Details

Autor(en) / Beteiligte

• Wang, L. S.
• Wang, R. J.
• Lu, C. P.
• Wang, J.
• Huang, W.
• Jian, Q.
• Wang, Y. B.
• Lin, L. Z.
• Song, L. T.

Titel

Quantitative Analysis of Total Nitrogen Content in Monoammonium Phosphate Fertilizer Using Visible-Near Infrared Spectroscopy and Least Squares Support Vector Machine

Ist Teil von

• Journal of applied spectroscopy, 2019-07, Vol.86 (3), p.465-469

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• А quantitative analysis method to determine the total nitrogen content in monoammonium phosphate (MAP) fertilizer using visible-near infrared (Vis-NIR) spectroscopy and least squares support vector machine (LS-SVM) is proposed. Sample set partitioning based on the joint x–y distance (SPXY) was used to select the calibration set. Fourteen spectral pre-processing methods were then employed to deal with the spectral data including Savitzky–Golay (SG) smoothing, fi rst derivative (D 1 ) and second derivative (D 2 ) with SG smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), wavelet, and combination thereof. Next, the LS-SVM model with radial basis function kernel was established with the best pre-processing method, and its performance was compared with that of partial least squares (PLS) model. The results revealed LS-SVM calibration with the discrete wavelet transform provided the best prediction for total nitrogen content in MAP fertilizer, yielding R 2 , root mean square error of prediction (RMSEP), and ratio of performance to deviation (RPD) values of 0.91, 0.101, and 3.34, respectively.