Inversion of nutrient components of Spartina alterniflora based on hyperspectral data

  Objective  Contents of crude protein, crude fiber and crude fat are major indexes to determine the food quality of herb. The spectral reflectances and nutritional values of Spartina alterniflora were collected in Dafeng Pere David Deer Reserve in Jiangsu Province, China (core area III), and the relationship between the spectral data and the nutrient components of the plant was studied to obtain nutritional component estimation models.

  Method  In March, May, July and October 2018, the hyperspectral reflectances of S. alterniflora were collected by an ASD Field Spec Pro FR2500 high spectrometer. Meanwhile, the crude protein, crude fiber and crude fat contents of S. alterniflora leaves were determined by field collection and lab work. The correlation analysis was obtained from comparison of the hyperspectral original reflectances, its first derivative reflectances and the measured values of crude protein, crude fiber and crude fat contents of S. alterniflora. In the range of 350−1 364 nm and 1 411−1 799 nm, the bands with the absolute correlation coefficient of each nutrient component contents of S. alterniflora higher than 0.7 were selected, and 8 vegetation indexes were constructed for modeling analysis, and the optimal model was selected.

  Result  Multiple linear estimation with spectral first-order reflectances at 673 nm and 1 740 nm was the best for estimating crude protein content (R² = 0.917, RMSEP =1.344), while linear estimation model with spectral first-order reflectances centered at 1 738 nm obtained optimal estimation result on crude fiber content (R² = 0.741, RMSEP = 1.708). The crude fat content was best estimated at 1 734 and 880 nm by multiple linear estimations with first-order reflectances (R² = 0.737, RMSEP = 0.343).

  Conclusion  Among the established estimation models, models of crude protein and crude fiber have excellent estimation results for the crude protein content and crude fiber content of S. alterniflora, the estimation model of crude fat can better predict the crude fat content of S. alterniflora. The study provides a reference and technical basis for evaluating the nutritional quality of the S. alterniflora.