关键词: 特征波长, 近红外光谱, 土壤有机质

Abstract: Because of the multicollinearity of near-infrared spectroscopy data, the selection of characteristic wavelength has been an important research for near-infrared spectroscopy analysis technology. Based on spectral data and the content of soil organic matter (SOM) of 108 soil samples, this paper used  three typical characteristics wavelength selection algorithm, the successive projections algorithm (SPA), interval partial least squares (IPLS), competitive adaptive reweighted sampling (CARS), for wavelength selection of near-infrared spectroscopy and modeling of soil organic matter content. The results showed that the model based on the characteristic wavelength extracted by the three methods above had better prediction ability than that of the full-spectrum model. Among them, the accuracy of the MLR prediction model based on SPA algorithm was the best, and the correlation coefficient (Rp) and root mean square error (RMSEP) of the prediction set were 0.970 2 and 1.214 4, respectively, with only 6 model parameters. Therefore, SPA-MLR could effectively apply near-infrared spectroscopy modeling, simplify the complexity of the model, and improve the computational efficiency of the model.

Key words: characteristic wavelength, near-infrared spectroscopy, soil organic matter