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浙江大学学报(工学版)
J4  2011, Vol. 45 Issue (2): 364-369    DOI: 10.3785/j.issn.1008-973X.2011.02.027
    
Investigation of the measurement of total N, available P and
K in organic substrate via VNIR spectroscopy
SHEN Ming-wei1, HAO Fei-lin2, HE Yong1
1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China；
2.College of Biology and Environment, Zhejiang SuRen University,Hangzhou 310029, China
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Abstract  

To explore the feasibility of rapid detecting total nitrogen (TN), available phosphorus (AP) and available kalium (AK) of greenhouse substrate via visible/near-infrared (VNIR)spectroscopy, three substrate status of natural loose, pressed (200 Pa) and dry powder were analyzed. Mixture uniform design was adopted in substrate component design, 10 pretreatments and regression methods of partial least square (PLS), Radical base function of artificial neural network (ANNRBF) and support vector regression (SVR) were used in data analysis. Results shows that the PLS regression achieves R2≥0.98 for TN in the dry powder status, ANN-RBF shows slightly better regression result for TN and AK in dry powder status than SVR. SVR is superior to ANN-RBF in both pressed and natural loose status, of which the R2 is over 0.98. On the whole, the R2 is comparatively high in dry power status for PLS and ANN-RBF, while the SVR shows no obvious difference in three measuring status and could be used for measuring TN and AK of the substrate via appropriate pretreatments.

Published: 17 March 2011
CLC:  TH 744.1  
  S 153  
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Cite this article:

SHEN Ming-wei, HAO Fei-lin, HE Yong. Investigation of the measurement of total N, available P and
K in organic substrate via VNIR spectroscopy. J4, 2011, 45(2): 364-369.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2011.02.027     OR     http://www.zjujournals.com/eng/Y2011/V45/I2/364


栽培基质中氮、磷、钾含量的光谱检测探索

为探索栽培基质中总氮(TN)、有效磷(AP)和钾(AK)的快速测定方法,应用可见/近红外光谱对在自然松散、压实和干粉末状态下的基质进行了分析.试验采用混料均匀设计,试验结果采用10种预处理方法和偏最小二乘法(PLS)、径向基神经网络(ANNRBF)和支持向量回归机(SVR)回归方法进行了分析.结果表明,在干粉末状态下,PLS仅对TN预测R2≥0.98,ANN-RBF对TN和AK的预测效果略好于SVR,SVR在压实和自然松散状态下对TN和AK的预测R2都在0.98以上,高于ANN-RBF.PLS和ANN-RBF回归对干粉末状态下的预测结果比较好,SVR对基质测量状态的变化不敏感,经过适宜预处理能用于基质中TN和AK的测量.

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