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<p>The moisture content of Chinese walnut is a critical quality indicator which directly influences storage properties and the oil content. This study evaluated the feasibility of hyperspectral imaging (HSI) integrated with chemometrics for moisture content assessment of Chinese walnuts in a rapid and non-destructive manner. After acquiring 200 hyperspectral images (188 were retained after outlier removal), average reflectance spectra from regions of interest (ROI) were analyzed. In comparison, partial least squares regression (PLSR) models using raw reflectance spectra (termed R-PLSR) outperformed those with preprocessed or alternative spectral units (absorbance and Kubelka-Munk), yielding R<sub>p</sub> = 0.7161, RMSEP = 0.6005, RPD = 1.35, and RER = 8.73. After that, two-dimensional correlation spectroscopy (2D-COS), regression coefficients (RC), and competitive adaptive reweighted sampling (CARS) were investigated as wavelength selection algorithms to simplify the R-PLSR model. Results showed that 9 wavelengths selected by CARS were preferred, and gave the comparable accuracy to the R-PLSR model (R<sub>p</sub> = 0.6921, RMSEP = 0.6084, RPD = 1.33, and RER = 8.62). Finally, this CARS-R-PLSR model enabled moisture content prediction and visualization of spatial distribution, and results aligned with actual conditions. Despite challenges posed by the thick shells of Chinese walnuts, results demonstrate that HSI has potential in the rough determination and visualization of moisture content.</p>

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Non-destructive assessment of moisture content of single Chinese walnut using hyperspectral imaging integrated with chemometric tools

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Hongzhe Jiang,
  • Cheng Xie,
  • Jiang Li,
  • Wanchun Liu,
  • Tao Li,
  • Hongping Zhou

摘要

The moisture content of Chinese walnut is a critical quality indicator which directly influences storage properties and the oil content. This study evaluated the feasibility of hyperspectral imaging (HSI) integrated with chemometrics for moisture content assessment of Chinese walnuts in a rapid and non-destructive manner. After acquiring 200 hyperspectral images (188 were retained after outlier removal), average reflectance spectra from regions of interest (ROI) were analyzed. In comparison, partial least squares regression (PLSR) models using raw reflectance spectra (termed R-PLSR) outperformed those with preprocessed or alternative spectral units (absorbance and Kubelka-Munk), yielding Rp = 0.7161, RMSEP = 0.6005, RPD = 1.35, and RER = 8.73. After that, two-dimensional correlation spectroscopy (2D-COS), regression coefficients (RC), and competitive adaptive reweighted sampling (CARS) were investigated as wavelength selection algorithms to simplify the R-PLSR model. Results showed that 9 wavelengths selected by CARS were preferred, and gave the comparable accuracy to the R-PLSR model (Rp = 0.6921, RMSEP = 0.6084, RPD = 1.33, and RER = 8.62). Finally, this CARS-R-PLSR model enabled moisture content prediction and visualization of spatial distribution, and results aligned with actual conditions. Despite challenges posed by the thick shells of Chinese walnuts, results demonstrate that HSI has potential in the rough determination and visualization of moisture content.