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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
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
1424-8220
Ahmed, Waseem
4326b5dd-ca37-4ea0-bd27-294f2ef011e6
Veluthandath, Aneesh Vincent
6a183413-e10f-4374-bc64-a33bf7fd9cfa
Rowe, David J.
a0e0fe82-5e29-42b8-b370-5236a722f015
Madsen, Jens
21cf021a-4bd4-4cdf-9db7-52ff2459663d
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
a867686e-0535-46cc-ad85-c2342086b25b
Ahmed, Waseem
4326b5dd-ca37-4ea0-bd27-294f2ef011e6
Veluthandath, Aneesh Vincent
6a183413-e10f-4374-bc64-a33bf7fd9cfa
Rowe, David J.
a0e0fe82-5e29-42b8-b370-5236a722f015
Madsen, Jens
21cf021a-4bd4-4cdf-9db7-52ff2459663d
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
a867686e-0535-46cc-ad85-c2342086b25b

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).

Record type: Article

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.

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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
ORCID for Aneesh Vincent Veluthandath: ORCID iD orcid.org/0000-0003-4306-6723
ORCID for David J. Rowe: ORCID iD orcid.org/0000-0002-1167-150X
ORCID for James S. Wilkinson: ORCID iD orcid.org/0000-0003-4712-1697
ORCID for Ganapathy Senthil Murugan: ORCID iD orcid.org/0000-0002-2733-3273

Catalogue record

Date deposited: 16 Mar 2022 18:01
Last modified: 21 Jan 2023 02:57

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Contributors

Author: Waseem Ahmed
Author: David J. Rowe ORCID iD
Author: Jens Madsen
Author: Howard Clark
Author: Anthony D. Postle

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