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Related Experiment Video

Updated: Jan 24, 2026

Using Multiple Light Scattering to Examine the Stability of Phyllanthus emblica L. Extracts Obtained with Different Extraction Methods
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A Dynamic Spectrum extraction method for extracting blood scattering information - Dual-position extraction method.

Yuyu Wang1, Gang Li1, Wei Tang1

  • 1State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin 300072, China; Tianjin Key Laboratory of Biomedical Detecting Techniques & Instruments, Tianjin University, Tianjin 300072, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|June 4, 2019
PubMed
Summary

A new dual-position extraction method improves non-invasive blood analysis by enhancing Dynamic Spectrum (DS) extraction from spectrum photoplethysmography (SPPG) signals. Combining this with single-trial extraction boosts accuracy in predicting blood components like hemoglobin.

Keywords:
Blood scatteringDual-position extraction methodDynamic SpectrumNon-linearNoninvasive blood component analysis

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Area of Science:

  • Biomedical Optics
  • Medical Instrumentation
  • Spectroscopy

Background:

  • Dynamic Spectrum (DS) shows promise for clinical applications by minimizing individual differences and measurement variations.
  • Non-linear scattering effects in blood can reduce the accuracy of non-invasive blood component analysis using DS.
  • Spectrum Photoplethysmography (SPPG) offers a multi-wavelength approach for physiological signal measurement.

Purpose of the Study:

  • To develop and validate a novel dual-position extraction method for Dynamic Spectrum (DS).
  • To mitigate non-linear blood scattering effects and enhance the accuracy of non-invasive blood component analysis.
  • To improve hemoglobin concentration prediction models using enhanced DS data.

Main Methods:

  • A dual-position extraction technique was proposed, analyzing upper and lower halves of SPPG signals separately.
  • Extracted DS data underwent statistical screening to remove gross errors, followed by averaging.
  • Hemoglobin concentration prediction models were built using Partial Least Squares (PLS) and Radial Basis Function (RBF) neural networks.

Main Results:

  • The dual-position extraction method alone showed lower modeling indicators compared to single-trial and optimized difference methods.
  • Combining DS data from dual-position and single-trial extraction significantly improved modeling indicators.
  • Non-linear modeling with RBF demonstrated that dual-position extraction better suppresses blood scattering effects, enhancing prediction accuracy.

Conclusions:

  • The dual-position extraction method, particularly when combined with single-trial extraction, enhances the accuracy of non-invasive blood component analysis.
  • Incorporating spectral information from different positions improves the capture of blood scattering characteristics.
  • This approach offers a more robust method for non-invasive physiological monitoring and diagnostics.