| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Senthil Murugan, Ganapathy
University of Southampton
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (22/22 displayed)
- 2022Study of prediction intervals in machine learning assisted mid-infrared spectroscopy for the diagnosis of neonatal respiratory distress syndrome
- 2022Prediction of neonatal respiratory distress biomarker concentration by application of machine learning to mid-infrared spectracitations
- 2019Mid-IR thermo-optic on-chip spectrometer on a III-V semiconductor platform
- 2018Chalcogenide glass waveguides with paper-based fluidics for mid-infrared absorption spectroscopycitations
- 2017Optical quality ZnSe films and low loss waveguides on Si substrates for mid-infrared applicationscitations
- 2014High-contrast, GeTe4 waveguides for mid-infrared biomedical sensing applicationscitations
- 2012Chalcogenide microsphere fabricated from fiber tapers using contact with a high-temperature ceramic surfacecitations
- 2012High-Q bismuth silicate nonlinear glass microsphere resonatorscitations
- 2012Investigation of Erbium-doped tellurite glasses for a planar waveguide power amplifier at 1.57 microns
- 2012Er-doped Tellurite glasses for planar waveguide power amplifier with extended gain bandwidthcitations
- 2011Integrated Nd-doped borosilicate glass microsphere lasercitations
- 2011Chalcogenide microsphere fabricated from fibre taper-drawn using resistive heating
- 2011Lead silicate glass microsphere resonators with absorption-limited Qcitations
- 2010Multifarious transparent glass nanocrystal composites
- 2010Position-dependent coupling between a channel waveguide and a distorted microsphere resonatorcitations
- 2010Chalcogenide glass microsphere lasercitations
- 2010Transparent silicate glass-ceramics embedding Ni-doped nanocrystals
- 2009Chalcogenide glass microspheres and their applications
- 2009Optical nonlinearities of tellurite glasses with ultrawide Raman bands
- 2007Chalcogenide glass microspheres: their production characterization and potentialcitations
- 2006Control of coupling between waveguides and microsphere resonators
- 2005Raman spectroscopic studies of quaternary tellurite glasses containing Nb2O5 and Ta2O5
Places of action
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article
Prediction of neonatal respiratory distress biomarker concentration by application of machine learning to mid-infrared spectra
Abstract
The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FTIR) combined with machine learning as a point-of-care (POC) diagnostic platform, considering neonatal respiratory distress syndrome (nRDS), for which no POC currently exists, as an example. nRDS can be diagnosed by a ratio of less than 2.2 of two nRDS biomarkers, lecithin and sphingomyelin (L/S ratio), and in this study, ATR–FTIR spectra were recorded from L/S ratios of between 1.0 and 3.4, which were generated using purified reagents. The calibration of principal component (PCR) and partial least squares (PLSR) regression models was performed using 155 raw baselined and second derivative spectra prior to predicting the concentration of a further 104 spectra. A three-factor PLSR model of second derivative spectra best predicted L/S ratios across the full range (R2: 0.967; MSE: 0.014). The L/S ratios from 1.0 to 3.4 were predicted with a prediction interval of +0.29, −0.37 when using a second derivative spectra PLSR model and had a mean prediction interval of +0.26, −0.34 around the L/S 2.2 region. These results support the validity of combining ATR–FTIR with machine learning to develop a point-of-care device for detecting and quantifying any biomarker with an interpretable mid-infrared spectrum.