| 光谱学与光谱分析 |
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| Study on the Determination of Total Nitrogen(TN) in Different Types of Soil by Near-Infrared Spectroscopy (NIS) |
| ZHANG Xue-lian1, LI Xiao-na1*, WU Ju-ying1, ZHENG Wei1, 2, HUANG Qian1, TANG Cong-feng1 |
1. Beijing Research & Development Center for Grass and Environment, Beijing 100097, China
2. College of Resource and Environment, Agricultural University of Hebei, Baoding 071001, China |
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Abstract In the present paper, the feasibility of determination of total nitrogen (TN) content in different types of soil based on NIS technology was studied. Some surface and subsurface soil samples were collected from Jiangsu, Henan, Shanxi, Hebei and Jilin province in China, on behalf of paddy, calcareous, loess, coastal alluvial and black soil types separately. After being air-dried, milled and screened, the scanned spectra were obtained by near-infrared spectrometer using the sample with diameter below 1 millimeter, meantime the content of TN was measured with the traditional Kelvin method. A model between TN content and spectrum was established and also it was modified through some spectrum pretreatment using the OPUS software. Then a best model was chosen with root mean squares of crosscheck (RMSECV) being the lowest. Finally the stability and application of the model was discussed through quantitative analysis. The results showed that a good model can be separately established in each province using the surface soil from the same area on behalf of the same soil type, whose RMSECV were within 0.01% on average, and the correlation efficient was above 0.85 basically. The model established using the surface soil can be well used for analysis of TN content in other surface and subsurface soil from the same area whose TN was within the range of the model. The root mean squares of prediction (RMSEP) and correlation efficient of the quantitative analysis were at about 0.01% and 0.80 separately. However, maybe affected by soil types, the model established in one area had certain limitations in the application to other areas even as its TN content was within the range of the model. Whenas, choosing some data from each area randomly to form a new sample group can establish a good new integrated model with RMSECV and correlation efficient being 0.010 2% and 0.985 6 respectively and that can be well used to predict TN content in soil from each area.
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Received: 2009-06-23
Accepted: 2009-09-26
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Corresponding Authors:
LI Xiao-na
E-mail: lxn1977@126.com
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