中文摘要：

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。

英文摘要：

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 （Fas- tICA） 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 was used to revise the weights of neural networks to quicken the rate of convergence and overcome the problem of failing easily into local minimums, and finally the ICA-GA-BP model was built. The models were used to estimate the content of TOC and CEC in soil samples both in calibration set and predicted set. Correlation coefficient （R2 ） of prediction and root mean square error of prediction （RMSEP） were used as the evaluation indexes. The results indicate that the R for the prediction of TOc content and CEC can both reach 0. 98. These indicated that the results of analysis were satisfiable based on ICA method, and offer a new approach to the fast prediction of components~ contents in soil.