可见/近红外光谱快速测定土壤中的有机碳含量和阳离子交换量

为实现土壤中有机碳(TOC)含量和阳离子交换量(CEC)的快速检测, 对300个土壤样品的可见/近红外光谱数据进行了分析。使用快速独立分量分析(FastICA)算法对光谱数据矩阵进行分解, 得到独立成分和相应的混合系数矩阵, 再利用误差反向传播算法(back-propagation, BP)构造三层神经网络结构。为了克服传统BP神经网络结构难以确定和易于陷入局部极小点的缺点, 采用遗传算法优化BP神经网络结构和初始权值, 得到ICA-GA-BP模型。利用此模型对土壤中TOC含量和CEC进行预测, 根据预测相关系数(R2)和预测标准偏差(RMSEP)来评价预测模型的性能, 表明该模型对TOC含量和CEC测定的相关系数R2均达到0.98以上。说明文章提出的ICA-GA-BP建模方法具有很好的预测效果, 为土壤品质的鉴别提供了一种新方法。

Rapid Prediction of Total Organic Carbon Content and CEC in Soil Using Visible/Near Infrared Spectroscopy

For the rapid detection of the total organic carbon(TOC) content and cation exchange capacity(CEC) in soil,visible/near infrared spectra(Vis/NIR) of 300 soil samples were analyzed.The algorithm of fast independent component analysis(FastICA) was used to decompose the data of Vis/NIR spectrum,and their independent components and the mixing matrix were obtained.Then,the calibration model with three-level artificial neural networks structure was built by using Back-Propagation(BP) algorithm.Genetic algorithm w...