讨论了如何利用连续投影算法提取土壤总氮的近红外特征波长。 使用连续投影算法对光谱数据进行初步压缩, 将优选出的波长按其对总氮贡献值的大小进一步筛选, 剔除不敏感的波长, 降低模型的复杂度。 分析85份土壤样品的近红外光谱, 使用连续投影算法得到了总氮的12个波长, 贡献值筛选后, 波长数量减少到6个, 所建模型的预测相关系数(Rp)为0.913, 预测均方根误差(RMSEP)为0.011%, 模型的预测精度与贡献值筛选前相当, 且优于全谱偏最小二乘回归结果。 结果表明结合贡献值筛选的连续投影算法能够有效选取待测成分的特征波长, 文章所优选的土壤总氮的6个特征波长可以作为小型滤光片式近红外光谱仪波长选择的参考依据。

The present paper proposed how to select characteristic near-infrared wavelength for soil total nitrogen by using successive projection algorithm (SPA). Spectral data are compressed by SPA in the first place to obtain the raw wavelengths. Then the group of wavelengths derived from SPA is screened by their contributions to the total nitrogen. The insensitive wavelengths for total nitrogen are eliminated, improving the parsimony of the calibration model. For the 85 soil samples in total nitrogen, SPA was used to select the raw wavelengths. After screening on contribution, the number of wavelengths dropped from 12 by direct SPA to 6. Finally, the calibration model using wavelengths selected by screening on contribution after SPA showed the correlation coefficient (Rp) of 0.913 and the root mean square error of prediction (RMSEP) of 0.011%. This model is as precise as the one before screening on contribution, and more precise than the result derived from partial least square (PLS) for the whole spectrum. The results demonstrate that the number of wavelengths selected by SPA can be reduced without significantly compromising prediction performance using the screening on contribution. The 6 selected total nitrogen wavelengths in this paper can be a reference for designing smart filter NIR spectrometer.