Optimised spectral pre-processing for discrimination of biofluids via ATR-FTIR spectroscopy

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2 Citations (Scopus)

Abstract

Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients (3,897 spectra), the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥ 6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.
LanguageEnglish
JournalAnalyst
Early online date19 Nov 2018
DOIs
Publication statusE-pub ahead of print - 19 Nov 2018

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Fourier Transform Infrared Spectroscopy
FTIR spectroscopy
Spectrum Analysis
Spectroscopy
Processing
Brain Neoplasms
Noise
smoothing
Sensitivity and Specificity
wavelet
brain
cancer
spectroscopy
filter
Noise abatement
Infrared spectroscopy
Brain
analysis
Derivatives
Datasets

Keywords

  • Mid-IR spectroscopy
  • pre-processing protocol
  • biofluid ATR-FTIR spectroscopy

Cite this

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title = "Optimised spectral pre-processing for discrimination of biofluids via ATR-FTIR spectroscopy",
abstract = "Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients (3,897 spectra), the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥ 6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.",
keywords = "Mid-IR spectroscopy, pre-processing protocol, biofluid ATR-FTIR spectroscopy",
author = "Butler, {Holly J.} and Smith, {Benjamin R.} and Robby Fritzsch and Pretheepan Radhakrishnan and Palmer, {David S.} and Baker, {Matthew J.}",
year = "2018",
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AU - Smith, Benjamin R.

AU - Fritzsch, Robby

AU - Radhakrishnan, Pretheepan

AU - Palmer, David S.

AU - Baker, Matthew J.

PY - 2018/11/19

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N2 - Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients (3,897 spectra), the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥ 6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.

AB - Pre-processing is an essential step in the analysis of spectral data. Mid-IR spectroscopy of biological samples is often subject to instrumental and sample specific variances which may often conceal valuable biological information. Whilst pre-processing can effectively reduce this unwanted variance, the plethora of possible processing steps has resulted in a lack of consensus in the field, often meaning that analysis outputs are not comparable. As pre-processing is specific to the sample under investigation, here we present a systematic approach for defining the optimum pre-processing protocol for biofluid ATR-FTIR spectroscopy. Using a trial-and-error based approach and a clinically relevant dataset describing control and brain cancer patients (3,897 spectra), the effects of pre-processing permutations on subsequent classification algorithms were observed, by assessing key diagnostic performance parameters, including sensitivity and specificity. It was found that optimum diagnostic performance correlated with the use of minimal binning and baseline correction, with derivative functions improving diagnostic performance most significantly. If smoothing is required, a Sovitzky-Golay approach was the preferred option in this investigation. Heavy binning appeared to reduce classification most significantly, alongside wavelet noise reduction (filter length ≥ 6), resulting in the lowest diagnostic performances of all pre-processing permutations tested.

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