Detection and analysis of alcohol near-infrared spectrum in vitro and vivo based on wavelet transform

Si Te Luo, Zeng Yong Li, Ming Zhang, Guo Qiang Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


The near-infrared spectroscopy (NIRS) signals of alcohol in vivo are always contaminated by noise. In the present study, wavelet analysis was used to eliminate noise and thereby detecting the NIRS signals of alcohol in vivo. In soft and hard threshold function, the spectral signals were de-noised by default threshold, Birge-Massart threshold and mini&max threshold respectively. Signal noise ratio (SNR) and root mean square error (RMSE) method were used to evaluate the effects of the de-noising. The results show that the default threshold de-noising has the best effects. Therefore, the default threshold de-noising was chosen to perform de-noising analysis in vivo and in vitro. Our result shows that the wavelet transform de-noising is effective in removing noise from NRS signals of alcohol in vivo. With different alcohol concentration, the de-noising spectrua can show evident absorption peaks. Wavelet analysis is an effective method in the non-invasive alcohol testing and quantitative analysis.
Original languageChinese (Simplified)
Pages (from-to)1541-1546
Number of pages6
JournalGuang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
Issue number6
Publication statusPublished - 1 Jun 2012


  • Alcohol
  • De-noising
  • Near-infrared spectra
  • Threshold
  • Wavelet transform

ASJC Scopus subject areas

  • Instrumentation
  • Spectroscopy

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