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THz spectrum processing method based on optimal wavelet selection.

Hongyi Ge, Zhenyu Sun, Xuejing Lu

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    This study optimized terahertz (THz) spectrum denoising using wavelet transforms for wheat analysis. Symlets 8 wavelet with 4-level decomposition proved most effective for improving spectral quality and prediction accuracy.

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    Area of Science:

    • Spectroscopy
    • Signal Processing

    Background:

    • Terahertz (THz) spectra are susceptible to system noise and water vapor absorption.
    • High-quality spectra are crucial for accurate qualitative and quantitative analysis models.

    Purpose of the Study:

    • To denoise terahertz spectra of wheat samples using wavelet transform.
    • To identify optimal wavelet basis functions and decomposition levels for THz spectral preprocessing.

    Main Methods:

    • Wavelet transform was applied to denoise terahertz spectra of wheat samples.
    • Compound evaluation indicators (T) were used to assess denoising quality.
    • Different wavelet families (Coiflets, Symlets, Fejer-Korovkin, Daubechies) and decomposition levels were compared.

    Main Results:

    • Coiflets and Symlets wavelets were found more suitable for THz spectrum denoising than Fejer-Korovkin and Daubechies.
    • Symlets 8 wavelet with 4-level decomposition demonstrated optimal denoising performance.
    • The proposed method effectively selects optimal wavelet parameters for THz spectrum analysis.

    Conclusions:

    • Wavelet transform is an effective preprocessing technique for terahertz spectral analysis.
    • Optimized wavelet denoising enhances spectral quality and prediction accuracy in qualitative and quantitative models.
    • The study provides a systematic approach for selecting optimal wavelet parameters in THz spectroscopy.