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Sensors 2013, 13(2), 1872-1883; doi:10.3390/s130201872
Article

Visible/Near Infrared Spectroscopy and Chemometrics for the Prediction of Trace Element (Fe and Zn) Levels in Rice Leaf

 and
*
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
* Author to whom correspondence should be addressed.
Received: 8 January 2013 / Revised: 24 January 2013 / Accepted: 28 January 2013 / Published: 1 February 2013
(This article belongs to the Section Physical Sensors)
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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.
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)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Shao, Y.; He, Y. Visible/Near Infrared Spectroscopy and Chemometrics for the Prediction of Trace Element (Fe and Zn) Levels in Rice Leaf. Sensors 2013, 13, 1872-1883.

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