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Centner, V., Verdu-Andres, J., Walczak, B., Jouan-Rimbaud, D., Despagne, F., Pasti, L., Massart, D.-L. and De Noord, O.E. (2000) Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets. Applied Spectroscopy, 54, 608-623.
http://dx.doi.org/10.1366/0003702001949816

has been cited by the following article:

• TITLE: Improve the Prediction Accuracy of Apple Tree Canopy Nitrogen Content through Multiple Scattering Correction Using Spectroscopy

  AUTHORS: Lulu Gao, Xicun Zhu, Cheng Li, Lizhen Cheng, Ling Wang, Gengxing Zhao, Yuanmao Jiang

  KEYWORDS: Multiple Scattering Correction, Hyperspectrum, Apple Tree Canopy, Nitrogen Content, Support Vector Machine

  JOURNAL NAME: Agricultural Sciences, Vol.7 No.10, October 21, 2016

  ABSTRACT: Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R2 of the model was 0.746; the validation R2 was 0.720; and its RMSE was 0.452 g·kgˉ1. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.