Comparison of multivariate calibration models for glucose,urea, and lactate from near-infrared and Raman spectra |
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Authors: | Min Ren Mark A Arnold |
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Institution: | (1) Department of Chemistry and Optical Science and Technology Center, University of Iowa, Iowa City, IA 52242, USA |
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Abstract: | Partial least-squares (PLS) calibration models have been generated from a series of near-infrared (near-IR) and Raman spectra
acquired separately from sixty different mixed solutions of glucose, lactate, and urea in aqueous phosphate buffer. Independent
PLS models were prepared and compared for glucose, lactate, and urea. Near-IR and Raman spectral features differed substantially
for these solutes, with Raman spectra enabling greater distinction with less spectral overlap than features in the near-IR
spectra. Despite this, PLS models derived from near-IR spectra outperformed those from Raman spectra. Standard errors of prediction
were 0.24, 0.11, and 0.14 mmol L−1 for glucose, lactate, and urea, respectively, from near-IR spectra and 0.40, 0.42, and 0.36 mmol L−1 for glucose, lactate, and urea, respectively, from Raman spectra. Differences between instrumental signal-to-noise ratios
were responsible for the better performance of the near-IR models. The chemical basis of model selectivity was examined for
each model by using a pure component selectivity analysis combined with analysis of the net analyte signal for each solute.
This selectivity analysis showed that models based on either near-IR or Raman spectra had excellent selectivity for the targeted
analyte. The net analyte signal analysis also revealed that analytical sensitivity was higher for the models generated from
near-IR spectra. This is consistent with the lower standard errors of prediction. |
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Keywords: | Near-infrared spectroscopy Raman spectroscopy Noninvasive glucose sensing Multivariate calibration |
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