光谱数据变换和光谱特征波长提取是二种重要的光谱预处理方法, 对消除环境等干扰具有重要的作用。 以往文献主要对比研究不同的光谱数据变换方法, 光谱特征波长提取方法的对比研究以及二者的组合研究较少。 为了获取适宜的光谱预处理方法, 提高潮间带沉积物氮的最小二乘支持向量机（LSSVM）模型精度, 研究了4种光谱变换方法与3种特征波长提取方法组合对沉积物氮LSSVM模型精度的影响, 以期实现潮间带沉积物氮的精确预测。 研究结果表明, 多元散射校正（MSC）或标准正态变换（SVN）光谱变换方法提高了光谱与氮含量的相关性, 最高相关系数分别达到0.69和0.71; 并且提高了LSSVM模型的预测精度, 模型的预测R2和RPD分别为0.88, 0.87和2.78, 2.69。 无信息变量消除（UVE）特征波长提取方法也提高了LSSVM模型的预测精度, 模型预测R2和RPD分别0.89和2.70。 但是, UVE提取的特征波长并不都与氮含量具有高相关性。 此外, 组合运用UVE特征波长提取方法和MSC或SVN光谱变换方法, 也提高了模型预测精度, 但并不优于单独运用UVE特征波长提取方法或单独运用MSC及SVN光谱变换方法。 研究结果可为潮间带沉积物氮估算和光谱数据预处理提供技术参考。

Spectral data transformation and feature wavelength extraction are two important spectral pretreatment methods, which play an important role in eliminating environmental interference. Previous literature mainly compared different spectral data transformation methods and there was less studyon the spectral feature wavelength extraction methods and the combination of the two methods. In order to obtain suitable spectral pretreatment method and improve the accuracy of LSSVM model of sediment nitrogen in the intertidal zone, the effect of 4 spectral transformation methods combined with 3 characteristic wavelength extraction methods on the accuracy of LSSVM model of sediment nitrogen is studied for accurate prediction of sediment nitrogen in the intertidal zone. The results showed that the spectral transformation methods of multivariate scattering correction (MSC) or normal distribution (SVN) increasedthe correlation between spectra and nitrogen content and the highest correlation reached 0.69 and 0.71 respectively. MSC and SVN improved the prediction accuracy of LSSVM model, and the prediction R2 and RPD are 0.88, 0.87 and 2.78, 2.69, respectively. The feature wavelength extraction method of uninformative variable elimination (UVE) also improved the prediction accuracy of LSSVM model, model test R2 and RPD were 0.89 and 2.70, respectively. However, not all of the characteristic wavelengths extracted by UVE have a high correlation with nitrogen content. In addition, the combination of UVE and MSC or SVN also improved the prediction accuracy of the model, but it is not better than UVE alone or MSC or SVN alone. The results of this paper can provide a technical reference for nitrogen estimation and spectral data preprocessing of intertidal sediments.