

confidence: 99%

“…The dashed red vertical line shows the absorbance of HTS reference spectra at the peak in question. Background spectra have been filtered out beforehand with the EMSC b‐parameter quality test [34]…”

Section: Resultsmentioning

confidence: 99%

“…Using the quality test, the background spectra were discarded, and only spectra corresponding to the sample area were used to avoid building models on background spectra. The background filter was established by considering the scaling parameter from a basic EMSC on the raw spectra [34]. Spectra which converged fast in the ME‐EMSC correction, after four or less iterations, were also discarded.…”

Section: Methodsmentioning

confidence: 99%

“…The dashed red vertical line shows the absorbance of HTS reference spectra at the peak in question. Background spectra have been filtered out beforehand with the EMSC b-parameter quality test [34] The ME-EMSC is based on the Mie formalism and involves transformations such as the Kramers-Kronig transformation and singular value decomposition. The DSAE learns to perform Mie correction from data that is corrected by the ME-EMSC algorithm for a given application and is not expected to work for any type of input spectra.…”

Section: | Conclusionmentioning

confidence: 99%

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Deep convolutional neural network recovers pure absorbance spectra from highly scatter‐distorted spectra of cells

Magnussen

Solheim

Blazhko

et al. 2020

Journal of Biophotonics

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Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals. The state-of-the-art Mie extinction extended multiplicative signal correction (ME-EMSC) algorithm is a powerful tool for the recovery of pure absorbance spectra from highly scatter-distorted spectra. However, the algorithm is computationally expensive and the correction of large infrared imaging datasets requires weeks of computations. In this paper, we present a deep convolutional descattering autoencoder (DSAE) which was trained on a set of ME-EMSC corrected infrared spectra and which can massively reduce the computation time for scatter correction. Since the raw spectra showed large variability in chemical features, different reference spectra matching the chemical signals of the spectra were used to initialize the ME-EMSC algorithm, which is beneficial for the quality of the correction and the speed of the algorithm. One DSAE was trained on the spectra, which were corrected with different reference spectra and validated on independent test data. The DSAE outperformed the ME-EMSC correction in terms of speed, robustness, and noise levels. We confirm that the same chemical information is contained in the DSAE corrected spectra as in the spectra corrected with ME-EMSC.

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Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images

Trukhan

Tafintseva

Tøndel

et al. 2020

Journal of Biophotonics

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Fourier‐transform infrared (FTIR) microspectroscopy is rounding the corner to become a label‐free routine method for cancer diagnosis. In order to build infrared‐spectral based classifiers, infrared images need to be registered with Hematoxylin and Eosin (H&E) stained histological images. While FTIR images have a deep spectral domain with thousands of channels carrying chemical and scatter information, the H&E images have only three color channels for each pixel and carry mainly morphological information. Therefore, image representations of infrared images are needed that match the morphological information in H&E images. In this paper, we propose a novel approach for representation of FTIR images based on extended multiplicative signal correction highlighting morphological features that showed to correlate well with morphological information in H&E images. Based on the obtained representations, we developed a strategy for global‐to‐local image registration for FTIR images and H&E stained histological images of parallel tissue sections.

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Extended multiplicative signal correction for FTIR spectral quality test and pre‐processing of infrared imaging data

Cited by 23 publications

References 48 publications

Isolation and characterization of fast‐growing green snow bacteria from coastal East Antarctica

Isolation and characterization of fast‐growing green snow bacteria from coastal East Antarctica

Deep convolutional neural network recovers pure absorbance spectra from highly scatter‐distorted spectra of cells

Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images

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