Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Construction of a Ginsenoside Content-predicting Model based on Hyperspectral Imaging

  • Ning, Xiao Feng (Department of Agricultural Mechanization Engineering, Shenyang Agricultural University) ;
  • Gong, Yuan Juan (Department of Agricultural Mechanization Engineering, Shenyang Agricultural University) ;
  • Chen, Yong Liang (Department of Agricultural Mechanization Engineering, Shenyang Agricultural University) ;
  • Li, Hongbo (Department of Cultivation and Identification of Chinese Herbs, Shenyang Agricultural University)
  • Received : 2018.07.24
  • Accepted : 2018.11.26
  • Published : 2018.12.01
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Abstract

Purpose: The aim of this study was to construct a saponin content-predicting model using shortwave infrared imaging spectroscopy. Methods: The experiment used a shortwave imaging spectrometer and ENVI spectral acquisition software sampling a spectrum of 910 nm-2500 nm. The corresponding preprocessing and mathematical modeling analysis was performed by Unscrambler 9.7 software to establish a ginsenoside nondestructive spectral testing prediction model. Results: The optimal preprocessing method was determined to be a standard normal variable transformation combined with the second-order differential method. The coefficient of determination, $R^2$, of the mathematical model established by the partial least squares method was found to be 0.9999, while the root mean squared error of prediction, RMSEP, was found to be 0.0043, and root mean squared error of calibration, RMSEC, was 0.0041. The residuals of the majority of the samples used for the prediction were between ${\pm}1$. Conclusion: The experiment showed that the predicted model featured a high correlation with real values and a good prediction result, such that this technique can be appropriately applied for the nondestructive testing of ginseng quality.

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References

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