基于可见-近红外光谱特征波长选择的土壤有机质快速检测研究

选择光谱特征波长进行建模可以减少冗余波长的干扰,提高模型的预测精度。采用小波阈值消噪法对采集的104个土壤样本光谱数据进行了预处理,并通过间隔偏最小二乘法、无信息变量消除、连续投影算法和群智能算法等9种方法筛选了建模波长。结果表明,小波阈值消噪法能有效降低光谱中的噪声。利用波长选择方法筛选建模波长不仅能减少建模变量的个数,而且还能提高模型的预测精度,特别是离散粒子群优化算法利用26个波长进行建模,预测决定系数达到了0.81,预测的相对标准误差为2.31。实验结果证明,通过对光谱波长进行选择不但可以降低模型的复杂度,还能有效预测土壤有机质达的含量。

Study of Rapid Detection of Soil Organic Matter Based on Characteristic Wavelength Selection of Visible-near Infrared Spectra

Selecting the characteristic wavelength in spectra for modeling can reduce the interference by redundant wavelengths and improve modeling accuracy. The spectral data of 104 soil samples collected are preprocessed by a wavelet threshold de-noising method. The wavelengths are selected for modeling by 9 wavelength selection methods including interval partial least squares, uninformative variable elimination, successive projection algorithm and swarm intelligence algorithm. The results show that the wavelet threshold de-noising method can reduce the noise in spectra effectively. Using wavelength selection methods to select the wavelengths for modeling not only can reduce modeling variables, but also can improve the prediction accuracy of the model. Particularly, since the discrete particle swarm optimization algorithm uses 26 wavelengths for modeling, its prediction determination coefficient reaches 0.81 and its relative standard prediction error is 2.31. The selection of spectral wavelengths not only can reduce the complexity of the model, but also can effectively predict the organic matter content in soil.