摘要
快速测定土壤有机质含量对作物生产和土壤肥力评价具有重要意义,红外光声光谱技术的应用为土壤有机质快速测定提供了可能。本研究以江苏省南京市溧水区水稻土土样为材料,探究了红外光声光谱技术在有机质测定中的应用。采用主成分分析、偏最小二乘和独立成分分析,分别提取了土壤光谱的主成分、偏最小二乘潜变量和独立成分,并以提取的信息输入支持向量机,从而构建了三种支持向量机校正模型。同时,偏最小二乘也被用于建立校正模型,作为支持向量机模型的对照。预测结果表明,基于独立成分的支持向量机模型效果最好,预测相关系数R2、均方根误差RMSEP和实际测量值的标准差与光谱模型预测值标准差的比值即RPD值分别为0.808、0.575和2.28。F检验表明,该模型显著优于基于主成分的支持向量机模型,但与基于偏最小二乘潜变量的支持向量机模型,以及经典偏最小二乘模型没有显著差异。t检验表明,各校正模型对有机质的预测结果与化学测定结果没有显著差异。因此,红外光声光谱技术为土壤有机质的快速测定提供了新的技术手段。
Fast qualification of soil organic matter （SOM） is important to crop production and evaluation of soil quality. Fourier transform infrared photoacoustic spectroscopy （FTIR-PAS） makes it feasible to quantify soil organic matter content in a rapid way. In this study, FTIR-PAS was applied to measure SOM in the soils collected from paddy fields in Lishui District of Jiangsu Province. Support vector machine （SVM） was utilized to build calibration models. Principal component analysis （PCA）, partial least squares （PLS） and independent component analysis （ICA） were performed separately to extract principal components （PCs）, latent variables of PLS （LVPLS） and independent components （ICs） from the soil spectra as input of support vector machine （SVM）. Hence, three SVM calibration models were built up. Meanwhile, PLS was also used to form a calibration model as control. Results show that the ICs-based SVM model performed best in prediction of SOM, with correlation coefficient （R2）, root-mean-square error （RMSEP） and ratio of performance to standard deviation （RPD） being 0.808, 0.575 and 2.28 respectively. Furthermore, F-test demonstrates that this model was significantly superior over the PCs-based SVM model, but was quite similar to the LVPLS-based SVM model and the classic PLS-based SVM model. Besides, no significant difference was observed between the predictions using the calibration models and the determination using the chemical method, as was demonstrated by t-test. It can, therefore, be concluded that the technology of infrared photoacoustic spectroscopy can be used as a new means for rapid determination of soil organic matter content.
出处
《土壤学报》
CAS
CSCD
北大核心
2014年第6期1262-1269,共8页
Acta Pedologica Sinica
基金
国家自然科学基金项目(41130749)资助
关键词
土壤有机质
红外光声光谱
支持向量机
定量预测
Soil organic matter
Infrared photoacoustic spectroscopy
Support vector machines
Quantitative prediction
作者简介
曾胤（1988-），男，湖北洪湖人，硕士研究生。E—mail：yzeng@issas．ac．cn
通讯作者，E—mail：chwdu@issas.ac．cn
