Prediction of neonatal respiratory distress biomarker concentration by application of machine learning to mid-infrared spectra
Prediction of neonatal respiratory distress biomarker concentration by application of machine learning to mid-infrared spectra
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.
ATR–FTIR, Machine learning, Neonatal respiratory distress syndrome, Point-of-care devices, Spectroscopy
Ahmed, Waseem
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Veluthandath, Aneesh Vincent
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Rowe, David J.
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Madsen, Jens
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Clark, Howard
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Postle, Anthony D.
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Wilkinson, James S.
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Murugan, Ganapathy Senthil
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1 March 2022
Ahmed, Waseem
4326b5dd-ca37-4ea0-bd27-294f2ef011e6
Veluthandath, Aneesh Vincent
6a183413-e10f-4374-bc64-a33bf7fd9cfa
Rowe, David J.
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Madsen, Jens
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Clark, Howard
a5fd860a-269b-4e37-9075-73f94eb00471
Postle, Anthony D.
c84154fa-e569-412c-a470-19aaadb847c6
Wilkinson, James S.
73483cf3-d9f2-4688-9b09-1c84257884ca
Murugan, Ganapathy Senthil
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Ahmed, Waseem, Veluthandath, Aneesh Vincent, Rowe, David J., Madsen, Jens, Clark, Howard, Postle, Anthony D., Wilkinson, James S. and Murugan, Ganapathy Senthil
(2022)
Prediction of neonatal respiratory distress biomarker concentration by application of machine learning to mid-infrared spectra.
Sensors, 22 (5), [1744].
(doi:10.3390/s22051744).
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.
Text
sensors-22-01744
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More information
Accepted/In Press date: 21 February 2022
Published date: 1 March 2022
Additional Information:
Funding Information:
Funding: The authors would like to acknowledge the funding support received from the UK EPSRC grant EP/S03109X/1.
Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords:
ATR–FTIR, Machine learning, Neonatal respiratory distress syndrome, Point-of-care devices, Spectroscopy
Identifiers
Local EPrints ID: 455295
URI: http://eprints.soton.ac.uk/id/eprint/455295
ISSN: 1424-8220
PURE UUID: 2adeeaa3-d3f9-4fc4-8ba4-fa78bafd1254
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Date deposited: 16 Mar 2022 18:01
Last modified: 17 Mar 2024 04:00
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Contributors
Author:
Waseem Ahmed
Author:
Aneesh Vincent Veluthandath
Author:
David J. Rowe
Author:
Jens Madsen
Author:
Howard Clark
Author:
Anthony D. Postle
Author:
Ganapathy Senthil Murugan
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