基于近红外光谱和正交信号-偏最小二乘法对土壤的分类

不同质地的土壤,由于蓄水能力和土壤颗粒大小的不同使得其光谱特性不同,这为采用近红外光谱技术对土壤质地进行判别分析提供了依据。该研究利用正交信号校正(OSC)方法可以获得与浓度有关的谱图信息这一优势,将其与偏最小二乘方法(PLS)结合,采用近红外光谱技术对不同质地的土壤:砂土、壤土、黏土进行判别分析。结果表明:建模样本的相关系数可达0.965,采用该模型对其余45个样本分别进行了预测,三种土壤预测样本的判别正确率分别为:93.3%,86.6%和86.6%。说明OSC方法可以提取谱图中的微弱的质地信息,实现土壤质地的快速鉴别分析。

Soil classification based on near infrared reflectance spectroscopy and orthogonal signal correction-partial least square

Soils with different soil textures have different spectra because of their different moisture-holding capacity and particle size. This feature provides a scientific basis for soil textural classification by Near-infrared Spectroscopy (NIR) technology. In the paper, the spectra of three different soil textural samples (sand, loam and clay soils) were analyzed. Since texture information in spectra is less than chemical information, the orthogonal signal correction (OSC) was applied in this research because it can keep down concentration information by orthogonal processing, and the partial least square (PLS) classification model was obtained. The results showed that the correlation coefficient of validation model was 0.946, and the correct recognition ratio for the three kinds of predicted samples were 93%, 86.6% and 86.6% respectively. The research indicates that OSC can extract texture information from weak spectra, so as to realize soil texture classification.