Publications by authors named "Yuqian Shang"

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength.

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Article Synopsis
  • Acid value (AV) is a key measure of edible oil quality, indicating deterioration, and this study focused on predicting the AV of camellia seed oil using hyperspectral imaging data from 168 samples.
  • The optimal prediction model, combining spectral and image features (2Der-SPA-GLCM-PLSR), showed strong correlation coefficients (Rc2 at 0.9698 and Rp2 at 0.9581), outperforming other models in accuracy.
  • The research highlights that hyperspectral imaging is an effective and environmentally friendly method for predicting AV in camellia seed oil, and could be applied to other edible oils, supporting sustainable development.
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