Visible-Near Infrared Spectroscopy and Chemometrics for the Prediction of Trace Element Fe and Zn Levels in Rice LeafReport as inadecuate




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College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China





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Abstract Two sensitive wavelength SW selection methods combined with visible-near infrared Vis-NIR spectroscopy were investigated to determine the levels of some trace elements Fe, Zn in rice leaf. A total of 90 samples were prepared for the calibration n = 70 and validation n = 20 sets. Calibration models using SWs selected by LVA and ICA were developed and nonlinear regression of a least squares-support vector machine LS-SVM was built. In the nonlinear models, six SWs selected by ICA can provide the optimal ICA-LS-SVM model when compared with LV-LS-SVM. The coefficients of determination R2, root mean square error of prediction RMSEP and bias by ICA-LS-SVM were 0.6189, 20.6510 ppm and −12.1549 ppm, respectively, for Fe, and 0.6731, 5.5919 ppm and 1.5232 ppm, respectively, for Zn. The overall results indicated that ICA was a powerful way for the selection of SWs, and Vis-NIR spectroscopy combined with ICA-LS-SVM was very efficient in terms of accurate determination of trace elements in rice leaf. View Full-Text

Keywords: Vis-NIR spectroscopy; rice; traces elements; independent component analysis ICA; least squares-support vector machine LS-SVM Vis-NIR spectroscopy; rice; traces elements; independent component analysis ICA; least squares-support vector machine LS-SVM





Author: Yongni Shao and Yong He *

Source: http://mdpi.com/



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